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De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations

Arshia, Amir Hossein; Shadravan, Shayan; Solhjoo, Aida; Sakhteman, Amirhossein; Sami, Ashkan


Amir Hossein Arshia

Shayan Shadravan

Aida Solhjoo

Amirhossein Sakhteman

Ashkan Sami


The main protease of SARS-CoV-2 is a critical target for the design and development of antiviral drugs. 2.5 M compounds were used in this study to train an LSTM generative network via transfer learning in order to identify the four best candidates capable of inhibiting the main proteases in SARS-CoV-2. The network was fine-tuned over ten generations, with each generation resulting in higher binding affinity scores. The binding affinities and interactions between the selected candidates and the SARS-CoV-2 main protease are predicted using a molecular docking simulation using AutoDock Vina. The compounds selected have a strong interaction with the key MET 165 and Cys145 residues. Molecular dynamics (MD) simulations were run for 150ns to validate the docking results on the top four ligands. Additionally, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and hydrogen bond analysis strongly support these findings. Furthermore, the MM-PBSA free energy calculations revealed that these chemical molecules have stable and favorable energies, resulting in a strong binding with Mpro's binding site. This study's extensive computational and statistical analyses indicate that the selected candidates may be used as potential inhibitors against the SARS-CoV-2 in-silico environment. However, additional in-vitro, in-vivo, and clinical trials are required to demonstrate their true efficacy.

Journal Article Type Article
Acceptance Date Oct 19, 2021
Online Publication Date Nov 2, 2021
Publication Date 2021-12
Deposit Date Dec 4, 2022
Journal Computers in Biology and Medicine
Print ISSN 0010-4825
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 139
Article Number 104967
Keywords SARS-CoV-2, Deep learning, Main protease, Molecular docking, Molecular dynamic simulation, Covid-19
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