De Novo design of potential inhibitors against SARS-CoV-2 Mpro. (August 2022)

• Record Type: Journal Article
• Authors: Li, Shimeng; Wang, Lianxin; Meng, Jinhui; Zhao, Qi; Zhang, Li; Liu, Hongsheng
• Is Part Of: Computers in biology and medicine. Volume 147(2022)
• Publication Date: 2022-08
• Subjects: de novo drug design -- Transfer learning -- Virtual screening -- Molecular dynamics simulation -- Deep learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105728
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The impact of the ravages of COVID-19 on people's lives is obvious, and the development of novel potential inhibitors against SARS-CoV-2 main protease (Mpro), which has been validated as a potential target for drug design, is urgently needed. This study developed a model named MproI-GEN, which can be used for the de novo design of potential Mpro inhibitors (MproIs) based on deep learning. The model was mainly composed of long-short term memory modules, and the last layer was re-trained with transfer learning. The validity (0.9248), novelty (0.9668), and uniqueness (0.0652) of the designed potential MproI library (PMproIL) were evaluated, and the results showed that MproI-GEN could be used to design structurally novel and reasonable molecules. Additionally, PMproIL was filtered based on machine learning models and molecular docking. After filtering, the potential MproIs were verified with molecular dynamics simulations to evaluate the binding stability levels of these MproIs and SARS-CoV-2 Mpro, thereby illustrating the inhibitory effects of the potential MproIs against Mpro. Two potential MproIs were proposed in this study. This study provides not only new possibilities for the development of COVID-19 drugs but also a complete pipeline for the discovery of novel lead compounds. Highlights: A character-level small molecules design model for MproI design is proposed. The potential MproI libraries were filtered by Machine Learning models and molecular docking. Two potential MproIs were designed with the MproI-GEN and validated with molecular dynamics simulation.