Books
Docking Screens for Drug Discovery. Editor: de Azevedo Jr., Walter Filgueira (Ed.)
Projects
SAnDReS: Statistical Analysis of Docking Results and Scoring functions
Taba: A Tool to Analyze the Binding Affinity
Citation
Editorships
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 and 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 and Medicinal Chemistry International Journal ISSN: 2474-7610
Section Editor in Chief (Bioinformatics) for Bioengineering International. ISSN 2668-7119
Dr. Walter F. de Azevedo, Jr. has been ranked among the most influential researchers in the world according to a database created by Journal Plos Biology (see news here). Dr. Azevedo`s influential works can be found here. Citation metrics available here.
News
Posted on September 10, 2021
Our first work about Covid-19 published in Current Medicinal Chemistry.
De Azevedo Junior WF, Bitencourt-Ferreira G, Godoy JR, Adriano HMA, Dos Santos Bezerra WA, Dos Santos Soares AM. Protein-ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2. Curr Med Chem. doi: 10.2174/0929867328666210329094111. PubMed
Exploring the Scoring Function Space [>>]
We envisage protein-ligand interaction as a result of the relation between the protein space (Smith, 1970; Hou et al., 2005) and the chemical space (Bohacek et al., 1996; Dobson, 2004; Kirkpatrick & Ellis, 2004; Lipinski & Hopkins, 2004; Shoichet, 2004; Stockwell, 2004), and we propose to approach these sets as a unique complex system, where the application of computational methodologies could contribute to understand the structural basis for the specificity of ligands for proteins. Such approaches have the potential to create novel scoring functions 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 (Xavier et al., 2016), SFSXplorer, and Taba (da Silva et al., 2020) to generate computational models to predict ligand-binding affinity. These programs 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.