Discovery of novel ULK1 inhibitors through machine learning-guided virtual screening and biological evaluation.
Future Med Chem
; : 1-17, 2024 Aug 15.
Article
en En
| MEDLINE
| ID: mdl-39145469
ABSTRACT
Aim:
Build a virtual screening model for ULK1 inhibitors based on artificial intelligence. Materials &methods:
Build machine learning and deep learning classification models and combine molecular docking and biological evaluation to screen ULK1 inhibitors from 13 million compounds. And molecular dynamics was used to explore the binding mechanism of active compounds. Results &conclusion:
Possibly due to less available training data, machine learning models significantly outperform deep learning models. Among them, the Naive Bayes model has the best performance. Through virtual screening, we obtained three inhibitors with IC50 of µM level and they all bind well to ULK1. This study provides an efficient virtual screening model and three promising compounds for the study of ULK1 inhibitors.
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Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Future Med Chem
Año:
2024
Tipo del documento:
Article
País de afiliación:
China