The recent progress in proteochemometric modelling: Focusing on target descriptors, cross-term descriptors and application scope

Abstract

As an extension of the conventional quantitative structure activity relationshipmodels, proteochemometric (PCM)modelling is a computationalmethod that can predict the bioactivity relations betweenmultiple ligands andmultiple targets. Traditional PCMmodelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCMmodelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors andmachine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCMmodelling but also expanded its application scope fromtraditional protein-ligand interaction tomore abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-termdescriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.