Computational Compound Prioritization Strategy Incorporating Docking Variability and Interaction Energies Identifies Putative LRH-1 Binders
Year: 2026
Type: article
Abstract: Compound ranking in structure-based virtual screening notoriously yields highly ranked false positive binders due to variable poses or biases in scoring terms. We developed a compound prioritization strategy that utilizes sampled docked poses from contrasting docking approaches (targeted physics-based docking and blind docking with a generative model) against multiple models of the target protein to train a multi-layer perceptron (MLP). The model predicts binders at the orthosteric ligand-binding pocket of the nuclear receptor LRH-1 (NR5A2). Our approach circumvents the reliance on a single docked pose for scoring compounds or individual scoring metrics for compound ranking. In a separate benchmarking set, we observed that the MLP identifies known binders that are chemically dissimilar from the compounds in the training set and is sensitive to single scaffold modifications, making it a potential tool for lead optimization. We applied our strategy to a prospective virtual screening campaign, which resulted in the discovery of four putative LRH-1 binders. We found that a combination of scoring and prediction metrics enriches for the hit compounds across library sizes. In all, this implementation presents a method to leverage structural and experimental data to aid virtual screening for a challenging protein target.
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Funders: Alexander von Humboldt-Stiftung, Vanderbilt University, Deutscher Akademischer Austauschdienst, Deutsche Forschungsgemeinschaft, German Network for Bioinformatics Infrastructure, National Institutes of Health, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases
Awards: G1633987393, G1862491092, G238684906, Molecular Biophysics Training Grant at Vanderbilt, G2826889988, G3735802008, G3759311550, G482723523, G485242556, SFB 1423: Strukturelle Dynamik der GPCRAktivierung und Signaltransduktion, Analysis of oxidative stress through deep atomic embeddings, G5440856903, G6029886166, TRR 386: HYP*MOL – Hyperpolarisation in molekularen Systemen, G7223072543, G7355620950, G7599597681, G8072767560, SPP 2363: Nutzung und Entwicklung des maschinellen Lernens für molekulare Anwendungen Molekulares maschinelles Lernen, Structure and function of phospholipids and inositol phosphates in the nucleus., Integrated Training in Engineering and Diabetes, Research Training in Diabetes and Endocrinology
OpenAlex ID: W7163826740