Boosted neural networks scoring functions for accurate ligand docking and ranking

Journal of Bioinformatics and Computational Biology
Hossam M Ashtawy, Nihar R Mahapatra

Abstract

Predicting the native poses of ligands correctly is one of the most important steps towards successful structure-based drug design. Binding affinities (BAs) estimated by traditional scoring functions (SFs) are typically used to score and rank-order poses to select the most promising conformation. This BA-based approach is widely applied and some success has been reported, but it is inconsistent and still far from perfect. The main reason for this is that SFs are trained on experimental BA values of only native poses found in co-crystallized structures of protein-ligand complexes (PLCs). However, during docking, they are needed to discriminate between native and decoy poses, a task for which they have not been specifically designed. To overcome this limitation, we propose to build task-specific SFs that model binding affinities (scoring task) as well as conformations (docking task) using the root mean square deviation (RMSD) of a ligand pose from the native pose. Our models are based on boosted ensembles of neural networks and other state-of-the-art machine learning (ML) algorithms in conjunction with multi-perspective interaction modeling techniques for accurate characterization of PLCs. We assess the docking and scoring/rankin...Continue Reading

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Citations

Mar 17, 2019·Interdisciplinary Sciences, Computational Life Sciences·Jin LiLe Zhang
Nov 16, 2019·Journal of Computer-aided Molecular Design·Edelmiro MomanVladimir A Potemkin
Sep 18, 2021·Chemical Biology & Drug Design·Mohammed BuleMohammad Abdollahi
Nov 16, 2019·Current Medicinal Chemistry·Gabriela Bitencourt-FerreiraWalter Filgueira de Azevedo

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