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

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

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

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

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

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

 

Computational Systems Biology

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., 2017Pintro 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., 2016Heck et al., 2017). It allows us to computationally test different scenarios where a potential new drug may interact with a protein target (Bitencourt-Ferreira and de Azevedo, 2021). We developed the programs SAnDReS (Xavier et al., 2016; Bitencourt-Ferreira et al., 2021) and Taba (da Silva et al., 2020, Bitencourt-Ferreira et al., 2021) to create machine-learning models targeted to the biological system of interest. We have successfully employed SAnDReS to study coagulation factor Xa (EC 3.4.21.6) (Xavier et al., 2016), cyclin-dependent kinases (EC 2.7.11.22) (de Ávila et al., 2017; Levin et al., 2018), HIV-1 protease (EC 3.4.23.16) (Pintro and de Azevedo, 2017), estrogen receptor (Amaral et al., 2018), cannabinoid receptor 1 (Russo and de Azevedo, 2019; Russo and de Azevedo, 2020), and 3-dehydroquinate dehydratase (EC 4.2.1.10) (de Ávila and de Azevedo, 2018). Also, we used SAnDReS (Xavier et al., 2016) 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  

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   

Bitencourt-Ferreira G, da Silva AD, de Azevedo WF Jr. Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets. A Study of Cyclin-Dependent Kinase 2. Curr Med Chem. 2021; 28(2): 253–265.   PubMed   

Bitencourt-Ferreira G, Rizzotto C, de Azevedo Junior WF. Machine Learning-Based Scoring Functions. Development and Applications With SAnDReS. Curr Med Chem. 2021; 28(9): 1746–1746.   PubMed   

Bitencourt-Ferreira G, de Azevedo Junior WF. Electrostatic Potential Energy in Protein-Drug Complexes. Curr Med Chem. doi: 10.2174/0929867328666210201150842.   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   Go To Taba


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: 305–310.  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; 92: 1468–1474.   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): 2459–2470.   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; 20(9): 820–827.   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      


Russo S, de Azevedo WF Jr. Computational Analysis of Dipyrone Metabolite 4-Aminoantipyrine as a Cannabinoid Receptor 1 Agonist. Curr Med Chem. 2020; 27(28): 4741–4749.   PubMed  

Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL, Azevedo WF Jr. SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions. Comb Chem High Throughput Screen. 2016; 19(10): 801–812.   PubMed   Go To SAnDReS   PDF