1.
Biomolecules
; 14(5)2024 May 01.
Artículo
en Inglés
| MEDLINE
| ID: mdl-38785951
RESUMEN
This study aimed to identify potential BCL-2 small molecule inhibitors using deep neural networks (DNN) and random forest (RF), algorithms as well as molecular docking and molecular dynamics (MD) simulations to screen a library of small molecules. The RF model classified 61% (2355/3867) of molecules as 'Active'. Further analysis through molecular docking with Vina identified CHEMBL3940231, CHEMBL3938023, and CHEMBL3947358 as top-scored small molecules with docking scores of -11, -10.9, and 10.8 kcal/mol, respectively. MD simulations validated these compounds' stability and binding affinity to the BCL2 protein.