ssJournal of Cheminformatics
Open AcceResearch article
Computer-assisted methods for molecular structure elucidation:
realizing a spectroscopist's dream
Mikhail Elyashberg1, Kirill Blinov1, Sergey Molodtsov2, Yegor Smurnyy1,
Antony J Williams*3 and Tatiana Churanova1
Address: 1Advanced Chemistry Development, Moscow Department, 6 Akademik Bakulev Street, Moscow 117513, Russian Federation,
2Novosibirsk Institute of Organic Chemistry, Siberian Division, Russian Academy of Sciences, 9 Akademik Lavrent'ev Av., Novosibirsk, 630090
Russian Federation and 3ChemZoo Inc., 904 Tamaras Circle, Wake Forest, NC, 27587, USA
Email: Mikhail Elyashberg - elyas@acdlabs.ru; Kirill Blinov - kirill@acdlabs.ru; Sergey Molodtsov - molodsov@nioch.nsc.ru;
Yegor Smurnyy - smurnyy@gmail.com; Antony J Williams* - antony.williams@chemspider.com; Tatiana Churanova - churan@acdlabs.ru
* Corresponding author
Abstract
Background: This article coincides with the 40 year anniversary of the first published works
devoted to the creation of algorithms for computer-aided structure elucidation (CASE). The
general principles on which CASE methods are based will be reviewed and the present state of the
art in this field will be described using, as an example, the expert system Structure Elucidator.
Results: The developers of CASE systems have been forced to overcome many obstacles
hindering the development of a software application capable of drastically reducing the time and
effort required to determine the structures of newly isolated organic compounds. Large complex
molecules of up to 100 or more skeletal atoms with topological peculiarity can be quickly identified
using the expert system Structure Elucidator based on spectral data. Logical analysis of 2D NMR
data frequently allows for the detection of the presence of COSY and HMBC correlations of
"nonstandard" length. Fuzzy structure generation provides a possibility to obtain the correct
solution even in those cases when an unknown number of nonstandard correlations of unknown
length are present in the spectra. The relative stereochemistry of big rigid molecules containing
many stereocenters can be determined using the StrucEluc system and NOESY/ROESY 2D NMR
data for this purpose.
Conclusion: The StrucEluc system continues to be developed in order to expand the general
applicability, provide improved workflows, usability of the system and increased reliability of the
results. It is expected that expert systems similar to that described in this paper will receive
increasing acceptance in the next decade and will ultimately be integrated directly to analytical
instruments for the purpose of organic analysis. Work in this direction is in progress. In spite of
the fact that many difficulties have already been overcome to deliver on the spectroscopist's dream
of "fully automated structure elucidation" there is still work to do. Nevertheless, as the efficiency
of expert systems is enhanced the solution of increasingly complex structural problems will be
achievable.
Published: 17 March 2009
Journal of Cheminformatics 2009, 1:3 doi:10.1186/1758-2946-1-3
Received: 9 January 2009
Accepted: 17 March 2009
This article is available from: http://www.jcheminf.com/content/1/1/3
© 2009 Elyashberg et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 26
(page number not for citation purposes)

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
Background
The potential of creating computer-assisted methods for
the structure elucidation of new organic compounds was
first discussed in the second half of the past century. Struc-
ture elucidation commonly combines information
extracted from several forms of spectra. The molecular for-
mula of the substance is generally derived from a mass-
spectrum and structural hypotheses are deduced from
spectral data which may usually include NMR, IR, UV, etc.
spectra. The distinctive feature of this approach is the
inference of the structure of an unknown compound that
is absent from spectral libraries, i.e. without employing
reference structures and their associated spectra. Later,
qualitative spectral analysis without reference data was
extended to quantitative spectral analysis in optical spec-
troscopy [1]. The solution of such problems can be facili-
tated by the retrieval of reference data in combination
with logical-combinatorial processing of the data. A new
area of investigation was developed that is now referred to
as Computer-Aided Structure Elucidation (CASE). CASE
was applied initially to "small molecules" as distinct from
biological macromolecules and biopolymers.
The first reports devoted to CASE systems were published
by four independent groups of researchers exactly forty
years ago [2-5]. Since the publication of these seminal
reports an extensive literature regarding computer meth-
ods of structure elucidation has been produced. From the
inception of CASE methods, attention has been directed
to the creation of artificial intelligence or "expert" systems
(ES) based on the analysis of 1D 1H and13C NMR data in
combination with MS and IR spectra. The first studies of
CASE development were described in a series of reviews
[6,7] and monographs [8-10]. In spite of the efforts of
many scientific groups no system capable of elucidating
large complex molecules was delivered during the first 20
years of intensive efforts. The primary reason for failure
was the lack of structural information that could be
retrieved from 1D NMR spectra to use as input to the
structure generator, the kernel of any expert system. The
first two decades of CASE development should, neverthe-
less, be considered as very fruitful since a general strategy
was established and the core algorithms for structure elu-
cidation and verification necessary to deliver expert sys-
tems were defined. The results obtained during this period
have been reviewed [11-13], and the general methodol-
ogy of expert systems as tools for molecular structure elu-
cidation have been reviewed by Elyashberg, et al. [14]. An
article entitled "Fully automated structure elucidation – a
spectroscopist's dream comes true" [15]) suggested an
effective method of application of extensive database (ca.
500 000 items) containing large fragments supplied with
their 13C NMR spectra. Unfortunately, this approach also
A new area of analytical chemistry – "chemometrics" was
born during the same period and CASE was considered as
an integral part of chemometrics in this time. Depending
on the field of application and the mathematical
approaches used two branches were distinguished in che-
mometrics: quantitative and qualitative. Quantitative che-
mometrics is based on continuous mathematics and the
major areas in this field include multivariate calibration,
pattern recognition, mathematical mixture resolution, etc.
The primary focus of these quantitative approaches is to
quantitative chemical analysis and spectrum processing.
Qualitative chemometrics is based on discrete mathematics
mainly on mathematical logic, graph theory and combi-
natorial analysis. These methods form the basis of the
methodologies developed for Computer-Aided Structure
Elucidation.
A new epoch in CASE development was initiated in the
nineties when the first reports dedicated to expert systems
based on 2D NMR spectra were published [16-21]. Now-
adays 2D NMR data can be routinely generated, even in
automation, and a multitude of data are available as
inputs to CASE systems – e.g. HSQC (HMQC), 1H-1H
COSY (TOCSY) and HMBC methods. The present capabil-
ities of 2D NMR expert systems to perform structure eluci-
dation and verification was recently reviewed by
Elyashberg et al [22]. It is worth noting that previously
CASE systems were associated with chemometrics but
leading chemometricians now assign the term "chemo-
metrics" to quantitative chemometrics only and the jour-
nal of Chemometrics and Laboratory Intelligent Systems does
not accept articles devoted to CASE systems.
This article intends to show how CASE systems are
approaching the goal of providing fully automated struc-
ture elucidation using CASE systems. The main principles
on which these systems are based will be discussed. Our
own work regarding the development of the expert system
Structure Elucidator [23-30] will be used to demonstrate
all stages of structure elucidation by modern computer-
aided methods. Advantages of CASE methods are demon-
strated on some examples of structure revision.
Results and Discussion
General principles of the CASE systems
A molecule as a "machine" for coding structural information
CASE expert systems are based on the same general cogni-
tive principles common to the properties of particles
belonging to the atomic and sub-atomic world. In order to
extract information regarding some property of a particle
it is necessary to stimulate the particle using electromag-
netic radiation or a stream of particles and to analyze the
response signal. In those cases where we want to extractPage 2 of 26
(page number not for citation purposes)
failed to realize the spectroscopist's dream which seems
hardly to be achieved in a visible future.
information about the structure of a molecule we excite
the system with electromagnetic radiation over a wide fre-

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
quency range using electrical and magnetic fields and
streams of electrons or ions. As a result we obtain UV-VIS,
IR, Raman, NMR and mass-spectra or even, when X-rays
are used, the 3D structure model of a molecule (see Fig.
1.). In this case the molecule under analysis acts like a spe-
cific coding machine (cipher machine) which codes struc-
tural information into each kind of spectrum using its
own code. The goal of a researcher is to crack these codes
and extract the maximum structural information achieva-
ble. Figuratively speaking the CASE problem can be for-
mulated in the following way: create a decoding machine
capable of decoding the structural information contained
in one or more spectra. It should be noted that structural
information is coded into different types of spectra at dif-
ferent levels of complexity. For instance, depending on
experimental conditions mass-spectrometry can produce
spectra containing a lot of structural information, but
extraction of the details can be very complicated. Never-
theless, MS usually can deliver the molecular mass and,
with improved resolution and accuracy in recent years it is
now fairly simple to determine the molecular formula for
the molecule under study, a key parameter necessary for
molecular structure elucidation.
An IR spectrum can provide valuable information about
the presence or absence of certain functional groups but
communicates very little about their environment in the
molecule. The richest structural information can be
extracted from NMR spectra since the environment of a
given magnetically active nucleus (1H, 13C, 15N, etc.) can
be revealed through the chemical shift value and the spin-
spin couplings with neighboring nuclei. NMR spectra are
therefore considered as a primary source of structural
information.
The development of computer-based methods of structure
elucidation requires that isomerism be taken into account
and, indeed, is the primary challenge. Fig. 2. displays the
structures of a series of small molecules of natural prod-
ucts and the number of potential structural isomers calcu-
lated by us in our work [29]. The figure shows that even
the simplest structures can have hundreds of millions if
not billions of isomers. The number of isomers associated
with the structures of medium-sized complex organic
molecules can be estimated as about 1020–1030 isomers
(on the order of Avogadro's number). Although the
number of isomers is huge those corresponding to a given
molecular formula do make up a countable and finite set.
We can conclude that the general CASE strategy utilizes
processes to eliminate "superfluous" isomers from the full
isomer set by imposing different structural constraints
produced from the molecular spectra and a priori informa-
tion (sample origin, chemical rules, etc.). A successful
result depends on the screening and rejection of N-1 struc-
tural formulae that do not comply with the experimental
data and systematic constraints applied.
It is possible to suggest the following straightforward
approach to solving the CASE problem: 1) generate all iso-
mers from a given molecular formula; 2) impose different
structural constraints one by one until the initial number
of isomers reduces down to one. This approach can actu-
ally be used but only in those cases where the unknown
molecule is small (not more than 15 skeletal atoms). For
larger molecules the number of isomers and the associ-
ated processing time are so huge that the problem cannot
be solved, a situation referred to as a "combinatorial
explosion"). To reduce the dimension of the problem it is
necessary to introduce molecular fragments which
account for a significant number of the skeletal atoms.
The main attention of researchers engaged in developing
CASE systems was focused on the creation of the optimal
methods for selecting appropriate fragments and elabora-
tion algorithms for structure generation from fragments
and free atoms.
During the first period of expert system development
(when 1D NMR, IR and MS, etc. spectra were used), it was
realized that fragment selection can be automated on the
basis of a "characteristic features" concept. This concept
can be formalized by establishing the main axioms and
hypotheses used for interpretation of molecular spectra
[6,8]. The two following statements are typical:
1. If a molecule contains fragment Ai, then the character-
istic spectral features of the fragment X1, X2,...Xj,...Xm will
be observed in the spectrum.
2. If a feature Xj is observed in the spectrum, then the mol-
ecule contains at least one of the fragments Ai(Xj), Ak(Xj),
... Al(Xj).
The molecule as a cipher machine for coding structural infor-mationFigure 1Page 3 of 26
(page number not for citation purposes)
The first statement is an "axiom" because it reflects the
experimentally established fact. The second one is a
The molecule as a cipher machine for coding struc-
tural information.

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
Page 4 of 26
(page number not for citation purposes)
Structures of some small molecules and theoretical numbers of isomers (N) corresponding to their molecular formulaeFigure 2
Structures of some small molecules and theoretical numbers of isomers (N) corresponding to their molecular
formulae.

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
hypothesis because not all conceivable fragments are
known and the Xj feature can be implicated by any
unknown fragment. These statements can be formalized
using mathematical logic and all possible combinations
of fragments that may be present in a molecule under
analysis can be found as a solution to the corresponding
logical equation [3,6,8]. The creation of a set of axioms
and hypotheses necessary for solution of a given problem
is equivalent to the creation of some particular axiomatic
theory. To obtain a valid solution to the problem (i.e. a
manageable output structural file containing the correct
structure) the set of axioms must be true, complete (in def-
inite sense) and consistent. A clear understanding of the
described nature of the problem is crucial for correct inter-
pretation of the solution obtained for a particular prob-
lem.
The aim of the expert system is obviously to extract the
maximum amount of structural information from the
available spectral data. In principle, the structural infor-
mation can be easily quantified [14]. Assume that all N
conceivable isomers corresponding to the molecular for-
mula are stable enough, and let pi, be the probability that
the ith (1 ≤ i ≤ N) isomer is the genuine structure. Before
solving the problem, all isomers are equiprobable and pi =
1/N. Then, the entropy E0characterizing the initial uncer-
tainty of the solution can be determined by Shannon's
formula [31]E0 = log2N. The entropy of the valid solution
Ev containing n structures will be Ev = log2n. Obviously, Ev
<E0. Then the quantity of information Iv obtained as a
result of the solution of the problem can be expressed as
the difference between the initial and final entropies:
Iv = E0-Ev = log2N - log2n = log2(N/n).
If n = 1 then the structure of the unknown compound is
unambiguously determined and the total quantity of
structural information I0 obtained as a result of the solu-
tion of the problem can be expressed as I0 = E0 = log2N.
Hence, the extraction of new structural information is
accompanied by a decrease in the number of isomers that
meet constraints imposed by the experimental data (spec-
tra), and the process of structure elucidation reduces to
the successive imposition of structural constraints on the
number of possible isomers up to the extraction of com-
plete structural information. If complete structural infor-
mation is taken as 100% then the fraction of complete
structural information q extracted from spectra as a result
of the solution of the problem can be estimated by the
ratio
Thus, for each particular problem, the most informative
method is that allowing for the extraction of the largest
fraction of total structural information and the elimina-
tion of the maximum number of incorrect structures.
Structural constraints
In general, structural constraints can be arbitrarily set in
two forms: affirmative (positive) and negative. Among the
affirmative constraints are the specification of the hybrid-
ization of carbon atoms, the obligatory neighborhoods of
some carbon atoms with heteroatoms, the enumeration
of fragments that can (or must) be present in the molecule
and the specification of the permissible sizes of cycles, etc.
Negative constraints form a system of prohibitions: the
prohibition of neighborhoods with certain heteroatoms,
the prohibition of the presence of particular fragments,
sizes of cycles, specific bond orders, etc. The requirement
of the best match between the calculated spectrum of the
expected structure and the experimental spectrum can be
considered as the most rigid constraint. Calculated spectra
impose constraints not only on characteristic spectral fea-
tures, but on all spectral features without exception.
NMR data are certainly the most informative spectra for
the purpose of molecular structure elucidation while 13C
NMR spectra are more informative still when compared
with 1H NMR spectra. However, their combined use yields
a synergistic effect and is especially pronounced in 2D
NMR spectra. 2D NMR spectra provide information useful
not only for the structure elucidation of the molecular
skeleton but also for the determination of the relative ster-
eochemistry of the molecule and calculation of its 3-D
model.
"Negative" structural constraints, which are introduced
because of the absence of some characteristic features in
the spectrum, are commonly more informative than pos-
itive constraints. For example, the absence of signals in the
region 150–200 ppm in the 13C NMR spectrum suggests
with a high probability that the carbonyl group is absent
in the molecule, whereas the presence of a signal in this
region can also be accounted for by the presence of other
groups (C = N, C = S, C = C-O, etc.).
A calculation of the quantity of structural information
obtained from each constraint and ranking constraints by
their contributions into solving a particular problem is, in
principle, feasible. These quantitative estimations are only
possible for small molecules however.
Main stages of the computer-aided structure elucidation
During the 1970s researchers established a general work-
flow for the CASE process (Fig. 3.). Based on the reasoningPage 5 of 26
(page number not for citation purposes)
q = (Iv/Io) × 100 = (1 - log2n/log2N) × 100. outlined above the sequence of operations that forms the
basis of expert system operation is both clear and natural.

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
Any available combination of molecular spectra may be
used as initial data. First, different positive structural con-
straints, mainly fragments, are determined from the spec-
tra and input into the program. Using this information
and the molecular formula, the system generates isomers.
Obviously, it is necessary to obtain only the isomers that
meet all of the imposed constraints and spectral features
observed in the experimental spectra of the unknown
compound. The fulfillment of structural constraints is
ensured by the generation algorithm, which exhaustively
yields all structures that meet the constraints. The compli-
ance of the structures with the observed spectral features is
verified using spectro-structural correlations in spectra of
different nature. They form a set of spectral filters. The fil-
ters involve the most typical molecular fragments (func-
tional groups) together with variation intervals of spectral
features typical for these fragments. Constraints that are a
system of prohibitions resulting from the rules of organic
chemistry, stereochemistry, and a priori knowledge about
the studied compound are added into the structural filter.
The fragments included in filters are searched for in each
structure. Structures containing fragments that are not
confirmed by the spectra are excluded from the output
structural file. The final step of structure filtration is the
check of the proximity of the calculated and experimental
spectra for each structure. The structure for which the pre-
dicted spectra are closest to the experimental spectra is
selected as the most probable.
It should be noted that this flow diagram (Fig. 3.) has
essentially remained valid until recently in spite of the fact
that the algorithms used to realize different stages of the
As mentioned above, when 1D NMR spectra were used
the execution of procedures described by the flow diagram
were successful only for modest sized molecules. Typical
examples of molecules recognized by the 1D expert sys-
tem X-PERT [32] are shown in Fig. 4.
Usage of structural information carried by 2D NMR spectra
The molecule size limitations were overcome when 2D
NMR data became available to second generation expert
systems. We will briefly consider the properties of struc-
tural information obtained from 2D NMR spectra. 2D
NMR spectra of various forms can be acquired in various
ways. In general they exhibit spin-spin couplings for each
magnetic nucleus interaction with other magnetic nuclei
in a molecule. This allows us, in principle, to elucidate the
molecular structure as though the elucidation were per-
formed ab initio without the use of spectrum-structure cor-
relations. Commonly, the following types of 2D
experiments are used [33]:
1H-13C heteronuclear correlation (for example, HSQC
(Heteronuclear Single-Quantum Coherence));
1H-1H homonuclear correlation (for example, COSY
(Homonuclear Correlation SpectroscopY));
1H-13C long-range heteronuclear correlation (for exam-
ple, HMBC (Heteronuclear Multiple-Bond Correlation)).
HSQC spectra exhibit spin-spin couplings between the
13Ci nucleus from the CiHn (group (n = 1–3) and the pro-
tons bonded to this atom, which makes it possible to
establish the relationship between the chemical shift in
the 13C spectrum of the Ci atom and the chemical shifts in
the 1H spectrum from protons bonded to this atom. COSY
spectra commonly exhibit peaks caused by the interaction
between protons separated by two or three bonds from
each other (though longer-range couplings are possible).
Flow diagram of a CASE systemigure 3
Flow diagram of a CASE system.
Examples of structures identified with the aid of the X-PERT program [32]Figu e 4Page 6 of 26
(page number not for citation purposes)
structure elucidation process were continuously varied
and improved during the last forty years.
Examples of structures identified with the aid of the
X-PERT program [32].

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
These signals correspond to spin-spin coupling constants
2JHH (a geminal interaction in a CH2 group) and 3JHH (a
vicinal interaction in the C-l(H-l)- C-2(H-2) fragment).
Useful structural information is obviously provided by
correlation peaks detected from vicinal interactions
between hydrogen atoms. If the vicinal interaction of H-l
and H-2 protons is revealed in the COSY spectrum, it is
said that a connectivity with a length of one C-C bond
occurs between the corresponding C-l and C-2 atoms. The
richest information is involved in HMBC spectra. These
spectra exhibit spin-spin couplings between protons and
13C nuclei separated by two or three bonds from each
other. The difficulty of interpreting HMBC spectra is that
conventional HMBC experiments do not distinguish 2JCH
and 3JCH coupling peaks. Therefore, a length of one to two
C-C bonds is assigned to HMBC connectivities. In general
the larger the number of COSY and HMBC connectivities
the more structural information (constraints) is extracted
from the spectra.
As well as the 2D NMR experiments mentioned above,
there is a series of other 2D NMR techniques (TOCSY,
NOESY, ROESY, etc.), which elucidate long-range proton-
proton interactions. For example, TOCSY interactions are
transmitted through a chain of neighboring protons.
Interactions through-space can be detected using NOESY
and ROESY spectra and they allow conclusions to be
drawn regarding the stereochemistry of the molecule.
In expert systems employing 2D NMR spectra it is
assumed by default that COSY connectivities have a
length equal to one C-C bond and the length of HMBC
connectivity is specified within one or two C-C bonds.
These parameters correspond to the distances between
interacting spins most commonly observed in practice.
These assumptions make up a set of "axioms" in 2D NMR
spectroscopy.
COSY and HMBC spectra can, for certain physical reasons
[33,34], also exhibit signals corresponding to the cou-
pling of spins separated by distances larger than the
default options of the program. These correlations are
called nonstandard correlations [28]. If, for example, the
COSY spectrum exhibits a significant cross peak from the
interaction of H-l and H-3 protons from the C-l(H-l)-C-
2(H-2)-C-3(H-3) fragment, the program by default takes
it as an indication that the C-l and C-3 atoms are adjacent.
Taking into account that a particular chemical shift is
assigned to each carbon atom, a contradiction appears
with the other data. As a result, the problem either has no
solution (number of structures n = 0) or the solution is
invalid. In our example, the contradiction is eliminated by
However, in practice the problem is to elucidate whether
2D NMR data involve nonstandard connectivities and, if
their presence is found, which of the "suspicious" connec-
tivities must be extended and by how many bonds. Note
that the total number of 2D NMR correlations is com-
monly from several tens to hundreds. Unfortunately, no
reliable experimental methods currently exist that allow
the unambiguous identification of nonstandard correla-
tions. Our studies demonstrated [30] that in some cases
the number of nonstandard correlations in 2D data
approaches 20 and the deviation from the "standard" con-
nectivity length can be from one to three bonds. Our expe-
rience has shown that the probability of the presence of at
least one nonstandard connectivity in a 2D NMR spec-
trum for a molecule under study was about 50% [29].
Since 2D data involve correlations of variable length even
under those conditions when the default options of the
program adequately represent the specific features of spin
couplings in the analyzed molecule, the problem of struc-
tural interpretation of the spectra reduces to deriving the
structure from fuzzy data. The situation is dramatically
complicated if at least one nonstandard correlation occurs
among the initial data: the data become contradictory. A
large uncertainty is also introduced by signal overlap in
the 13C and 1H spectra. As a result of the presence of non-
standard correlations, their quantity, and the true length
of each of them are a priori unknown, we arrive at a con-
clusion that, in the general case, initial information can be
not only fuzzy, but also contradictory and indefinite. It
should also be noted that some expected peaks may not
appear in the 2D spectra. If a deficiency of hydrogen
atoms occurs in the molecule, the problem sometimes
becomes unsolvable without the introduction of particu-
lar fragments. Then, the initial 2D information is also
incomplete.
Taking into account the properties of initial 2D NMR data,
it is unreasonable to expect that in the general case an
expert system can elucidate the molecular structure from
2D NMR spectra in a completely automated mode. How-
ever many problems, as our experience demonstrates [25-
27], can be solved in an unattended mode. Since fully-
automated solutions are uncommon it was necessary to
develop interactive methods for computer-assisted molec-
ular structure elucidation taking into account the proper-
ties of 2D NMR information. We developed these
methods based on the systematization of experience accu-
mulated as a result of successful and unsuccessful
attempts to solve a large number of complex problems.
These methods and algorithms were implemented in the
expert system Structure Elucidator (StrucEluc) [23-30],
which, evidently, is currently the most advanced systemPage 7 of 26
(page number not for citation purposes)
the extension of the (C-l)-(C-3) connectivity by one C-C
bond.
available. Using the example of this system we will con-
sider the methodology of molecular structure elucidation

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
from 2D NMR spectra with the use of computer-assisted
methods.
Expert System Structure Elucidator
Common Mode of System Operation
Knowledgebase of the system
The system was developed for the purpose of elucidating
the structure and the relative stereochemistry of a complex
organic molecule from a combination of spectral data but
specifically using 2D NMR spectra. The system knowledge
can be divided into two categories. The factual knowledge
contained within the system consists of the following
components:
 A Complete Structure Library (CSL) containing about
400,000 molecules accompanied by their 13C and 1H
NMR spectra,
 A Fragment Library (FL) containing more than 1.5 mil-
lion fragments together with their 13C NMR subspectra,
 A library containing 175,000 structures and their
assigned 13C and 1H NMR spectra intended for the predic-
tion of 13C and 1H chemical shifts from the structural for-
mula of molecules.
The axiomatic knowledge includes:
 An Atom Property Correlation Table (APCT) which is
used for setting atom hybridization and details regarding
possible neighboring heteroatoms. The library contains
atom-centered fragments with variation intervals of chem-
ical shifts of the central carbon atom in the 13C NMR spec-
tra and the intervals of chemical shifts of the protons
attached to the central atom.
 Correlation tables for spectral-based structure filtering.
The library contains the most widespread functional
groups with intervals of their characteristic features in the
NMR and IR spectra.
The reliability of this axiomatic knowledge i.e. the corre-
spondence between the spectral ranges and the structural
fragments included in the correlation tables was thor-
oughly checked. The filtering of both correlation tables
through the CSL subset containing 280,000 structures has
shown that 98% of structures passed through the verifica-
tion procedures [29].
The molecular formula or molecular mass of the analyzed
compound, HSQC, HMBC, and COSY spectra, as well as
1D 13C and 1H NMR spectra are used as initial data. If the
13C NMR spectrum cannot be recorded because of concen-
establish the relative stereochemistry of the molecule
either NOESY or ROESY spectral data are used.
In those cases when only 1D NMR spectra are available,
the program performs a search in the Fragment Library
based on the 13C NMR spectrum and, as a result, frag-
ments whose subspectra are contained within the experi-
mental spectrum are selected. Next, structure generation
from the selected fragments is performed using overlap-
ping common atoms to investigate the presence of larger
fragments and, ultimately, to try and piece together a full
structure solution. If this approach fails then the program
automatically switches to the so-called "classical" genera-
tion mode, an approach which is common in the first-
generation systems (e.g. [32]. This method has been
described in detail in our previous work [35].
During the course of processing the 2D NMR spectral data
the program analyzes the contour diagrams associated
with the 2D spectra and determines the chemical shifts of
the intervening nuclei (and therefore the coordinates of
the peaks). The spectral parameters of the peaks are then
imported into tables containing chemical shifts, intensi-
ties, and the multiplicities of the signals for the 1D spectra
and the chemical shifts of the coupled nuclei and the
intensities of the peaks in the 2D NMR spectra. It is also
possible to input tables of 1D and 2D NMR spectrum
peaks directly from a keyboard. Next, HMBC and COSY
correlations are converted into connectivities represented
by the chemical shifts of pairs of carbon atoms. Thus, for
example, if a COSY spectrum exhibits a (H-i)↔(H-k) cor-
relation then the connectivity involving the chemical
shifts of the C-i and C-k atoms is produced.
Molecular Connectivity Diagram
Further solution of the problem proceeds under the user's
control in most cases. To provide a complete and clear
pattern of the properties of the skeletal atoms and the con-
nectivities between them the program places skeletal
atoms together with hydrogen atoms attached to skeletal
atoms in a display window. We refer to this visual depic-
tion as a Molecular Connectivity Diagram, MCD (Fig. 5.).
The values of the chemical shifts of the carbon and hydro-
gen atoms are accompanied with atom properties and are
shown for each CHn group.
Obviously, if the hybridization state of the carbon atoms
and the possibility of their bonding to heteroatoms are
taken into account (i.e. specific constraints are intro-
duced), then the process of structure generation is sub-
stantially accelerated. Therefore, with the use of the APCT
library, the program sets (if it is possible) the most prob-
able hybridization of each carbon atom (sp3, sp2, sp) andPage 8 of 26
(page number not for citation purposes)
tration or time limitations then the program will attempt
to reconstruct a spectrum from the 2D NMR data. To
the possibility of that carbon being adjacent to a neighbor
with heteroatoms ("forbidden," "at least one atom," "at

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
least two atoms," "not defined"). The atom properties
automatically assigned by the program can be edited by
the user taking into account the chemical composition
and additional information available from other spectral
data (e.g., IR and Raman spectroscopy). If a distinct mul-
tiplet is observed in the 1H NMR spectrum from a struc-
tural block (C-i)Hn then the total number of H atoms
attached to carbons adjacent to the (C-i) carbon is set.
This property is determined by the chemist after visual
analysis of the 1H spectrum pattern and taking into
account coupling constants (if measured). Note that the
atom properties should be set and edited with great cau-
tion because an erroneous assumption leads to the exclu-
sion of the correct structure from the output file. All
structural constraints presented in the molecular connec-
tivity diagram are used during the structure generation. In
certain cases it is possible that one or more signals and too
weak to be observed in the 13C NMR spectrum. This is
especially common of course for quaternary carbon and
carbonyl carbon atom. However, since the total number
MCD and structure generation is then performed. In such
cases the number of generated structures will be consider-
ably greater because the inserted carbon atoms do not
have their properties (hybridization, neighboring with
heteroatoms, etc.) defined.
Before initiating structure generation the program auto-
matically performs a logical analysis of the data presented
in the MCD to check their consistency (i.e., the absence of
nonstandard connectivities). Our algorithm proposed for
the logical analysis and correction of the MCD is rather
sophisticated and its complete description has been pub-
lished previously [28]. When one or more nonstandard
connectivities are found an attempt is made to resolve the
contradiction automatically by the elongation of suspi-
cious connectivities by one bond. This frequently allows
structure generation from the corrected MCD. An attempt
to perform structure generation without correcting the
MCD leads to an empty output file or an invalid solution
(i.e. the absence of the correct structure from the output
file). The system includes approaches [27-30] for the
detection of an invalid solution.
Our studies have demonstrated that in approximately
90% of all cases the program detects the presence of non-
standard connectivities. However, even when the program
yields a false conclusion that contradictions are absent, a
valid solution can be obtained using fuzzy structure gener-
ation, which was first developed in our research [28,30].
This strategy uses approaches which provide a valid solu-
tion even at a very high degree of uncertainty of the initial
information. The problem is formulated as follows: find a
valid solution provided that the 2D NMR data involves an
unknown number m (m = 1–15) of nonstandard connec-
tivities and the length of each of them is also unknown.
The efficiency of this proposed approach was verified by
the examination of more than 100 real problems with ini-
tial data containing up to 15 nonstandard connectivities
differing in length from the standard correlations by 1–3
bonds. Note that StrucEluc is the only system that
includes mathematical algorithms for the search of con-
tradictions and their elimination and, therefore, can work
with real 2D NMR data.
Structure Generation and Verification
Structure generation is performed from the structural
blocks C, CH, CH2, CH3, and heteroatoms when con-
straints are imposed that are entered as connectivities.
Note that a group of carbon atoms showing COSY con-
nectivities between them makes up a fragment (a con-
nected subgraph), while each carbon atom connected to it
via HMBC connectivities forms a "fuzzy fragment". Struc-
ture generation is controlled by options which impinge
Example of a structure and its Molecular Connectivity Dia-gra of HMBC connectivitiesFigure 5
Example of a structure and its Molecular Connectiv-
ity Diagram of HMBC connectivities. In the structure,
the HMBC connectivities are shown by arrows. It is seen that
131.6-18.0 and 36.9–131.8 connectivities are nonstandard
(extending out above 3 bond correlations).Page 9 of 26
(page number not for citation purposes)
of carbon atoms is known from the molecular formula the
absent carbon atoms are automatically included in the
constraints on the sizes of the cycles and bond orders,
introduce lists of obligatory and forbidden fragments and

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
include a check for the fulfillment of Bredt's rule, etc. The
use of the APCT library for setting the atom properties (see
above) significantly accelerates the structure generation
process. In particular, for 80% of ca. 300 problems that we
have solved, the generation time was less than 1 min.
It is important to note that during the structure generation
process chemical bonds are drawn between atoms pos-
sessing chemical shifts and properties specified in the
MCD rather than abstract atoms. Therefore, in the gener-
ated structures, C and H atoms have assigned chemical
shifts. The spectral filtration of structures proceeds simul-
taneously with generation, and three modes of filtration
severity can be specified taking into account the ambiguity
of boundaries of characteristic spectral ranges. It has been
found that filtration in even the most relaxed mode leads
to a decrease in the number of structures in the output file
by a factor of tens and even hundreds [30].
Selection of the Most Probable Structure
For the correct elimination of duplicates and choice of the
most probable structure the prediction of 13C NMR spec-
tra and the calculation of the average deviations of the cal-
culated spectra from the experimental data is used in the
StrucEluc system. Therefore, 13C NMR spectra are pre-
dicted for all structures passed through the filters. Since an
output file may be rather big (hundreds, thousand and
even tens of thousands of structures) very fast algorithms
for NMR spectrum prediction are necessary.
With this in mind we developed fast calculation algo-
rithms [36-38], one of which is based on increments and
the other employs artificial neural networks. These algo-
rithms provide a calculation speed of 6000–10,000 chem-
ical shifts per second with the average deviation of the
calculated chemical shifts from the experimental shifts
equal to d = 1.8 ppm. For a file containing tens of thou-
sands of structural isomers the calculation time by the two
methods is not longer than several minutes. Next, redun-
dant isomorphous structures are removed. Since different
deviations correspond to duplicate structures with differ-
ent signal assignments the structure with the minimum
deviation is retained from each subset of identical struc-
tures (i.e., the "best representatives" are selected from
each family of identical structures). Isomers are then
ranked by ascending deviation and our experiences show
that the correct structure commonly is in first place with
the minimal deviation or at least among the first several
structures at the beginning of the list.
To check the correctness of the preferable structure the 13C
and 1H spectra can be calculated for the first 20–50 struc-
tures from the ranked file with the use of the fragment
explanation how each predicted chemical shift was calcu-
lated. These calculations use a HOSE-code (Hierarchical
Organization of Spherical Environments) based predic-
tion approach (see review [22]) and employ a database
containing 175,000 structures with assigned 13C and 1H
chemical shifts. For each atom within the candidate struc-
ture, related structures used for the prediction can be
shown with their assigned chemical shifts and this allows
the user to understand the origin of the predicted chemi-
cal shifts. Finally, the file is repeatedly ranked by ascend-
ing dA, where dA is the deviation of the calculated 13C
spectra using the fragment method. If the difference
between the deviations calculated for the first and second
ranked structures is small [d(2) - d(1) < 0.2 ppm] then the
final determination of the preferable structure is per-
formed by the expert. Generally the choice is reduced to
between two or, less frequently, three structures. In diffi-
cult cases, the 1H NMR spectra can be calculated for a
detailed comparison of the signal positions and multiplic-
ities in the calculated and experimental spectra. Solutions
that may be invalid are revealed by a large deviation of the
calculated 13C spectrum from the experimental for the
first structure of the ranked file. For instance, if dA(1) > 3–
4 ppm the solution should be checked using fuzzy struc-
ture generation. The reduced dA(1) value found as a result
of fuzzy structure generation should be considered as a
hint regarding the presence of one or more nonstandard
connectivities. The correct solution is usually obtained
using different modes of fuzzy structure generation [30].
The NOESY spectrum [39], which imposes constraints on
geometric distances between intervening protons can also
give valuable structural information (spatial constraints)
at this step.
In the course of structure elucidation of natural products
we encountered a number of unknown molecules with
symmetry. The presence or absence of molecular symme-
try does not influence structure generation within 1D
expert systems. We determined that an attempt to gener-
ate symmetric structures from 2D NMR data lead to
unmanageable processing times. The cause of this failure
was carefully investigated and peculiarities of symmetric
structure generation were discovered. An enhanced algo-
rithm was developed to reveal symmetry features in 2D
NMR data and then it is automatically adjusted to the gen-
eration of symmetric molecules. As a result the processing
time necessary for the generation of symmetric molecules
is now of the same order as for molecules without symme-
try. This algorithm is still not optimized and we are pres-
ently researching improvements to allow further
acceleration of the structure generation process by using
symmetry for this purpose.Page 10 of 26
(page number not for citation purposes)
method [23]. Although this method is not as fast as the
other two methods it allows the user to obtain a detailed
From the very beginning the StrucEluc system was
intended for the identification of standard organic mole-

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
cules. Since ionic structures are possible for both natural
products and synthesized organic molecules the structure
generation algorithm was also adjusted to allow for the
generation of ionic structures. Thus, in principle the sys-
tem is now capable of elucidating structures of any
organic molecules regardless of their topological and elec-
tronic properties.
Let us consider two examples demonstrating the applica-
tion of the StrucEluc system in the Common Mode.
Examples of problem solving in Common Mode
Problem solution using Crisp Structure Generation
Ge et al [40] isolated and determined the structure of a
new unusual natural product named Hopeanolin. To chal-
lenge StrucEluc we used published 1D and 2D NMR data
[40] to elucidate the "unknown" structure. 80 HMBC and
3 COSY correlations were input into the program and the
MCD was created. Atom hybridization was automatically
set for all carbons except 8 CH atoms and two quaternary
C atoms with chemical shifts in the range 90–120 ppm. In
so doing the program took into account that chemical
shifts observed in this region can be assigned either to C =
C or to O-C (O-C-O) carbons. A single obvious constraint
was imposed: three sp2 carbon atoms with chemical shifts
between 170 and 180 ppm were marked as having two
neighboring oxygens each. No nonstandard correlations
were detected in the 2D NMR data by checking the MCD.
The results of the structure generation and filtering were:
68 structures were generated in 12 s, and 9 structures were
stored after filtering (we denote this as k = 68→9). 13C
NMR chemical shifts were predicted for all structures
using all of the fragment, incremental and neural net
approaches. The four structures at the top of the ranked
structural file are shown in Fig. 6., where the "best" struc-
ture is Hopeanolin.
Application of Fuzzy Structure Generation
In the analysis of cleospinol A [41] with molecular formula
C20H32O2, the 2D NMR data are comprised of 21 COSY
and 55 HMBC correlations. These data were used to eval-
uate the possibility of solving a problem in those cases
when a large number of nonstandard correlations were
present. In this case the 2D NMR data contained 3 HMBC
and 12 COSY nonstandard correlations whose length
should be enlarged by 1–3 bonds.
As shown in Fig. 7. the COSY connectivities are repre-
sented below on the structure by blue double-headed
arrows while the HMBC correlations are defined by green
unidirectional arrows from the proton to the carbon to
which it is long-range coupled.
The COSY, HMQC, and HMBC spectral data associated
with the compound were fed to the program and the MCD
was generated. A check of the MCD was accompanied by
the automatic removal of contradictions. The software
program displayed a message declaring that contradic-
tions had been detected and resolved while the minimum
number of NSCs was estimated to be equal to 7. Unfortu-
nately, strict structure generation from the automatically
edited MCD resulted in an empty output file. This result
was interpreted as evidence of the presence of either unde-
tected additional nonstandard correlations or those
whose lengths must be augmented by more than one
bond. Detailed analysis of this problem and possible ways
of its solution are reported elsewhere [30]. Here we will
describe the most universal and systematic approach.
Fuzzy structure generation was initiated assuming only
that the number of nonstandard connectivities numbered
no more than 15. In this case 18,281,379 connectivity
combinations from 40,225,345,056 theoretically possible
combinations were used for structure generation. The fol-
The four structures at the top of the ranked structural fileFigure 6
The four structures at the top of the ranked structural file. The first ranked structure is identical to the structure of Page 11 of 26
(page number not for citation purposes)
Hopeanolin determined by authors [40].

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
lowing result was obtained: k = 769→430→245 (245
structures retained after removing duplicates), a genera-
tion time of tg = 29 min 9 sec, and the correct structure was
ranked first by all methods of spectrum prediction.
The program therefore was able to identify the correct
solution even when 15 nonstandard connectivities existed
in the 2D NMR data and especially in the presence of
HMBC and COSY connectivities representing both 6JCH
and 6JHH correlations. Note that only ~10-4 of the theoret-
ically possible connectivity combinations were processed.
The real number of processed connectivity combinations
nreal was > 18 million. Nevertheless, the high-speed struc-
ture generator present in the Structure Elucidator program
completed the process in a reasonable time.
The Fragment Mode of Operation
As shown above computer-assisted structure elucidation
using 2D NMR data is quite efficient for the determination
of structures of complex natural compounds. However, if
the structural restrictions imposed by the MCD are not
sufficient for the generation of a reasonable number of
possible structures within an appropriate time, it is to be
expected that the utilization of molecular fragments can
greatly facilitate the solution of the problem. The fragment
approach has been successfully used in all first generation
expert systems based on 1D NMR spectra only. However
in those cases when 2D NMR data are employed, the uti-
lization of molecular fragments is hampered because all
carbon atoms existing in a fragment used in solving the
problem must be supplied with chemical shifts. Moreover,
sponding fragments in the experimental 13C NMR
spectrum of the unknown under study. In addition, the
accommodation of one or more fragments within a set of
connectivities derived from the 2D NMR data is a problem
that required the development of new algorithms. Appro-
priate fragments to aid in the solution of a problem can
usually be found in the fragment library of the StrucEluc
system. The main advantage of these fragments is that all
fragment carbon atoms are supplied with the 13C NMR
assignments obtained from the full structures that were
used for creation of the fragment data base.
The first step in the process is a fragment search of the frag-
ment library (FL). As a result, a set of L found fragments is
selected. The next step is the creation of the MCDs using
the found fragments (FF). For this purpose, either all FFs
or any selected number of them can be incorporated by
the operator. An algorithm that implements this proce-
dure was developed and realized within StrucEluc system
[25]. The program produces all rearrangements of the
experimental chemical shifts within the corresponding
carbon atoms of the fragment. The chemical shift distribu-
tion of carbon atoms that produces a conceivable assign-
ment of a given fragment has to be verified. During the
verification process, the program checks whether or not
the carbon atom assignments correspond to the experi-
mental chemical shift correlations comprising the skeletal
atoms making up the fragment. The fragments that survive
the test are then included in the set of prospective frag-
ments.
The more skeletal atoms that are accounted for by the frag-
ments then the shorter the process of structure elucida-
tion. With this in mind an algorithm combining the
prospective fragments within one molecular connectivity
diagram was developed. To realize this procedure, all pos-
sible combinations of prospective fragments are searched
and only combinations that are in agreement with the
experimental 2D NMR correlations are chosen. The frag-
ment combinations that pass this examination form a set
of prospective fragment combinations. These fragments are
then "projected" onto the MCDs together with any
remaining free atoms. The user can then visually analyze
these MCD diagrams.
The speed of structure generation depends on the size of
the molecular fragments. If the number of small frag-
ments composing the MCD is large enough then this will
speed up the generation process. Structure generation is
also much faster when the MCD is comprised of only a
small number of large fragments. Depending on the size
of the molecule being analyzed and the size of fragments
placed at the beginning of the ranked list of found frag-
The structure of cleospinol AFigure 7
The structure of cleospinol A. COSY correlations are
denoted by blue arrows, HMBC correlations – by
green arrows.Page 12 of 26
(page number not for citation purposes)
the values of these chemical shifts must be as close as pos-
sible to the observed values for the atoms of the corre-
ments, the number of fragments included into the MCD
usually varies from 1 to 4.

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
The conclusion of all further verification procedures is a
check of all of the MCDs produced for the presence of
contradictions. The program offers an option that deletes
all MCDs that are recognized as containing contradic-
tions. The contradictory MCDs contain fragments with
carbon atoms associated with assignments that contradict
the standard length of the corresponding connectivities.
The diagrams remaining after checking can be used in the
structure generation process.
In the process of analyzing a novel compound, it is
entirely possible that there will be no fragments in the
database that will reduce the magnitude of the challenge.
It is natural in such cases to expect that the introduction
of user-defined fragments may help to form the MCDs.
The main qualitative difference between a found fragment
(FF), and a user fragment (UF), is that the FF already con-
tains carbon atoms with assigned chemical shifts while
the carbon atoms of the UF have no carbon chemical shift
assignments. Two ways have been suggested to introduce
user fragments into the program:
 calculate the carbon chemical shifts of the fragment;
 search the FL for fragments that comprise the user frag-
ment.
It is likely that fragments from at least one of these two
sources will be available for use by the program. Experi-
ence has shown [25-27] that an appropriate combination
of FFs and UFs frequently allows the solution of rather dif-
ficult problems.
Example
A new dimeric natural product, ashwagandhanolide (Fig.
8a), was isolated by Subbaraju et al [42]. Its molecular for-
mula was determined as C56H78O12S on the basis of the
molecular ion observed at m/z 975.5285. The structure of
this compound was determined using 2D NMR data as
well as additional information obtained from comparison
of experimental spectra with the structures and spectra of
related molecules. In the article [42] only 35 HMBC corre-
lations are reported (there is no COSY data). The number
The structure of ashwagandhanolide (a) and a Found Fragment (b)Figure 8Page 13 of 26
(page number not for citation purposes)
The structure of ashwagandhanolide (a) and a Found Fragment (b).

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
of correlations is small due to severe overlap in the 1H
NMR spectrum. An attempt to solve the problem using
StrucEluc in Common Mode showed that the processing
time and the number of generated structures would be
unmanageable. Therefore a fragment search using the 13C
NMR spectrum was performed and 5524 fragments were
found in the Fragment Library. The displayed Found Frag-
ments are ranked in decreasing order of the number of
carbon atoms, and the first ranked fragment is shown in
Fig. 8b. Visual comparison of the molecular structure with
the structure of the fragment confirms that the fragment is
a substructure of the structure and its carbon chemical
shifts are very close to the values measured for the full
structure. The procedure of creating MCDs from the
Found Fragments was initiated and the program produced
960 MCDs with different shift assignments. Checking
MCDs for contradictions took 28 min, and structure gen-
eration resulted in k = 960→24→6, tg = 22 s.
The three top structures in the ranked file are shown in
Fig. 9. The most probable structure 1 coincides with the
structure determined in the publication [42].
User Database application
Currently, about 27 million compounds have been iden-
tified and well over a quarter million new compounds are
synthesized or isolated each year. It is possible that many
such compounds will have no analogues in the knowl-
edgebases of expert systems. As a result it is not always
possible to find fragments in the database that will help to
elucidate the structure of a compound from a new class of
chemicals. Besides, it is common that an investigator is
unable to specify a fragment that may be present in the
structure under examination. Investigation [25,27,43,44]
has shown that if the methods described above are inef-
fective, then the creation of a user database could enable
a solution. The StrucEluc system provides algorithms and
capabilities to create user databases and thereby to allow
searches for fragments of related compounds. In particu-
lar, even if only one compound of a similar structure is
known, it can be successfully used for the creation of a
user database. With the help of user databases the system
can easily be adjusted for the elucidation of compound
classes that are commonly investigated by a given labora-
tory. Investigations have shown that the failure to utilize
library fragments was most frequently due to the follow-
ing issues: a) the fragments appropriate for a given prob-
lem are missing in the knowledgebase; b) appropriate
fragments are found but the number of possible permuta-
tions of the carbon atom assignments in these fragments
is so huge that the structure generation process is too long;
c) the molecule under investigation is characterized by a
deficit of hydrogen atoms and as a result the 2D NMR cor-
relations do not deliver a number of constraints enough
to produce a manageable output file in a reasonable time.
As an example we will consider utilization of the user
database for the structure elucidation of natural products
of the cryptolepine series.
The structures of the natural products which failed to be
elucidated both in the common and found fragment
modes are shown in Fig. 10.
These molecules are relatively large, highly unsaturated,
and have large "silent" fragments (displayed in bold) that
contain no hydrogen atoms, thereby preventing access to
The three top structures of the ranked fileFigure 9Page 14 of 26
(page number not for citation purposes)
The three top structures of the ranked file.

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3structural information through COSY correlations. In par-
ticular, for structures a-d COSY correlations are observed
for protons contained within the benzene rings only.
These factors contribute to a very challenging elucidation
process for these compounds.
The elucidation of the structures of alkaloids a-c was
approached using a user database adjusted for cryp-
tolepine structure analysis. The main assumption was that
unknown compounds were members of the cryptolepine
indoloquinoline alkaloid series since all materials were
isolated from the same plant. Taking this into account,
information regarding earlier published members of the
series shown in Fig. 11 was introduced into a user data-
base. Assigned spectral data referring to these compounds
were obtained from original publications. This methodol-
ogy is fairly typical in practice and is frequently used by
chemists when manually determining the chemical struc-
tures of natural products or reaction products from a syn-
thesis.
Compounds of the cryptolepine series were incorporated
in the user library as full structures along with their asso-
ciated 13C NMR spectra. The algorithm used to create a
User Fragment Library is similar to the algorithm used to
create the StrucEluc system knowledgebase and includes
the following two basic steps:
1) The program excises as complete as possible a set of
fragments from all structures included in the structural
file. 2) Atoms in the fragments are assigned chemical shift
values that they have in the corresponding full structure.
This procedure produced a user library containing 342
fragments from the cryptolepine series. This user database
was then successfully used to elucidate structures a-c. The
elucidation process is described in details in our other
works [25,27,43].
This approach has been also applied to the solution of a
particularly challenging problem that remained unre-
solved for over a decade [44]. The structure of an
unknown alkaloid d (Fig. 10), quindolinocryptotack-
ieine, also belonging to the cryptolepine family, was elu-
cidated. NMR data were collected starting in 1991 and a
solution remained elusive, in part due to extensive spec-
tral overlap in both the 1H and 13C spectra. Manual anal-
ysis did not present a conclusive structure while
computer-assisted elucidation produced a series of con-
ceivable structures. A single structure consistent with all of
the spectral data was selected from the list of computer-
generated structures. To the best of our knowledge, this is
the first case of the application of an expert system to
determine the molecular structure of a complex natural
product which was not amenable to identification by
competent spectroscopists.
Impact of rapid NMR spectrum prediction on system
properties and capabilities
Traditionally developers of expert systems aspired to use
all possibilities to provide a solution to a problem as a
minimum structural file satisfying all the constraints
imposed by spectra and a priori information. The shorter
the list of candidate structures the simpler is the choice of
the most probable structure. The crucial constraint is the
processor time necessary for NMR spectrum prediction for
all structures contained within the output file. From the
general considerations explained in the Section 2, it is
clear that the output file can be markedly reduced by
imposing severe constraints ("axioms") capable of reject-
ing as many as possible conceivable isomers. Application
of severe constraints does however increase the risk that
the genuine structure will be lost and the solution will
become invalid.
The situation improved when new incremental and artifi-
cial neural net methods of 13C chemical shift calculation
were produced [36-38]. High speed chemical shift predic-
tion (6000–10,000 shifts/s) now allows calculations to be
Examples of structures of the natural products which failed to be elucidated both in the Common an Found FragmentmodesFigur 10
Examples of structures of the natural products which
failed to be elucidated both in the Common and Page 15 of 26
(page number not for citation purposes)
generated for a file containing hundreds of thousands ofFound Fragment modes.

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
structures in 5–10 min and as a result there is no necessity
for imposing the strict constraints. The refusal of too rigid
and risky constraints sufficiently raises the reliability of
solutions obtained with the assistance of the StrucEluc
system.
Accelerating the Structure Generation Process
The Structure Generation algorithm is arranged in such a
way that first it produces substructures which are then
complemented by new bonds until full structures are gen-
erated. It is possible to consider that structures generated
from a given substructure make up a definite branch of the
whole generation process. If the "root" substructure con-
tains a carbon atom C-i with such an environment for
which the predicted chemical shift δ(C-i) contradicts the
experimental 13C NMR spectrum, the whole structural
branch will be truncated during the spectral filtering proc-
ess. We believed that fast 13C chemical shift prediction for
incomplete structures during the generation process
trum. With this in mind we developed a pilot version of a
structure generator that is enhanced by the increment
based 13C chemical shift calculation of incomplete struc-
tures. The main problem that should be resolved is to find
a compromise between the number of HOSE code envi-
ronment spheres taken into account during the chemical
shift calculation for a given carbon atom and balancing it
with saving structure generation time. This algorithm is
rather complicated and work is presently underway to
improve the system. To date the coefficient of acceleration
varies between 1 and 70 depending on the nature of the
problem. The coefficient is expected to be greater for mol-
ecules containing many heteroatoms and different func-
tionalities.
Rapid verification of structural hypotheses
As shown above the StrucEluc system is capable of infer-
ring all conceivable structures that agree with the experi-
mental spectra and the additional constraints imposed by
Compounds of the cryptolepine series used to create User fragment libraryFigure 11
Compounds of the cryptolepine series used to create User fragment library.Page 16 of 26
(page number not for citation purposes)
would prevent generation of such structural branches that
deliberately contradict the experimental 13C NMR spec-
the chemist. NMR spectrum prediction is then used to
select the most probable structure and compare it with

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
other structures ranked in decreasing order of probability.
The advantages of this systematic approach are obvious:
all structures are exhaustively enumerated, ranked and
automatically displayed.
The overwhelming majority of structures published in the
literature were elucidated by traditional methods using
knowledge, experience and the intuition of organic chem-
ists. Obviously the human expert is frequently unable to
derive and check all possible structures. Therefore, it is not
surprising that sometimes incorrect structures are eluci-
dated by chemists. A recent example is the high-profile
case of the hexacyclinol structure (C23H28O7). Schlegel et
al [45] separated a natural product and suggested its struc-
ture based on the 2D NMR data. Le Clair [46] reported a
synthesis of the structure, but Rychnovsky [47] suggested
another structure based on 13C chemical shift prediction
using a GIAO-based quantum-mechanical DFT (Density
Functional Theory) prediction method. He showed that
the calculated spectrum of his structure was more consist-
ent with the experimental spectrum than the calculated
spectrum of Schlegel's structure. Rychnovsky's suggestion
was confirmed by Porco et al [48] who synthesized the
suggested structure and unambiguously determined the
structure using X-rays. It was later shown [49] that if
StrucEluc was used for the structure elucidation of this
particular natural product the researchers would have eas-
ily avoided their erroneous suggestions.
Rychnovsky convincingly showed that quantum-mechan-
ical DFT chemical shift calculations could be very useful
and efficient for the structure elucidation of complex
organic molecules in spite of significant time investments
(the total processing time for an isomer C23H28O7was
approximately 12 h using a 3 GHz Pentium 4 processor
based computer).
A series of articles have been published (for instance [50-
52]) where DFT NMR spectrum prediction was success-
fully used for this purpose. However as mentioned earlier
quantum-mechanical calculations are time consuming
and require a knowledgeable and skilled expert to obtain
reliable results. We suggested that the application of our
fast and accurate chemical shift prediction algorithms
could help in many cases without the need for quantum-
mechanical calculations or even in their place. Our sug-
gestion was confirmed by empirical NMR spectrum pre-
dictions performed for many structures taken from the
literature for which the DFT approach was used to verify
different structural hypotheses. We recently submitted an
article describing a series of interesting cases [53]. We con-
sider below two examples applying the StrucEluc applica-
tion to the revision of structures
none A and Boletunone B (see structures a and b in Fig. 12).
Steglich and Hellwig [55] have shown that both structures
suggested by the authors [54] are wrong, and alternative
structural formulae (A and B, Fig. 12) were offered and
proved for these compounds.
We have investigated the potential of using StrucEluc to
analyze the data of Kim et al and both structures have
been elucidated in a systematic manner.
Boletunone A
As shown in Fig. 12. in order to accept the proposed struc-
ture a, the authors [54] postulated that the length of one
HMBC correlation (198.7→76.2) corresponds to 4JCH.
When the 1D and 2D NMR spectra (COSY and HMBC)
were input into the StrucEluc program the first run was
performed in Common Mode with crisp Structure Gener-
ation. The two structures presented in Fig. 13. were gener-
ated in 0.15 s and the 13C chemical shifts predicted by
neural nets, and the average deviations dN(13C) are
shown.
The revised structure, A, was unambiguously distin-
guished as the best structure by the average chemical shift
deviation, while structure a was not generated at all
because the presence of nonstandard correlations was not
allowed. Note that the quality of spectrum prediction for
structure A is characterized by R2 = 0.999 and a graph of
the linear regression shown in Fig. 14. demonstrates
almost perfect coincidence with the target line Y = X.
As a next step Kim's declaration regarding one non-stand-
ard correlation was taken into account and Fuzzy Struc-
ture Generation was initiated under a condition that 3-
and 4-membered cycles are forbidden.
Result: k = 18→13→10, tg = 0.7 s.
The first four structures of the ranked output file are
shown in Fig. 15.
The correct structure A was again unambiguously deter-
mined while the original structure a was ranked in fourth
position with a dN(13C) value more than twice as large
than for the first-ranked structure. The R2 value extracted
for structure a is equal to 0.995 but the regression graph
(Fig. 16.) demonstrates significant scattering of the calcu-
lated shifts.
Boletunone B
Bagno et al [50] have used DFT chemical shift prediction
to validate structure b of Boletunone B [54] in comparison
with the revised structure B proposed in work [55]. They
1 13Page 17 of 26
(page number not for citation purposes)
Kim et al [54] described the structures and properties of
two new natural products and attributed them to Boletu-
calculated the H and C spectra both for the original and
revised structures. The geometries were optimized at the
B3LYP/6-31G(d,p) level in the gas phase, and the NMR

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
properties calculated with B3LYP/cc-pVTZ, in the gas
phase and with a solvent reaction field of DMSO. The
mean absolute errors were corrected to take into account
the systematic errors of the DFT chemical shift calcula-
tions.
The authors [50] showed that the 13C spectrum calculated
for the revised structure B is in better agreement with the
The 1D and 2D NMR data of Boletunone B presented by
Kim et al [54] were input to StrucEluc and Crisp Genera-
tion was performed – no nonstandard correlations were
assumed in the 2D NMR data for the first run. The results
gave: k = 4343→37→31, tg = 12 s.
It turned out that neither structure b nor structure B were
generated, while the "best" structure in the ranked struc-
13
The originally proposed (a, b) and revised structures of Boletunone A and BFigure 12
The originally proposed (a, b) and revised structures of Boletunone A and B.Page 18 of 26
(page number not for citation purposes)
experimental spectrum than the spectrum calculated for
the originally proposed structure b.
tural file had a dN( C) value equal to 3.1 ppm. The result
obtained hints that at least one nonstandard connectivity

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
is present in the 2D NMR data (Kim et al [54] assumed
one NSC in HMBC data). Structure generation was
repeated in the Fuzzy Mode and the presence of one NCS
was allowed in the HMBC data only. The results gave: k =
37176→149→135, tg = 1 m 40 s. The top ranked struc-
tures are shown in Fig. 17.
The revised and correct structure was again recognized as
the most probable using neural net based 13C chemical
shift calculation, while the original structure b was ranked
only in fourth position. The values of R2 calculated for
structures B and b are equal to 0.999 and 0.995 while the
maximum chemical shift deviations are 3 ppm (B) and 9
ppm (b).
These examples show that the application of a systematic
approach that was thoroughly developed and tested by
solving hundreds of problems in combination with new
fast and accurate algorithms of NMR spectrum prediction
gives correct solutions very quickly and without the appli-
cation of cumbersome and time-consuming quantum-
mechanical methods. When several structural hypotheses
should be compared prior to applying DFT methods it is
worth validating competing structures using more generic
NMR predictors such as those discussed in this work.
carbon atoms in an unusual environment not described in
the training or reference structural sets. DFT calculation
may also help when the average deviations calculated for
the competing structures are very close and there is no reli-
able basis for making the final decision.
The determination of relative stereochemistry of
elucidated structures
The biological activity of natural products and drug mole-
cules can be highly dependent on the stereochemistry of a
molecule. Indeed, there are examples known where one
stereoisomer can exhibit vastly different pharmacologic
activity from the other stereoisomer. As an example, D-
propoxyphene has analgesic activity while the other opti-
cal isomer, L-propoxyphene, has antihistaminic activity.
Stereoisomer considerations can influence reaction path-
ways and certainly reaction kinetics, with one form being
favored over another. Generally, the final step in contem-
porary structure characterization efforts is to define the
relative and if possible absolute stereochemistry. NMR
methods are generally well suited to the former while the
latter is generally obtained using chemical structure mod-
ification combined with NMR studies or by X-ray crystal-
lographic methods. NMR-based determination of relative
stereochemistry is based on the nuclear Overhauser effect
Solution to the problem for boletunone AFig re 13
Solution to the problem for boletunone A.Page 19 of 26
(page number not for citation purposes)
Quantum-mechanical calculations can play a decisive role
in selecting the right structure if empirical NMR spectrum
prediction fails to calculate the chemical shifts of some
(NOE), which is dependant on the distance separating the
cross-relaxing nuclides [39]. Typically NOESY or ROESY
two-dimensional NMR experiments or their selective 1D

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
analogs are used to provide the data for this analysis in
rigid molecules.
The determination of the relative stereochemistry for new
is conceivable to distinguish the following two stages in
the traditional strategy of relative stereochemistry deter-
mination: 1) selection of a set of the most probable stere-
oisomers using similar reference structures for which the
The correlation between calculated and experimental 13C NMR spectra for structure AFigure 14
The correlation between calculated and experimental 13C NMR spectra for structure A. The target line Y = X is
colored in blue.Page 20 of 26
(page number not for citation purposes)
organic compounds, especially natural products, has
become a routine procedure which needs to be speeded
up and automated as much as possible. Conditionally, it
relative stereochemistry has been determined; 2) examin-
ing these stereoisomers by NOESY/ROESY spectra, molec-
ular modeling and quantum-chemical 13C NMR

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
prediction for finding the most preferable stereoisomer.
Methods allowing computational modeling of stereo-
chemistry have been developed and incorporated into
StrucEluc system.
The selection of the set of the most probable stereoisomers
When ACD/NMR predictors were developed information
regarding the relative stereochemistry of reference mole-
cules was taken into account. As a result, NMR spectra
were calculated by all three methods (fragmental, incre-
mental and neural nets) and these computational
approaches are sensitive to different degrees to the orien-
tation of the stereobonds in the structure under investiga-
tion. We undertook a study aimed to evaluate the
possibility of using empirical NMR chemical shift predic-
tion as a tool to select the set of most probable stereoi-
somers [56]. We found that the fragmental approach was
the most sensitive to stereochemistry of the three methods
and showed that it can be used for the purpose of stereo-
chemical investigations. The approach was examined
using a series of new natural products reported in the lit-
erature in 2008 and belonging to a number of different
classes – steroids, alkaloids, terpenes, cembranoids, etc.
The stereochemistry of the compounds was reported in
the corresponding publications. The structural formula of
each structure examined was input into the Proposed
Structure window of StrucEluc, and all N = 2n (n – number
of stereocenters) mathematically conceivable stereoi-
somers were generated and depicted by the program. In
our experiments the value of N varied between 64 and
4096 (n = 6÷12). 13C NMR chemical shift prediction was
then performed and the stereoisomers were ranked in
descending order of the average deviation between the
experimental and calculated spectra. The study showed
that the correct stereoisomer is usually placed at the top of
the ranked file and took between 1st to 3rd position in the
list therefore allowing the program to serve as a filter capa-
ble of rejecting non-perspective stereoisomers. Note that
NOE data were not even used at this stage. Subsequent vis-
ualization of the NOESY/ROESY connectivities on the
structures allows rapid determination of the most pre-
ferred member of the "best" isomers set.
For example, Maloney et al. [57] reported the structural
The top of the file containing structures ranked by dN(13C) valuesFigure 15
The top of the file containing structures ranked by dN(13C) values. The revised structure A is in first position while
the structure a is placed in fourth position.
The correlation between calculated and experimental 13C NMR spectra for s ructure aFigure 16
The correlation between calculated and experimen-
tal 13C NMR spectra for structure a. The target line Y =
X is colored in blue. The first number in the frames denotes Page 21 of 26
(page number not for citation purposes)
characterization of two new cucurbitacins, one of which is
shown in Fig. 18 (12 stereogenic centers were determined
and marked by the StrucEluc program automatically):
the experimental value of a chemical shift and the second
number represent the calculated one.

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
13C NMR spectra were calculated for 4096 stereoisomers
in ca. 3 h and the ranking procedure promoted the correct
structure to the first position. Note that spectra could be
calculated only for 2048 stereoisomers due to presence of
enatiomeric pairs which have identical spectra (the pro-
gram adjusts to this peculiarity of the full stereoisomer
family). In another example, for the structure elucidated
and reported in section 3.2.1.1 the correct stereoisomer
(among 64 possible structures, n = 6) was placed by the
program in third position in the rankings (Fig. 19):
Nevertheless, the final determination of relative stereo-
chemistry requires additional confirmation. In publica-
tions where DFT based 13C chemical shift prediction was
used for selection of the most probable stereoisomer of
natural products, the set of stereoisomers to be examined
was preliminary distinguished by the user on the basis of
different experimental data. We hope that the described
approach can ease selection of the set of stereoisomers to
be tested by QM spectral prediction and reduce the costs
associated with time consuming GIAO based calculations.
Empirical methods are expected to be more reliable when
the structure under investigation is relatively rigid.
Simultaneous determination of 3D model and relative
stereochemistry
The StrucEluc system was also enhanced by adding an
algorithm to allow the determination of 3D model and
the relative stereochemistry of a molecular structure based
on nuclear Overhauser effect constraints. The program
The beginning of the file containing structures ranked by dN(13C) valuesFigur 17
The beginning of the file containing structures ranked by dN(13C) values. The revised structure B is ranked in first
place while the incorrect structure b is placed in fourth position.
Structure of the cucurbitacinFigure 18
Structure of the cucurbitacin. Stereocenters are auto- Structure of the right stereoisomerFigure 19Page 22 of 26
(page number not for citation purposes)
matically labeled by the program. Structure of the right stereoisomer.

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
extracts NOE information from either NOESY and/or
ROESY spectra and determines the molecular stereochem-
istry accordingly. Results of selective NOE or ROE experi-
ments can also be used as input to the program. This
process can be carried out for several of the most likely
structures produced during a structure elucidation by the
expert system or performed on a chemical structure pro-
posed by the chemist.
The utility of NOESY/ROESY spectra for relative stereo-
chemistry determination is based on a direct correlation
between both the crosspeak volume integration and the
internuclear distance. Peak intensity in NOE/ROE meas-
urements has an inverse sixth power relationship. Conse-
quently [58], what can be referred to as a "strong" NOE is
generally observed between pairs of hydrogens which are
1.8–2.5 Å apart. Responses of "medium" intensity usually
correspond to an internuclear distance of 2.5–4.0 Å while
"weak" NOEs will generally be observed for larger dis-
tances if they are observed at all. NOE responses are not
commonly observed for nuclei farther than 5.0–6.0 Å
apart. These data can be used to minimize energy to
obtain the optimal molecular geometry.
Minimization algorithms deal with numerical values and,
in this case, these numerical values are extracted from a set
of NOEs overlaid on a 3D structure and examined for
goodness of fit. The function describing this goodness of
fit is called a penalty function. The better the solution then
the lower the value of the function. The function must
exhibit the lowest value for the best-matching stereoi-
somer.
In our work [59], an appropriate function was suggested
that could be minimized by calculation for all stereoiso-
meric structures or by using a stochastic genetic algorithm
to limit the number of stereoisomers that need to be
investigated [60]. To improve genetic algorithm conver-
gence, efficient methods of parameter optimization were
suggested and compared. Since the chromosomes used
here only incorporate information about stereocenter
configuration, and not information such as chair or boat
ring conformations, conformational rigidity is essential to
obtain accurate results. This requirement presently limits
the algorithm to the fused ring portions of molecules.
Meanwhile relatively simple problems of 2–6 chiral cent-
ers may be solved in a straightforward manner by the enu-
meration of all stereoisomers and the calculation of the
corresponding target function values. This process is fast
and this approach is preferred over the use of genetic algo-
rithms for molecules with smaller numbers of stereocent-
ers.
the 3D geometries of potential molecular structures was
demonstrated by the application of the suggested
approach to two complex natural products Taxol
(C47H51NO14) and brevetoxin B (C50H70O14). The most
challenging is the structure of brevetoxin B (Fig. 20).
The highly complex molecular architecture is character-
ized by a novel array of ether oxygen atoms, regularly
placed on a single carbon chain. This remarkable structure
includes 11 rings, 23 stereogenic centers, and 3 carbon-
carbon double bonds. The processing time necessary for
running over all ~8.4 million stereoisomers correspond-
ing to this structure was estimated to be about one month.
The application of a genetic algorithm allowed us to cor-
rectly determine the brevetoxin stereochemistry and the 3D
geometry model of the molecule (processing time of 2 h
50 m). This demonstrates the power of the approach we
have described to facilitate the identification of relative
stereochemistry in a complex molecule containing multi-
ple stereocenters.
System efficiency and future work
Different modes of system operating and manifold exam-
ples of its application to the molecular structure elucida-
tion of new complex organic compounds (mostly natural
products) have been described in our earlier articles [23-
30,43,44]. These works contain detailed results of investi-
gation of the system performance and working parame-
ters. We have confirmed the high efficiency and
applications of the StrucEluc system and the methodology
of its application by the elucidation of the structures of
more than 300 complex natural compounds. So far more
than 100 large molecules with between 30 to 106 skeletal
atoms have been elucidated using StrucEluc. The system is
based on highly sophisticated, flexible and fast algorithms
for structure generation, structure filtering and spectrum
prediction. As a result, the total time for solving problems
does not exceed one minute for > 80% of the problems
solved. The system can interface to programs developed
for the calculations of properties and physicochemical
parameters of organic molecules from their structural for-
mulae. This allows the prediction of many characteristics
of the new compound elucidated using the system and
can also generate systematic names according to IUPAC
recommendations.
The StrucEluc system is a commercial product of
Advanced Chemistry Development (ACD/Labs), and is
presently widely used at many pharmaceutical companies
and universities worldwide for the identification of newly
isolated natural products, synthetic impurities,
degradants and for the assignment of signals in 1-D and 2-
D NMR spectra and the verification of structural hypothe-Page 23 of 26
(page number not for citation purposes)
A method for using a stochastic genetic algorithm for the
elucidation of the stereochemistry and determination of
ses, etc.

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
Conclusion
Computer-Aided Structure Elucidation is an area of chem-
oinformatics and analytical chemistry and has been devel-
oped over a period of forty years. This development path
has forced the developers of CASE systems to overcome
many obstacles hindering the development of a software
application capable of drastically reducing the time and
effort required to determine the structures of newly iso-
lated organic compounds. Large complex molecules of up
to 100 or more skeletal atoms with topological peculiarity
can be quickly identified using, for example, the expert
system Structure Elucidator based on spectral data. Logical
analysis of 2D NMR data frequently allows for the detec-
tion of the presence of COSY and HMBC correlations of
"nonstandard" length and provides a solution to the
problem. Fuzzy structure generation provides a possibility
to obtain the correct solution even in those cases when an
unknown number of nonstandard correlations of
unknown length are present in the spectra. The relative
stereochemistry of big rigid molecules containing many
stereocenters can be determined using the StrucEluc sys-
tem and NOESY/ROESY 2D NMR data for this purpose.
The StrucEluc system is still being intensively developed
in order to expand the general application, improved
workflows and usability of the system and increased reli-
ability of results. It is expected that expert systems similar
to that described in this paper will receive increasing
acceptance in the next decade and will ultimately be inte-
grated directly to analytical instruments for the purpose of
organic analysis. Work in this direction is in progress. In
spite of the fact that many difficulties have already been
overcome to deliver on the spectroscopist's dream of
enhanced the solution of increasingly complex structural
problems will be achievable.
Abbreviations
CASE: Computer Aided Structure Elucidation; MCD:
Molecular Connectivity Diagram; FF: Found Fragment(s);
UF: User Fragment(s).
Competing interests
The Structure Elucidator software program discussed in
this publication is a commercial software product mar-
keted by Advanced Chemistry Development (ACD/Labs).
All authors are employees, ex-employees or collaborators
of ACD/Labs.
Authors' contributions
ME has been involved with the development of the Struc-
ture Elucidator software package for over a decade. AW
was the product manager for Structure Elucidator during
his employment with ACD/Labs and remains an active
collaborator. KB is the project leader for Structure Elucida-
tor. SM is the project leader for the structure generator
component of the software. YS worked on the genetic
algorithms associated with the stereochemical analysis.
TC worked on database development, testing and valida-
tion of the software. All authors read and approved the
final manuscript.
References
1. Gribov LA, Elyashberg ME, Karasev YZ: Quantitative molecular
analysis by infrared spectrometry without standard materi-
als. Anal Chim Acta 1995, 316:217-224.
2. Lederberg J, Sutherland GL, Buchanan BG, Feigenbaum EA, Robert-
son AV, Duffield AM, Djerassi C: Application of artificial intelli-
gence to chemical inference. I. The number of possible
The structure of brevetoxin BFigure 20
The structure of brevetoxin B. The configurations of stereocenters are determined by X-ray analysis.Page 24 of 26
(page number not for citation purposes)
"fully automated structure elucidation" there is still work
to do. Nevertheless, as the efficiency of expert systems is
organic compounds. Acyclic structures containing C, H, O
and N. J Am Chem Soc 1968, 91:2973.

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
3. Elyashberg ME, Gribov LA: Formal logic method of infrared
spectrum interpretation. Zh Prikl Spectrosk 1968, 8:296-300.
4. Sasaki SI, Abe H, Ouki T, Sakamoto M, Ochia SI: Automated struc-
ture elucidation of several kinds of aliphatic and alicyclic
compounds. Anal Chem 1968, 40:2220.
5. Nelson DB, Munk ME, Gasli KB, Horald DL: Alanylactinobicyclon.
An application of computer techniques to structure elucida-
tion. J Org Chem 1969, 34:3800.
6. Gribov LA, Elyashberg ME: Computer-aided identification of
organic molecules by their molecular spectra. Crit Rev Anal
Chem 1979, 8:111-220.
7. Hippe Z, Hippe R: Computer retrieval of spectral data. Appl
Spectrosc Rev 1980:135-186.
8. Elyashberg ME, Gribov LA, Serov VV: Molecular spectral analysis
and computer (in Russian). Moscow: Nauka; 1980.
9. Lindsay RK, Buchanan BG, Feigenbaum EA, Lederberg J: Applica-
tions of artificial intelligence for organic chemistry: The
DENDRAL Project. New York: McGraw-Hill; 1980.
10. Gray NAB: Artificial intelligence in chemistry. Anal Chim Acta
1980, 210:9-32.
11. Gray NAB: Computer-assisted structure elucidation. New
York: Wiley; 1986.
12. Elyashberg ME: Expert systems for analysis by molecular spec-
troscopy. J Anal Chem 1992, 47:698-709.
13. Elyashberg ME: Expert systems for the determination of struc-
tures of organic molecules by spectral methods. Russ Chem
Rev 1999, 68:525-547.
14. Elyashberg ME, Karasev YZ, Martirosian ER, Thiele H, Somberg H:
Expert systems as a tool for the molecular structure elucida-
tion by spectral methods. Strategies of solution to the prob-
lems. Anal Chim Acta 1997, 348:443-463.
15. Will M, Fachinger W, Richert JR: Fully automated structure elu-
cidation – a spectroscopist's dream comes true. J Chem Inf
Comput Sci 1996, 36:221-227.
16. Christie BD, Munk ME: The role of two-dimensional nuclear
magnetic resonance spectroscopy in computer-enhanced
structure elucidation. J Am Chem Soc 1991, 113:3750-3757.
17. Peng C, Yuan S, Zheng C, Hui Y: Efficient application of 2D NMR
correlation information in computer-assisted structure-elu-
cidation of complex natural products. J Chem Inf Comput Sci
1994, 34:805-813.
18. Steinbeck C: Lucy – a program for structure elucidation from
NMR correlation experiments. Angew Chem Int Ed Engl 1996,
35:1984-1986.
19. Nuzillard J-M, Massiot G: Logic for structure determination.
Tetrahedron 1991, 47:3655-3664.
20. Funatsu K, Sasaki S: Recent advances in the automated struc-
ture elucidation system, CHEMICS. Utilization of two-
dimensional NMR spectral information and development of
peripheral functions for examination of candidates. J Chem Inf
Comp Sci 1996, 36:190-204.
21. Lindel T, Junker J, Koeck M: 2D-NMR-guided constitutional anal-
ysis of organic compounds employing the computer pro-
gram COCON. Eur J Org Chem 1999:573-577.
22. Elyashberg ME, Williams AJ, Martin GE: Computer-assisted struc-
ture verification and elucidation tools in NMR-based struc-
ture elucidation. Prog NMR Spectrosc 2008, 53:1-104.
23. Blinov KA, Elyashberg ME, Molodtsov SG, Williams AJ, Martirosian
ER: An expert system for automated structure elucidation
utilizing 1H-1H, 13C-1H and 15N-1H 2D NMR correlations. Fre-
senius J Anal Chem 2001, 369:709-714.
24. Elyashberg M, Blinov K, Williams A, Molodtsov S, Martirosian E:
Application of a new expert system for the structure eluci-
dation of natural products from 1D and 2D NMR data. J Nat
Prod 2002, 65:693-703.
25. Blinov KA, Carlson DV, Elyashberg ME, Martin GE, Martirosian ER,
Molodtsov SG, Williams AJ: Computer-assisted structure eluci-
dation of natural products with limited 2D NMR data: appli-
cation of the StrucEluc system. Magn Reson Chem 2003,
41:359-372.
26. Elyashberg ME, Blinov KA, Martirosian ER, Molodtsov SG, Williams
AJ, Martin GE: Automated natural product structure elucida-
tion – the benefits of a symbiotic relationship between the
spectroscopist and the expert system. J Heterocyclic Chem 2003,
27. Elyashberg ME, Blinov KA, Molodtsov SG, Williams AJ, Martin GE:
Structure Elucidator: a versatile expert system for molecu-
lar structure elucidation from 1D and 2D NMR data and
molecular fragments. J Chem Inf Comput Sci 2004, 44:771-792.
28. Molodtsov SG, Elyashberg ME, Blinov KA, Williams AJ, Martin GM,
Lefebvre B: Structure elucidation from 2D NMR spectra using
the StrucEluc expert system: detection and removal of con-
tradictions in the data. J Chem Inf Comput Sci 2004, 44:1737-1751.
29. Elyashberg ME, Blinov KA, Williams AJ, Molodtsov SG, Martin GE:
Are deterministic expert systems for computer-assisted
structure elucidation obsolete? J Chem Inf Model 2006,
46:1643-1656.
30. Elyashberg ME, Blinov KA, Molodtsov SG, Williams AJ, Martin GE:
Fuzzy Structure Generation: a new efficient tool for compu-
ter-aided structure elucidation (CASE). J Chem Inf Model 2007,
47:1053-1066.
31. Shannon CE: A mathematical theory of communication. Bell
Syst Tech J 1948, 27:379-423.
32. Elyashberg ME, Martirosian ER, Karasev Yu Z, Thiele H, Somberg H:
An user-friendly expert system for the molecular structure
elucidation by spectral methods. Anal Chim Acta 1997,
337:265-285.
33. Berger S, Braun S: 200 and more NMR experiments: A practical
course. New York: Wiley; 2004.
34. Derome AE: Modern NMR techniques for chemistry research.
Oxford: Pergamon; 1987.
35. Elyashberg ME, Blinov KA, Martirosian ER: A new approach to
computer-aided molecular structure elucidation: the expert
system Structure Elucidator. Lab Automat Inf Manag 1999,
34:15-30.
36. Smurnyy YD, Blinov KA, Churanova TS, Elyashberg ME, Williams AJ:
Toward more reliable 13C and 1H chemical shift prediction:
a systematic comparison of neural-network and least-
squares regression based approaches. J Chem Inf Model 2008,
48:128-134.
37. Blinov KA, Smurnyy YD, Elyashberg ME, Churanova TS, Kvasha MP,
Steinbeck C, Lefebvre BA, Williams AJ: Performance validation of
neural network based 13C NMR prediction using a publicly
available data source. J Chem Inf Model 2008, 48:550-555.
38. Blinov KA, Smurnyy YD, Churanova TS, Elyashberg ME, Williams AJ:
Development of a fast and accurate method of 13C NMR
chemical shift prediction. Chemom Intell Lab Syst .
39. Neuhaus D, Williamson M: The nuclear Overhauser effect in
structural and conformational analysis. New York: Wiley;
2000.
40. Ge HM, Huang B, Tan SH, Shi DH, Song YC, Tan RX: Bioactive oli-
gostilbenoids from the stem bark of Hopea exalata. J Nat Prod
2006, 69:1800-1802.
41. Collins DO, Reynolds WF, Reese PB: New cembranes from
Cleome spinosa. J Nat Prod 2004, 67:179-183.
42. Subbaraju GV, Vanisree M, Rao CV, Sivaramakrishna C, Sridhar P, Jay-
prakasam B, Nair MG: Ashwagandhanolide, a bioactive dimeric
thiowithanolide isolated from the roots of withania somnif-
era. J Nat Prod 2006, 69:1790-1792.
43. Martin GE, Hadden BD, Russell CE, Kaluzny DJ, Guido JE, Duholke
WK, Stiemsma BA, Thamann TJ, Crouch RC, Blinov KA, Elyashberg
ME, Martirosian ER, Molodtsov SG, Williams AJ, Schiff PL Jr: Identi-
fication of degradants of a complex alkaloid using NMR cry-
oprobe technology and ACD/Structure Elucidator. J
Heterocyclic Chem 2002, 39:1241-1250.
44. Blinov KA, Elyashberg ME, Martirosian ER, Molodtsov SG, Williams
AJ, Sharaf MMH, Schiff PLJ, Crouch RC, Martin GE, Hadden CE, Guido
JE, Mills KA: Quindolinocryptotackieine: the elucidation of a
novel indoloquinoline alkaloid structure through the use of
computer-assisted structure elucidation and 2D NMR. Magn
Reson Chem 2003, 41:577-584.
45. Schlegel B, Härtl A, Dahse H-M, Gollmick FA, Gräfe U, Dörfelt H,
Kappes B: Hexacyclinol, a new antiproliferative metabolite of
Panus rudis HKI 0254. J Antibiot 2002, 55:814-817.
46. La Clair JJ: Total syntheses of hexacyclinol, 5-epi -hexacyclinol,
and desoxohexacyclinol, unveil an antimalarial prodrug
motif. Angew Chem Int Ed 2006, 45:2769-2773.
47. Rychnovsky SD: Predicting NMR spectra by computational
methods: Structure revision of hexacyclinol. Organic LettersPage 25 of 26
(page number not for citation purposes)
40:1017-1029. 2006, 8:2895-2898.

Journal of Cheminformatics 2009, 1:3 http://www.jcheminf.com/content/1/1/3
Open access provides opportunities to our
colleagues in other parts of the globe, by allowing
anyone to view the content free of charge.
Publish with ChemistryCentral and every
scientist can read your work free of charge
W. Jeffery Hurst, The Hershey Company.
available free of charge to the entire scientific community
peer reviewed and published immediately upon acceptance
cited in PubMed and archived on PubMed Central
yours you keep the copyright
48. Porco JAJ, Su S, Lei X, Bardhan S, Rychnovsky SD: Total synthesis
and structure assignment of (+)- hexacyclinol. Angew Chem Int
Ed 2006, 45:1-4.
49. Williams AJ, Elyashberg ME, Blinov KA, Lankin DC, Martin GE, Rey-
nolds WF, Porco JA, Singleton CA, Su S: Applying computer-
assisted structure elucidation algorithms for the purpose of
structure validation: revisiting the NMR assignments of hex-
acyclinol. J Nat Prod 2008, 71:581-588.
50. Bagno A, Rastrelli F, Saielli G: Toward the complete prediction
of the 1H and 13C NMR spectra of complex organic mole-
cules by DFT methods: application to natural substances.
Chemistry 2006, 12:5514-5525.
51. Balandina A, Saifina D, Mamedov V, Latypov S: Application of the-
oretically computed chemical shifts to structure determina-
tion of novel heterocyclic compounds. J Mol Struc 2006,
791:77-81.
52. Barone G, Gomez-Paloma L, Duca D, Silvestri A, Riccio R, Bifulco G:
Structure validation of natural products by quantum-
mechanical GIAO calculations of 13C NMR chemical shifts.
Chem Eur J 2002, 8:3233-3239.
53. Elyashberg ME, Blinov K, Williams AW: A systematic approach
for the generation and verification of structural hypotheses.
Magn Reson Chem .
54. Kim W-G, Kim J-W, Ryoo I-J, Kim J-P, Kim Y-H, Yoo I-D: Boletu-
nones A and B, highly functionalized novel sesquiterpenes
from boletus calopus. Org Lett 2004, 6:823-826.
55. Steglich W, Hellwig V: Revision of the structures assigned to the
fungal metabolites boletunones A and B. Org Lett 2004,
6:3175-3177.
56. Elyashberg ME, Blinov K, Williams AW: The application of empir-
ical methods of 13C NMR chemical shift prediction as a filter
for determining possible relative stereochemistry. Magn
Reson Chem 2009, 47(4):333-341.
57. Maloney KN, Fujita M, Eggert US, Schroeder FC, Field CM, Mitchison
TJ, Clardy J: Actin-Aggregating Cucurbitacins from Physocar-
pus capitatus. J Nat Prod 2008, 71:1927-1929.
58. Wüthrich K: NMR studies of structure and function of biolog-
ical macromolecules. Angew Chem Int Ed 2003, 42:3340-3363.
59. Smurnyy YD, Elyashberg ME, Blinov KA, Lefebvre B, Martin GE, Wil-
liams AJ: Computer-aided determination of relative stereo-
chemistry and 3D models of complex organic molecules
from 2D NMR spectra. Tetrahedron 2005, 61:9980-9989.
60. Mitchell M: An Introduction to Genetic Algorithms. Cam-
bridge, MA: The MIT Press; 1999. Page 26 of 26
(page number not for citation purposes)
Submit your manuscript here:
http://www.chemistrycentral.com/manuscript/