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


Recent Publications


da Silva AD et al. J Comput Chem. 2020; 41(1): 69-73.


Volkart PA et al. Curr Drug Targets. 2019;20(7):716-726


Russo S, De Azevedo WF. Curr Med Chem 2019. 26(10):1908-1919

This is the website of the Computational Systems Biology research group of Prof. Walter F. de Azevedo Jr. 

We are a research group working on the development of computer models to assess intermolecular interactions involving biomolecules and potential ligands. We have been working on the creation of novel computational models for unraveling the molecular mechanisms underlying enzyme inhibition and protein-ligand interactions. We use these computational models to predict the binding affinity of a potential inhibitor for an enzyme; such knowledge has the potential to speed up drug discovery and decrease the cost of new drugs. Furthermore, the availability of computational models to predict binding affinity based on the atomic coordinates of protein-ligand complexes adds flexibility to the process of drug discovery, since it allows us to computationally test different scenarios where a potential new drug may interact with a protein target.

Exploring the Scoring Function Space [>>]

We envisage protein-ligand interaction as a result of the relation between the protein space (Smith, 1970) and the chemical space (Bohacek et al., 1996; Dobson, 2004Kirkpatrick & Ellis, 2004; Lipinski & Hopkins, 2004Shoichet, 2004; Stockwell, 2004), and we propose to approach these sets as a unique complex system, where the application of computational methodologies could contribute to establishing the physical principles to understand the structural basis for the specificity of ligands for proteins. Such approaches have the potential to create novel semi-empirical free energy scoring function to predict binding affinity with superior predictive power when compared with standard methodologies. We propose to use the abstraction of a mathematical space composed of infinite computational models to predict ligand-binding affinity, named here as scoring function space (Heck et al., 2017; Bitencourt-Ferreira & de Azevedo Jr., 2019). By the use of supervised machine learning techniques, we can explore this scoring function space to build a computational model targeted to a specific biological system. For instance, we created targeted-scoring functions for HIV-1 Protease and Cyclin-Dependent Kinases. We developed the programs SAnDReS, SFSXplorer, and Taba (da Silva et al., 2020to generate computational models to predict ligand-binding affinity. SAnDReS, SFSXplorer, and Taba are integrated computational tools to explore the scoring function space.


A view of the scoring function space as a way to develop a computational model to predict ligand-binding affinity. Structures of proteins available with the following PDB codes: 2OW4, 2OVU, 2IDZ, 2GSJ, 2G85, 2A4L1ZTB, 1Z99, 1WE2, 1M73, 1FLH, and 1FHJ.

Research [>>]

The availability of more than 150,000 experimentally determined macromolecule structures paved the way to investigate computer models to predict three-dimensional structures of proteins and to simulate protein-ligand interactions. Furthermore, integrating protein structures and binding affinity information creates a favorable environment for the development of specific scoring functions to predict ligand-binding affinity.  We are working on the development of computational tools to analyze protein-ligand interactions.  Our research interests are interdisciplinary with five major emphases:   

Additionally, we have interests in following projects: electrostatics potential energy function for protein-ligand complexes, development of quantum mechanics models to address protein-ligand interactions, development of hybrid computational models involving quantum computing and artificial intelligence, creation of new computational models to simulate action potentials in neurons, creation of targeted  scoring functions, and development of deep learning applications to predict the behavior of complex systems.

If you have interest in the above described projects, please feel free to contact Prof. Walter F. de Azevedo Jr. e-mail:


Pontifical Catholic University of Rio Grande do Sul, PUCRS.

Ipiranga Avenue, 6681 Partenon - Porto Alegre/RS. Brazil. Zip Code: 90619-900.

This site was designed by Dr. Walter F. de Azevedo Jr.