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 & 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
We have been working on the development of computational models for unraveling the molecular mechanisms underlying enzyme inhibition and protein-ligand interactions (Bitencourt-Ferreira and de Azevedo, 2019a; 2019b; 2019c; da Silva et al., 2020). We can 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 development of new drugs (de Ávila et al., 2017, Pintro and Azevedo, 2017). 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 (Xavier et al., 2016, Heck et al., 2017). It allows us to computationally test different scenarios where a potential new drug may interact with a protein target. We developed the programs SAnDReS (Xavier et al., 2016) and Taba (da Silva et al., 2020) to create machine-learning models targeted to the biological system of interest. We have successfully employed SAnDReS to study coagulation factor Xa (Xavier et al., 2016), cyclin-dependent kinases (de Ávila et al., 2017; Levin et al., 2018), HIV-1 protease (Pintro and de Azevedo, 2017), estrogen receptor (Amaral et al., 2018), cannabinoid receptor 1 (Russo and de Azevedo, 2019), and 3-dehydroquinate dehydratase (de Ávila and de Azevedo, 2018). Also, we used SAnDReS to develop a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes (Bitencourt-Ferreira and de Azevedo Jr., 2018).
References
Amaral MEA, Nery LR, Leite CE, de Azevedo Junior WF, Campos MM. Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Invest New Drugs. 2018; 36(5): 782–796. PubMed PDF
Bitencourt-Ferreira G, de Azevedo Jr. WF. Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys Chem. 2018; 240: 63–69. PubMed PDF
Bitencourt-Ferreira G, de Azevedo WF Jr. Machine Learning to Predict Binding Affinity. Methods Mol Biol. 2019a; 2053: 251–273. PubMed
Bitencourt-Ferreira G, de Azevedo WF Jr. Exploring the Scoring Function Space. Methods Mol Biol. 2019b; 2053: 275–281. PubMed
Bitencourt-Ferreira G, de Azevedo WF Jr. How Docking Programs Work. Methods Mol Biol. 2019c; 2053: 35–50. PubMed
da Silva AD, Bitencourt-Ferreira G, de Azevedo WF Jr. Taba: A Tool to Analyze the Binding Affinity. J Comput Chem. 2020; 41(1): 69–73. PubMed
de Ávila MB, Xavier MM, Pintro VO, de Azevedo WF. Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. Biochem Biophys Res Commun. 2017; 494: 305-10. PubMed PDF
de Ávila MB, de Azevedo WF Jr. Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem Biol Drug Des. 2018. doi: 10.1111/cbdd.13312. PubMed PDF
Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF. Supervised Machine Learning Methods Applied to Predict Ligand-Binding Affinity. Curr Med Chem. 2017; 24(23): 2459-70. PubMed PDF
Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8. PubMed PDF
Pintro VO, Azevedo WF. Optimized Virtual Screening Workflow. Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease. Comb Chem High Throughput Screen. 2017. doi: 10.2174/1386207320666171121110019. PubMed
Russo S, De Azevedo WF. Advances in the Understanding of the Cannabinoid Receptor 1 - Focusing on the Inverse Agonists Interactions. Curr Med Chem. 2019; 26(10): 1908–1919. PubMed PDF