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Vladimir Chupakhin¹, John DiBella¹

¹Simulations Plus, Inc., 800 Park Offices Drive, Suite 401, Research Triangle Park, North Carolina 27709, United States.

Journal of chemical information and modeling

|December 11, 2025

概括

预测药物的特性,如吸收,分布,新陈代谢,分泌和毒性 (ADMET) 是至关重要的. 使用TabPFN等表式学习器的描述器优先策略在ADMET预测中实现了显著的错误减少,简化了药物发现.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
在药理学中的机器学习.

背景情况:

吸收,分布,新陈代谢,分泌和毒性 (ADMET) 属性预测是小分子药物发现的关键挑战.
准确的ADMET预测加速了可行的候选药物的识别.

研究的目的:

为PolarisHub防病毒竞赛提供第三名的解决方案,专注于ADMET属性预测.
通过使用高容量的表式学习器来评估描述器优先策略,以预测与小分子设计相关的关键ADMET终点.

主要方法:

来自ADMET预测器的描述符和模型作为元特征的系统策划.
在单任务回归中,对表格数据 (TabPFN) 应用预训练的基础模型.
与梯度提升基线 (CatBoost) 的比较.
新型特征集的工程,包括调整的片段表示和代谢场所模式.

主要成果:

在5个ADMET终点 (HLM,MLM,MDR1-MDCKII透性,动力溶解性,LogD) 中,TabPFN的表现始终优于或与CatBoost基线相匹配.
实现了高达44%的平均绝对误差 (MAE) 减少.
工程特征集提供了适度的性能增长.
证明了将原则性特征工程与强大的建模方法相结合的有效性.

结论:

描述器优先策略与强大的表式学习者相结合,在实际的ADMET建模中提供了竞争优势.
复杂的机器学习架构并不总是必要的,当丰富的,验证的描述符是可用的.
提出的方法简化了部署,并减少了对广泛的超参数搜索的需求.

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