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

• Published in: Journal of computational chemistry Vol. 45; no. 27; pp. 2333 - 2346
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 15.10.2024
• Subjects: Affinity; Availability; Binding; Function space; Ligands; Machine learning; Performance prediction; Proteins; binding affinity; crystal structure; protein–ligand interactions; machine learning; scoring function space
• ISSN: 0192-8651; 1096-987X; 1096-987X
• DOI: 10.1002/jcc.27449

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.