Fiche publication
Date publication
juin 2024
Journal
Journal of computational chemistry
Auteurs
Membres identifiés du Cancéropôle Est :
Pr BAUD Stéphanie
Tous les auteurs :
de Azevedo WF, Quiroga R, Villarreal MA, da Silveira NJF, Bitencourt-Ferreira G, da Silva AD, Veit-Acosta M, Oliveira PR, Tutone M, Biziukova N, Poroikov V, Tarasova O, Baud S
Lien Pubmed
Résumé
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 ΔRF. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.
Mots clés
binding affinity, crystal structure, machine learning, protein–ligand interactions, scoring function space
Référence
J Comput Chem. 2024 06 20;:
