Potential Inhibitors of SARS-CoV-2 Developed through Machine Learning, Molecular Docking, and MD Simulation

Background: The advent of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 2019 (COVID-19) pandemic, has impacted physical and mental health worldwide. The lack of effective antiviral drugs necessitates a robust therapeutic approach to develop anti-SARS-CoV-2 drugs. Various investigations have recognized ACE2 as the primary receptor of SARS-CoV-2, and this amalgamation of ACE2 with the spike protein of the coronavirus is paramount for viral entry into the host cells and inducing infection. Consequently, restricting the virus';s accessibility to ACE2 offers an alternative therapeutic approach to averting this illness. Objective: The study aimed to identify potent inhibitors with enhanced affinity for the ACE2 protein and validate their stability and efficacy against established inhibitors via molecular docking, machine learning, and MD simulations. Methodology: 202 ACE2 inhibitors (PDB ID and 6LZG), comprising repurposed antiviral compounds and specific ACE2 inhibitors, were selected for molecular docking. The two most effective compounds obtained from docking were further analyzed using machine learning to identify potential compounds with enhanced ACE2-binding affinity. To refine the dataset, molecular decoys were generated through the Database of Useful Decoys: Enhanced (DUD-E) server, and Singular Value Decomposition (SVD) was applied for data preprocessing. The Tree-based Pipeline Optimization Tool (TPOT) was then utilized to optimize the machine learning pipeline. The most promising ML-predicted compounds were re-evaluated through docking and subjected to Molecular Dynamics (MD) simulations to evaluate their structural stability and interactions with ACE2. Finally, these compounds were evaluated against the top two pre-established inhibitors using various computational tools. Results: The two best pre-established inhibitors were identified as Birinapant and Elbasvir, while the best machine-learning-predicted compounds were PubChem ID: 23658468 and PubChem ID: 117637105. Pharmacophore studies were conducted on the most effective machine-learning-predicted compounds, followed by a comparative ADME/T analysis between the best ML-screened and pre-established inhibitors. The results indicated that the top ML compound (PubChem ID: 23658468) demonstrated favorable BBB permeability and a high HIA index, highlighting its potential for therapeutic applications. The ML-screened ligand demonstrated structural stability with an RMSD (0.24 nm) and greater global stability (Rg: 2.08 nm) than Birinapant. Hydrogen bonding interactions further validated their strong binding affinity. MM/PBSA analysis confirmed the ML-screened compound';s stronger binding affinity, with a binding free energy of - 132.90 kcal/mol, indicating enhanced stability in complex formation. Conclusion: The results emphasize the efficacy of integrating molecular docking, machine learning, and molecular dynamics simulations in facilitating the rapid identification of novel inhibitors. PubChem ID: 23658468 demonstrates robust binding affinity to ACE2 and favorable pharmacokinetic properties, establishing it as a promising candidate for further investigation.