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\n[https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27449](https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27449)  \nSAnDR 1096-987XeS 2.0: Development of machine-learning models to explore the scoring function space  \nWalter Filgueira de Azevedo Jr  1, Rodrigo Quiroga 2, Marcos Ariel Villarreal 2, Nelson José Freitas da Silveira 3, Gabriela Bitencourt-Ferreira 4, Amauri Duarte da Silva 5, Martina Veit-Acosta 6,  \nPatricia Rufino Oliveira 7, Marco Tutone 8, Nadezhda Biziukova 9, Vladimir Poroikov 9, Olga Tarasova 9, Stéphaine Baud  10  \nAffiliations  \n• 1Department of Physics, Institute of Exact Sciences, Federal University of Alfenas, Alfenas, Brazil.  \n• 2Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), CONICET-Departamento de Química Teórica y Computacional, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina.  \n• 3Laboratory of Molecular Modeling and Computer Simulation, Federal University of Alfenas, Alfenas, Brazil.  \n• 4Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil.  \n• 5Programa de Pós-Graduação em Tecnologias da Informação e Gestão em Saúde, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil.  \n• 6Western Michigan University, Kalamazoo, Michigan, USA.  \n• 7School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, Brazil.  \n• 8Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Palermo, Italy.  \n• 9Institute of Biomedical Chemistry, Moscow, Russia.  \n• 10Laboratoire SiRMa, UMR CNRS/URCA 7369, UFR Sciences Exactes et Naturelles, Université de Reims Champagne-Ardenne, CNRS, MEDYC, Reims, France.  \nAbstract  \nClassical 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  \nexploration 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, SAnDReSgenerated models showed predictive performance close to or better than other machinelearning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at [https://github.com/azevedolab/sandres](https://github.com/azevedolab/sandres).  \nKeywords: binding affinity; crystal structure; machine learning; protein–ligand interactions; scoring function space.  \n© 2024 Wiley Periodicals LLC.  \nEste Artículo se encuentra embargado hasta el día 21 de junio 2025  \nEn el siguiente enlace [https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27449](https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27449 se encuentra)[ se encuentra](https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27449 se encuentra)[ ](https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27449 se encuentra)[disponible:](disponible:)  \n“Supporting Information”  \nSupplementary Material S1 . Features used to build machine-learning models.  \nSupplementary Material S2 . Regression methods.  \nSupplementary Material S3 . Scoring functions[a] .  \nSupplementary material S4 . Docking results for CDK2 structures with inhibitors bound to the ATP-binding pocket.  \nSupplementary Material S5 . Predictive performance for poses of the test set (case study 1) .  \nSupplementary Material S6 . Predictive performance for docked pose structures of the test set (case s","cbCaieS3zIO6Q3pG","https://ap.wps.com/l/cbCaieS3zIO6Q3pG","pdf",150012,"English","# Abstract\n## Methodology and inputs\n## Proof of concept and case studies\n## Software availability\n## Keywords and supplementary materials","[{\"question\":\"What problem does SAnDReS address in ligand binding affinity prediction?\",\"answer\":\"SAnDReS targets the limited accuracy of classical scoring functions when determining ligand binding affinity for proteins.\"},{\"question\":\"How does SAnDReS 2.0 compute binding affinity?\",\"answer\":\"It combines AutoDock Vina 1.2 with 54 regression methods from Scikit-Learn to calculate binding affinity from protein–ligand structures.\"},{\"question\":\"Which types of structures are used to build and evaluate the machine-learning models?\",\"answer\":\"Models are generated from crystal structures, docked structures, and AlphaFold-generated structures, then tested in three case studies.\"}]","SAnDReS 2.0 - Development of machine-learning models to explore the scoring function space | PDF"]