SAnDReS 2.0: Development of machine-learning models to explore the scoring function space.
J Comput Chem
; 45(27): 2333-2346, 2024 Oct 15.
Article
em En
| MEDLINE
| ID: mdl-38900052
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Aprendizado de Máquina
Idioma:
En
Revista:
J Comput Chem
Assunto da revista:
QUIMICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Brasil