Current Drug Targets

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

Bentham Link

Current Medicinal Chemistry

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

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Journal of Molecular Structure

Member of the Editorial Board of the Journal of Molecular Structure ISSN: 0022-2860

Journal of Molecular Structure Link

Molecular Diversity

Member of the Editorial Board of Molecular Diversity ISSN: 1381-1991 (Print) 1573-501X (Online)

Molecular Diversity Link

Exploration of Drug Science

Associate Editor for Exploration of Drug Science

Exploration of Drug Science Link

Frontiers in Chemistry

Reviewer Editor for Frontiers in Chemistry ISSN: 2296-2646

Frontiers in Chemistry Link

Organic and Medicinal Chemistry International Journal

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

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Walter Filgueira De Azevedo Jr.



Dr. Walter F. de Azevedo, Jr. earned a BSc in Physics (1990), an MSc in Applied Physics (1992), and a DSc in Applied Physics (1997) from the University of São Paulo (Brazil). In his doctoral studies, Dr. Azevedo worked under the supervision of Prof. Yvonne Primerano Mascarenhas (University of São Paulo) and Prof. Sung-Hou Kim (University of California, Berkeley) on a split Doctoral program with a fellowship from the Brazilian Research Council (CNPq). During his first two years at Berkeley, he was under a CNPq fellowship (1993-95). Due to his performance, Prof. S.-H. Kim hired him as Visiting Researcher for the Department of Chemistry, University of California at Berkeley (1995-96). The work developed during these three years at Berkeley resulted in his thesis about the structure of Cyclin-Dependent Kinase 2 (CDK2) in complex with inhibitors (PDB access code: 2A4L) (de Azevedo et al., 1996; de Azevedo et al., 1997). Dr. Azevedo is the first author of both papers, and these publications gathered more than 1,000 citations on the Web of Science.

During 1997-98 he had a postdoc position at São Paulo State University (Unesp) with a Fapesp fellowship. He holds a habilitation degree in Physics (livre-docência) from the São Paulo State University (Unesp)(2004). 

In 1998, Dr. Azevedo participated in a research project with NASA that sent proteins to crystallize in a microgravity environment onboard the Space Shuttle Discovery (STS-95). This research had coverage of Brazilian TV networks. He published a book entitled "Docking Screens for Drug Discovery" with Springer Nature in 2019. This book sold 46,000 copies (April 2024) with over 2 million dollars in sales ( In 2020, the Journal Plos Biology ranked Dr. Azevedo among the most influential researchers in the world (Fields: Biochemistry & Molecular Biology and Biophysics).

Dr. Azevedo has vast editorial experience. He is the frontiers section editor (Bioinformatics/Biophysics) for the Current Drug Targets, section editor (Bioinformatics in Drug Design and Discovery) for the Current Medicinal Chemistry, review editor for Frontiers in Chemistry, associate editor for Exploration of Drug Science, member of the editorial boards Molecular Diversity and the Journal of Molecular Structures, and editor of Docking Screens for Drug Discovery (Methods of Molecular Biology)-Springer Nature. He is a reviewer for over 60 high-impact journals, including Nature Communications and Briefings in Bioinformatics.

His research interests are interdisciplinary, with three main emphases: machine learning, complex systems, and computational systems biology. Dr. Azevedo developed several free software to explore the concept of Scoring Function Space. He has over 200 scientific publications about protein structures, computer models of complex systems, and simulations of protein systems. These workers have over 7000 citations on the Web of Science (h-index: 48. m-quotient: 1.7), +7000 citations in Scopus (h-index: 48), and +9000 citations on Google Scholar (h-index: 53).

Research Interests 

           Dr. 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.

             Dr. Walter F. de Azevedo, Jr.