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. These computational models can be used to predict 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 & 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, since it allows us to computationally test different scenarios where a potential new drug may interact with a protein target (Xavier et al., 2016Heck et al., 2017). We developed a tool to create computational models to predict binding affinity, named SAnDReS  (Xavier et al., 2016).   


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     

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    

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