Prediction of Binding Affinity of ACE2 Inhibitors Based on Molecular Docking and Free Energy Perturbation Calculations

Accurate prediction of binding affinity for ACE2 inhibitors remains a critical challenge in computational drug discovery, particularly given the enzyme's therapeutic importance in cardiovascular diseases and its role as the cellular entry receptor for SARS-CoV-2.This study aimed to develop and validate a comprehensive computational framework integrating molecular docking with free energy perturbation (FEP) calculations to achieve both high-throughput screening capability and rigorous thermodynamic accuracy for ACE2 inhibitor binding affinity prediction.A diverse compound library of 1,247 molecules was assembled and subjected to molecular docking using Schrdinger Glide against the ACE2 crystal structure (PDB: 1R42), with compounds achieving GlideScore -6.0 kcal/mol selected for subsequent analysis.FEP+ calculations employing OPLS3 force field and REST2 enhanced sampling were performed on 156 selected compounds using a 12 -window thermodynamic integration protocol, with each window subjected to 2 ns equilibration followed by 5 ns production runs.Model validation was conducted using 48 experimentally characterized compounds with known binding affinities, evaluated through 5-fold cross-validation and multiple performance metrics.The combined docking-FEP approach demonstrated superior predictive accuracy with a Pearson correlation