Frontiers Section Editor (Bioinformatics and Biophysics) for the Current Drug Targets ISSN: 1873-5592


Section Editor (Bioinformatics in Drug Design and Discovery) for the Current Medicinal Chemistry ISSN: 1875-533X


Section Editor (Combinatorial/Medicinal Chemistry) for the Combinatorial Chemistry & High Throughput Screening ISSN: 1875-5402


Member of the Editorial Board for the Current Bioinformatics ISSN: 2212-392X (Online) ISSN: 1574-8936 (Print)


Member of the Editorial Board for the Organic & Medicinal Chemistry International Journal ISSN: 2474-7610


Section Editor in Chief (Bioinformatics) for Bioengineering International. ISSN 2668-7119




Protein-Ligand Interacions


Molecular Docking


Bioinspired Computing


Computational Systems Biology

Prof. Walter F. de Azevedo, Jr. PhD 

Walter Filgueira De Azevedo Jr.
Walter F. de Azevedo Jr., PhD.


"Computational scientists solve tomorrow's problems with yesterday's computers; computer scientists seem to do it the other way around."--anonymous.   

Prof. Azevedo was named in the list of 100,000 most influential scientists in the world 2020. Prof. Azevedo is Frontiers Section Editor (Bioinformatics and Biophysics) of the Current Drug Targets, section editor (Bioinformatics in Drug Design and Discovery) of the Current Medicinal Chemistry, section editor (Combinatorial/Medicinal Chemistry) for the Combinatorial Chemistry & High Throughput Screening, member of the editorial board of Current Bioinformatics, and editor of Docking Screens for Drug Discovery (Methods of Molecular Biology)(Springer Nature). He is also member of the editorial board of PeerJ, PeerJ Physical Chemistry, Organic & Medicinal Chemistry International Journal, and section editor in chief (Bioinformatics) of the Bioengineering International. He graduated in Physics (BSc in Physics) from the University of São Paulo (USP) in 1990. He completed a Master Degree in Applied Physics also from the USP (1992), working under the supervision of Prof. Yvonne P. Mascarenhas, the founder of crystallography in Brazil. His dissertation was about X-ray crystallography applied to organometallics compounds (De Azevedo Jr. et al., 1995). During his PhD, he worked under the supervision of Prof. Sung-Hou Kim (University of California, Berkeley), on a split Ph.D. program with a fellowship from Brazilian Research Council (CNPq)(1993-1996). His PhD was about the crystallographic structure of CDK2 (De Azevedo Jr. et al., 1996). His current position is coordinator of the Structural Biochemistry Laboratory at Pontifical Catholic University of Rio Grande do Sul (PUCRS). His research interests are interdisciplinary with two major emphases: molecular simulations and protein-ligand interactions. He published over 190 scientific papers about protein structures and computer models to assess intermolecular interactions involving biomolecules and potential ligands (H-index: 42, RG Index > 42.0). These publications have over 5800 citations in the Web of Science (Publons h-index: 42), more than 5800 citations in the Scopus (h-index: 42), and over 7400 citations in the Google Scholar (h-index: 45). 

Published short biographies of Prof. Azevedo.



Research Interests 

           Prof. Walter F. de Azevedo Jr. and the clusters Pandora and LaBoheme, UNESP-2003. 

           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 (Xavier et al., 2016; Bitencourt-Ferreira & de Azevedo Jr., 2019) and Taba (da Silva et al., 2020). 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 was applied to study cyclin-dependent kinases to generate enzyme-targeted scoring functions for prediction of binding affinity (da Silva et al., 2020).

           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 (Heck et al., 2017; Bitencourt-Ferreira & de Azevedo Jr., 2019) 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 general 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.

             Prof. Walter F. de Azevedo Jr., PhD.    

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