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Walter Filgueira de Azevedo Junior

  • PhD
  • Professor
  • Pontifical Catholic University of Rio Grande do Sul - PUCRS
  • 35h-indexImpact measure calculated using publication and citation counts. Updated daily.
  • 4562CitationsNumber of citations received by Walter's publications. Updated daily.


Current Medicinal Chemistry

Section Editor (Bioinformatics in Drug Design and Discovery)

2017 - Present

Current Bioinformatics

Member of the Editorial Board

2017 - Present

Recent publications

  • Cyclin-Dependent Kinase 2 in Cellular Senescence and Cancer. A Structural and Functional Review.

    • Volkart P
    • Bitencourt-Ferreira G
    • Souto A
    • et al.
    Get full text
  • Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity

    • Heck G
    • Pintro V
    • Pereira R
    • et al.
    Get full text

Professional experience


Pontifical Catholic University of Rio Grande do Sul - PUCRS

August 2005 - Present



University of São Paulo

October 1992 - April 1997(5 years)

Research interests

Computational systems biologyBioinspired computingMolecular dockingMolecular simulationsProtein-ligand interactions


I focus my research on the protein-ligand interaction problem. As a scientific problem, we may address it from different perspectives. In the later 1990s and early 2000s, my goal was on the application of experimental techniques such as X-ray diffraction crystallography and nuclear magnetic resonance (NMR) to determine the three-dimensional structures of biological macromolecules and investigate their interactions with potential binders. In recent years, I have moved forward to simulate the interactions using molecular docking and creating machine-learning models to assess binding affinity using the atomic coordinates of protein-ligand complexes. My focus is now on the development of programs to evaluate protein-ligand interactions. The most recent progress was the creation of the programs SAnDReS and Taba. SAnDReS is a suite of computational tools to carry out docking simulations and to generate machine-learning models to predict binding affinity. The program Taba was developed to create machine-learning models based on an ensemble of protein structures for complexes involving protein and ligand. Taba considers that we may approach the protein-ligand problem as a spring-mass system, where we approximate the binding affinity using a pseudo-energy equation similar to the equation to calculate the potential energy of a spring-mass system. I successfully applied SAnDReS to study cyclin-dependent kinase, HIV-1 protease, and estrogen receptor. Taba is in the final stage of development, and I use it to study protein targets to generate enzyme-targeted scoring functions for prediction of binding affinity. I envisage the problem of protein-ligand interaction as a result of the relation between the protein sequence space and the chemical space, and I propose to approach these sets as a unique complex system, where the application of computational methodologies could contribute to the creation of specific scoring functions to predict binding affinity. To establish a robust mathematical framework to address this problem, I developed the concept of scoring function space that I use to find an adequate model to predict binding affinity for a biological system of interest. I designed the program SAnDReS as a tool to explore this scoring function space, where through machine learning techniques I build new models to predict binding affinity. One way to think about this approach is considering the experimental binding data and the structures available for a given protein as a system, where through the application of machine learning techniques I generate a computational model tailored to this biological system. In doing so, I give up to find a fit-all-model for all proteins; I address this problem creating a fine-tuned model, that seems reasonable considering the limited amount of experimental data, especially considering the complex structures for which experimental binding affinity is available. Also, I expect that as being proteins subject the evolution and being inserted in a complex chemical environment, as found in the biological systems, the application of a targeted machine-learning model is adequate to predict binding affinity.

Co-authors (262)

  • Nelson da Silveira

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