ABSTRACT Computer‐aided drug discovery (CADD) has revolutionized the way we screen huge libraries of synthetic and natural compounds, offering a faster and more affordable alternative to traditional lab‐based assays. Yet, questions remain about the reliability of these computational predictions, especially given the risk of false positives that cast uncertainty over the entire screening process. To explore this, we tested a library of 354 flavonoids against caspase‐3; a key enzyme involved in apoptosis, and compared the results from different docking tools with in vitro enzymatic inhibition assays. Our objectives were threefold: to identify a highly reliable docking tool for screening flavonoids, to identify potent caspase‐3 inhibitors, and to determine whether these compounds could protect liver cells in a fatty liver disease model. Although AutoDock4 performed better than the other two tools in our study, the predictive accuracy of all three docking platforms did not correlate well with the biochemical screen. While consensus strategies (RbR and RbE) improved ranking accuracy, they showed limited reliability as well. We identified six lead compounds that inhibited the caspase‐3 activity. Out of which, in both cell lines (HepG2 & Huh‐7), Okanin showed significant protection against amiodarone, while Okanin, Irigenin, and Isoliquiritigenin provided substantial protection against oleic acid‐induced cell death. The findings highlight the limitations of individual docking programs and emphasize the utility of an integrated approach to enhance the effectiveness of predictions for virtual screening. Furthermore, this study provides a robust framework for the rational design of flavonoid‐based caspase inhibitors with optimized efficacy and pharmacological profiles.
Asveda et al. (2026) studied this question.