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Insilico generation of novel ligands for the inhibition of SARS-CoV-2 main protease (3CLpro) using deep learning

Authors :
Prejwal Prabhakaran
Ananda Vardhan Hebbani
Soumya V. Menon
Biswaranjan Paital
Sneha Murmu
Sunil Kumar
Mahender Kumar Singh
Dipak Kumar Sahoo
Padma Priya Dharmavaram Desai
Source :
Frontiers in Microbiology, Vol 14 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

The recent emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing the coronavirus disease (COVID-19) has become a global public health crisis, and a crucial need exists for rapid identification and development of novel therapeutic interventions. In this study, a recurrent neural network (RNN) is trained and optimized to produce novel ligands that could serve as potential inhibitors to the SARS-CoV-2 viral protease: 3 chymotrypsin-like protease (3CLpro). Structure-based virtual screening was performed through molecular docking, ADMET profiling, and predictions of various molecular properties were done to evaluate the toxicity and drug-likeness of the generated novel ligands. The properties of the generated ligands were also compared with current drugs under various phases of clinical trials to assess the efficacy of the novel ligands. Twenty novel ligands were selected that exhibited good drug-likeness properties, with most ligands conforming to Lipinski’s rule of 5, high binding affinity (highest binding affinity: −9.4 kcal/mol), and promising ADMET profile. Additionally, the generated ligands complexed with 3CLpro were found to be stable based on the results of molecular dynamics simulation studies conducted over a 100 ns period. Overall, the findings offer a promising avenue for the rapid identification and development of effective therapeutic interventions to treat COVID-19.

Details

Language :
English
ISSN :
1664302X
Volume :
14
Database :
Directory of Open Access Journals
Journal :
Frontiers in Microbiology
Publication Type :
Academic Journal
Accession number :
edsdoj.0f47e2c82f42478cbb173b8178c4bb1c
Document Type :
article
Full Text :
https://doi.org/10.3389/fmicb.2023.1194794