MolDockLab: a data‑driven workflow for balanced consensus docking in hit identification

Le résumé fourni par la source

Structure-based virtual screening (SBVS) is a cornerstone of modern computer-aided drug design, yet its success is highly dependent on selecting an appropriate combination of docking tools, scoring functions (SFs), and ranking strategies from the vast array of available options. MolDockLab addresses this challenge by providing an automated, data-driven framework that optimizes SBVS workflows for a given protein target, for balanced predictive performance and computational efficiency, ultimately improving hit identification.MolDockLab systematically explores all combinations of five docking engines, fifteen SFs, and three consensus ranking strategies using a calibration set of approximately 200 compounds with known bioactivity. The SBVS workflow showing the highest correlation with experimental data is then applied to the larger screening library. Final hit selection from the top 1% integrates protein–ligand interaction profiler (PLIP)-derived protein–ligand interaction fingerprints, structural diversity assessment, and expert manual inspection.MolDockLab was validated through both a retrospective and a prospective case study. In the retrospective evaluation on epidermal growth factor receptor (EGFR), the chosen pipeline achieved a Spearman correlation of 0.35 and an enrichment factor at 10% (EF₁₀%) of 1.51, both consistent with the calibration set, used for pipeline selection. In the prospective application to the energy coupling factor transporters (ECF-T), a challenging transmembrane target featuring a dynamic, membrane-embedded cryptic binding site and no co-crystallized ligand, the optimal pipeline on the calibration set reached a Spearman correlation of 0.45 and an EF₁₀% of 3.13. Subsequent post-processing enabled the identification and in vitro confirmation of two chemically novel active compounds, which their potencies rival those of the best ECF-T inhibitors reported to date.Application to a challenging case study demonstrates that MolDockLab effectively enhances hit identification by tailoring SBVS workflows to individual targets. While the framework improves robustness and reproducibility, its performance remains dependent on the availability of an adequate calibration set for pipeline selection.

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