A Molecular Generative Model of COVID-19 Main Protease Inhibitors Using Long Short-Term Memory-Based Recurrent Neural Network

• Published in: Journal of computational biology Vol. 31; no. 1; pp. 83 - 98
• Main Authors: Mehrzadi, Arash, Rezaee, Elham, Gharaghani, Sajjad, Fakhar, Zeynab, Mirhosseini, Seyed Mohsen
• Format: Journal Article
• Language: English
• Published: United States Mary Ann Liebert, Inc., publishers 01-01-2024
• Subjects: COVID-19; Humans; Memory, Short-Term; Molecular Docking Simulation; Molecular Dynamics Simulation; Neural Networks, Computer; Protease Inhibitors - chemistry; Protease Inhibitors - pharmacology; Protease Inhibitors - therapeutic use; SARS-CoV-2; COVID-19; MD simulations; deep learning; molecular docking; recurrent neural network; main protease
• ISSN: 1557-8666; 1557-8666
• DOI: 10.1089/cmb.2023.0064

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a serious threat to public health and prompted researchers to find anti-coronavirus 2019 (COVID-19) compounds. In this study, the long short-term memory-based recurrent neural network was used to generate new inhibitors for the coronavirus. First, the model was trained to generate drug compounds in the form of valid simplified molecular-input line-entry system strings. Then, the structures of COVID-19 main protease inhibitors were applied to fine-tune the model. After fine-tuning, the network could generate new molecular structures as novel SARS-CoV-2 main protease inhibitors. Molecular docking exhibited that some generated compounds have the proper affinity to the active site of the protease. Molecular Dynamics simulations explored binding free energies of the compounds over simulation trajectories. In addition, in silico absorption, distribution, metabolism, and excretion studies showed that some novel compounds could be formulated as orally active agents. Based on molecular docking and molecular dynamics simulation studies, compound AADH possessed significant binding affinity and presumably inhibition against the SARS-CoV-2 main protease enzyme. Therefore, the proposed deep learning-based model was capable of generating promising anti-COVID-19 drugs.