SAnDReS 2.0: Development of machine-learning models to explore the scoring function space

Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance....

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Veröffentlicht in:Journal of computational chemistry 2024-10, Vol.45 (27), p.2333-2346
Hauptverfasser: de Azevedo, Jr, Walter Filgueira, Quiroga, Rodrigo, Villarreal, Marcos Ariel, da Silveira, Nelson José Freitas, Bitencourt-Ferreira, Gabriela, da Silva, Amauri Duarte, Veit-Acosta, Martina, Oliveira, Patricia Rufino, Tutone, Marco, Biziukova, Nadezhda, Poroikov, Vladimir, Tarasova, Olga, Baud, Stéphaine
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Sprache:eng
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Zusammenfassung:Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as K , CSM-lig, and Δ RF . SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.
ISSN:0192-8651
1096-987X
1096-987X
DOI:10.1002/jcc.27449