KANEL：Kolmogorov-Arnold网络集成学习实现高通量虚拟筛选中的早期命中富集
KANEL: Kolmogorov-Arnold Network Ensemble Learning Enables Early Hit Enrichment in High-Throughput Virtual Screening
AI总结：
KANEL通过结合可解释的Kolmogorov-Arnold网络与XGBoost等模型，提升高通量虚拟筛选中早期命中富集的准确性。
摘要：
机器学习模型在化学生物活性预测中被越来越多地用于优先选择虚拟筛选库中的少量化合物以进行实验跟进。在这些应用中，通过计算前N个命中（PPV@N）的正预测值（PPV）等早期命中富集指标来评估模型准确性，比传统的全局指标如AUC更为合适和可行。我们提出了KANEL，一种结合可解释的Kolmogorov-Arnold网络（KANs）与XGBoost、随机森林和多层感知机模型的集成工作流程，这些模型在互补的分子表示（LillyMol描述符、RDKit衍生描述符和Morgan指纹）上进行训练。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
