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SAnDReS
Highlights
SAnDReS 2.0 (Statistical Analysis of Docking Results and Scoring functions) brings advanced computational tools for protein-ligand docking simulation and machine-learning modeling. We have AutoDock Vina (version 1.2.3) as a docking engine. Also, SAnDReS 2.0 has 54 regression methods implemented using Scikit-Learn, which allows us to explore the Scoring Function Space (SFS) concept. This exploration of the SFS permits us to have an adequate machine learning model for a targeted protein system. This approach creates computational models with superior predictive performance compared with classical scoring functions (also known as universal scoring functions). SAnDReS aims to merge the holistic view of systems biology with machine-learning methods to contribute to drug discovery projects. Dr. Walter F. de Azevedo Jr. proposed the initial idea of SAnDReS in 2016, which now has an international team of scientists participating in its development and testing.
Funding
The Brazilian National Council for Scientific and Technological Development (CNPq) (Process 306298/2022-8) supports this research project.