Discovery of novel ULK1 inhibitors through machine learning-guided virtual screening and biological evaluation.

Autor: Kong MM; The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.; Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision & Brain Health), Wenzhou, Zhejiang, 325000, China., Wei T; School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China., Liu B; Faculty of Applied Sciences, Macao Polytechnic University, Macao, SAR, China., Xi ZX; The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China., Ding JT; The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China., Liu X; School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China., Li K; The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China., Qin TL; The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China., Qian ZY; The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China., Wu WC; The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China., Wu JZ; Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision & Brain Health), Wenzhou, Zhejiang, 325000, China.; The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China., Li WL; The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
Jazyk: angličtina
Zdroj: Future medicinal chemistry [Future Med Chem] 2024; Vol. 16 (18), pp. 1821-1837. Date of Electronic Publication: 2024 Aug 15.
DOI: 10.1080/17568919.2024.2385288
Abstrakt: 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 IC 50 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.
Databáze: MEDLINE
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