Virtual Screening of Small Molecules Targeting BCL2 with Machine Learning, Molecular Docking, and MD Simulation.
Abtin TondarSergio Sánchez-HerreroAsim Kumar BepariAmir BahmaniLaura Calvet LiñánAntonio José Cañada-MartínezPublished in: Biomolecules (2024)
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.
Keyphrases
- molecular docking
- molecular dynamics
- neural network
- machine learning
- small molecule
- density functional theory
- molecular dynamics simulations
- protein protein
- climate change
- artificial intelligence
- high throughput
- drug delivery
- cancer therapy
- amino acid
- mass spectrometry
- human health
- risk assessment
- transcription factor
- virtual reality
- monte carlo