Designing highly efficient heterocyclic inhibitors for SARS-CoV-2 3C-like proteinase: a comprehensive in silico study

Abstract

To address the limitations of current COVID-19 treatments, we conducted an integrated in-silico investigation to design potential drugs with proven efficacy against the virus. We developed Quantitative Structure-Activity Relationship (QSAR) models using a database of 63 Aromatic heterocyclic compounds, focusing on key parameters Effective Diameter (ED) and Diameter Maximum (DM). Our models, utilizing multi-linear regression (MLR) and Artificial Neural Network (ANN), were validated according to OECD principles and successfully used to predict unexplored aromatic heterocyclic compounds with Pyridine Cores. Compound 4 (Dexbrompheniramine) exhibited high inhibition against the SARS coronavirus 3 C-like protease, leading to the design of two new molecules (compounds 15 and 16) with enhanced activity based on structural enhancements from the QSAR model. Docking studies and molecular dynamics simulations confirmed the improved binding energies and stability of compounds 15 and 16, with compound 15 showing remarkable stability and strong binding affinity with the 3 C-like proteinase (1P9U). This comprehensive in-silico review identifies compound 15 as a promising candidate for experimental evaluation as a potential COVID-19 drug, highlighting a significant advancement in our battle against the pandemic.