Molecular Docking   

         

The computational prediction of the position of a given ligand into the binding pocket of a protein target is called protein-ligand molecular docking (Dias & De Azevedo, 2008; De Azevedo, 2010; Heberlé & Azevedo, 2011). Our focus here is on the application of optimized molecular docking strategies to identify potential new inhibitors for enzymes that are targets for drug development. We are interested in the discovery of new inhibitors for cyclin-dependent kinases (De Azevedo, 2017; Levin et al., 2017;  de Ávila et al., 2017), HIV-1 protease (Pintro & Azevedo, 2017), and several others proteins targets (Heck et al., 2017). We also seek the development of integrated strategies for molecular docking simulations (Xavier et al., 2016). These docking strategies use applications of bio-inspired computing to carry out molecular docking simulations (Heberlé & Azevedo, 2011). Furthermore, we are seeking the development of targeted-scoring function for the biological system we are interested in (Heck et al., 2017).  


References   

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 Azevedo WF Jr. MolDock applied to structure-based virtual screening. Curr Drug Targets. 2010; 11(3):327-34. PubMed   

De Azevedo Jr. WF. Opinion Paper: Targeting Multiple Cyclin-Dependent Kinases (CDKs): A New Strategy for Molecular Docking Studies. Curr Drug Targets. 2016;17(1):2.   PubMed  PDF  

Dias R, de Azevedo WF Jr. Molecular docking algorithms. Curr Drug Targets. 2008; 9(12):1040-7. PubMed 

Heberlé G, de Azevedo WF Jr. Bio-inspired algorithms applied to molecular docking simulations. Curr Med Chem. 2011; 18(9):1339-52. PubMed      

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 NM, Pintro VO, de Ávila MB, de Mattos BB, De Azevedo WF Jr. Understanding the Structural Basis for Inhibition of Cyclin-Dependent Kinases. New Pieces in the Molecular Puzzle. Curr Drug Targets. 2017; 18(9): 1104-1111.   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        

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): 801-12.   Link   PubMed   Go To SAnDReS   PDF