Books

 

 

 

Projects

 

 

 

Citation

 

Editorships

Avatar

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

Avatar

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

Avatar

Member of the Editorial Board for the PeerJ ISSN: 2167-8359

Avatar

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

 

Research

Avatar

Protein-Ligand Interacions

Avatar

Molecular Docking

Avatar

Bioinspired Computing

Avatar

Computational Systems Biology

 

Recent Publications

Avatar

da Silva AD et al. J Comput Chem. 2020; 41(1): 69-73.

Avatar

Volkart PA et al. Curr Drug Targets. 2019;20(7):716-726

Avatar

Russo S, De Azevedo WF. Curr Med Chem 2019. 26(10):1908-1919

Bio-inspired Computing 

Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. Algorithms that mimic biological systems can create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. Biologically inspired algorithms (BIA) comprise a class of stochastic optimization and adaptation methodologies, designed for global optimization. One of the most promising biologically inspired algorithms is the evolutionary algorithm. The great majority of evolutionary algorithms extracts inspiration from the process of genetic evolution. In Darwinian evolution, species selection is based on their capacity for survival of the fittest in an ecosystem. Under this view, classes of evolutionary algorithms, known as genetic algorithms, genetic programming, and evolutionary programming, have been developed. All classes of evolutionary algorithms share a large number of characteristics (Heberlé & De Azevedo, 2011). We have been working in the application and development of bio-inspired computation to assess the problem of protein-ligand interactions (Xavier et al., 2016Heck et al., 2017de Ávila et al., 2017Pintro & Azevedo, 2017).  

Schematic representation of the main operators present in a genetic algorithm. The Python code related to this figure is available here.

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: 305–310.  PubMed   PDF     

Heberlé G, de Azevedo WF Jr. Bio-inspired algorithms applied to molecular docking simulations. Curr Med Chem. 2011; 18(9):1339–1352.   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): 2459–2470.   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     

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.   Link   PubMed   Go To SAnDReS   PDF