[2603.25755] KANEL: Kolmogorov-Arnold Network Ensemble Learning Enables Early Hit Enrichment in High-Throughput Virtual Screening

Physics > Chemical Physics arXiv:2603.25755 (physics) [Submitted on 25 Mar 2026] Title:KANEL: Kolmogorov-Arnold Network Ensemble Learning Enables Early Hit Enrichment in High-Throughput Virtual Screening Authors:Pavel Koptev, Nikita Krainov, Konstantin Malkov, Alexander Tropsha View a PDF of the paper titled KANEL: Kolmogorov-Arnold Network Ensemble Learning Enables Early Hit Enrichment in High-Throughput Virtual Screening, by Pavel Koptev and 3 other authors View PDF Abstract:Machine learning models of chemical bioactivity are increasingly used for prioritizing a small number of compounds in virtual screening libraries for experimental follow-up. In these applications, assessing model accuracy by early hit enrichment such as Positive Predicted Value (PPV) calculated for top N hits (PPV@N) is more appropriate and actionable than traditional global metrics such as AUC. We present KANEL, an ensemble workflow that combines interpretable Kolmogorov-Arnold Networks (KANs) with XGBoost, random forest, and multilayer perceptron models trained on complementary molecular representations (LillyMol descriptors, RDKit-derived descriptors, and Morgan fingerprints). Comments: Subjects: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML) Cite as: arXiv:2603.25755 [physics.chem-ph]   (or arXiv:2603.25755v1 [physics.chem-ph] for this version)   https://doi.org/10.48550/arXiv.2603.25755 Focus to learn more arXiv-issued DO...