Meta-Analysis of Methods Used in Free Energy Calculations of Molecular Modeling in Drug Discovery

Abstract

Purpose: This study assesses the accuracy and reliability of free energy calculation (FEC) methods as quantitative tools in drug discovery by synthesizing empirical evidence on their predictive performance relative to experimental binding affinity data. Methodology: A systematic review and meta-analysis were conducted following PRISMA guidelines. Five databases relevant to pharmaceutical and computational research were searched. Of 1,208 identified studies, 25 met the inclusion criteria and were analyzed (n = 25). The primary performance metric was the correlation coefficient between computed and experimentally measured binding affinities. A random-effects model was applied to estimate the pooled effect size while accounting for between-study variability. Results: The meta-analysis produced a strong pooled correlation coefficient (r = 0.78; 95% CI: 0.74–0.81), indicating high predictive accuracy of FEC methods. The standard error (SE = 0.043) reflects robust estimation precision, and the large z-score (z = 24.29, p < 0.001) confirms statistical significance. The narrow confidence interval further demonstrates the consistency and reliability of FEC performance across studies. Novelty and Contribution: This study provides one of the first quantitative meta-analytic evaluations of FEC methods in drug development. By integrating evidence across diverse computational frameworks and molecular systems, it offers strong empirical validation of FECs as reliable predictors of drug–target binding interactions. Practical and Social Implications: The findings support broader adoption of FEC methods in pharmaceutical research, particularly for lead optimization and affinity prediction. Increased confidence in FEC accuracy can reduce experimental costs, accelerate drug development, and contribute to more efficient production of effective therapeutics.