SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space
Year: 2024
Type: article
Abstract: 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 KDEEP, CSM‐lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres .
DOI: 10.1002/jcc.27449
Institutions: Universidade Federal de Alfenas, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Córdoba, Pontifícia Universidade Católica do Rio Grande do Sul, Universidade Federal de Ciências da Saúde de Porto Alegre, Western Michigan University, Universidade de São Paulo, University of Palermo, Institute of Biomedical Chemistry, Centre National de la Recherche Scientifique, Matrice Extracellulaire et Dynamique Cellulaire MEDyC, Université de Reims Champagne-Ardenne
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4399871844