Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

Gabriela       Bitencourt-Ferreira; Camila       Rizzotto; Walter    Filgueira    de Azevedo Junior
doi:10.2174/0929867327666200515101820
Abstract

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs.
Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes.
Methods: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions.
Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions.
Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.
Keywords: Machine learning, SAnDReS, cyclin-dependent kinase, protein-ligand interactions, binding affinity, Gibbs free energy.
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DOI https://dx.doi.org/10.2174/0929867327666200515101820	Print ISSN 0929-8673
Publisher Name Bentham Science Publisher	Online ISSN 1875-533X
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