用自动化机器学习提升药物渗透性预测准确率
CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction
- 采用自动机器学习结合八类分子表征，系统评估性能
- CaliciBoost模型达最优MAE，3D特征使误差降低15.73%
- 为数据少的药物吸收预测提供有效特征选择方案
Caco-2渗透性是早期药物发现中预测候选药物口服吸收的关键体外指标。为提升计算预测的准确性与效率，我们系统研究了八种分子特征表示类型（包括2D/3D描述符、结构指纹、深度学习嵌入）与自动化机器学习技术结合在预测Caco-2渗透性中的表现。基于两个规模与多样性不同的数据集（TDC基准和精选OCHEM数据），评估了不同表征下的模型性能，发现PaDEL、Mordred和RDKit描述符特别有效。值得注意的是，基于AutoML的模型CaliciBoost取得了最佳MAE表现。此外，对于PaDEL和Mordred表征，引入3D描述符相比仅使用2D特征，使MAE降低15.73%，特征重要性分析验证了这一结果。这些发现凸显了AutoML在ADMET建模中的有效性，并为数据有限的预测任务提供了实用的特征选择指导。
原文摘要 · Abstract (English)
Caco-2 permeability serves as a critical in vitro indicator for predicting the oral absorption of drug candidates during early-stage drug discovery. To enhance the accuracy and efficiency of computational predictions, we systematically investigated the impact of eight molecular feature representation types including 2D/3D descriptors, structural fingerprints, and deep learning-based embeddings combined with automated machine learning techniques to predict Caco-2 permeability. Using two datasets of differing scale and diversity (TDC benchmark and curated OCHEM data), we assessed model performance across representations and identified PaDEL, Mordred, and RDKit descriptors as particularly effective for Caco-2 prediction. Notably, the AutoML-based model CaliciBoost achieved the best MAE performance. Furthermore, for both PaDEL and Mordred representations, the incorporation of 3D descriptors resulted in a 15.73% reduction in MAE compared to using 2D features alone, as confirmed by feature importance analysis. These findings highlight the effectiveness of AutoML approaches in ADMET modeling and offer practical guidance for feature selection in data-limited prediction tasks.
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