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基于人工智能的分子性质预测药物的光敏化作用的药物

Amun G Hofmann¹, Benedikt Weber², Sally Ibbotson³

¹FIFOS - Forum for Integrative Research & Systems Biology, Vienna, Austria.

Journal of drug targeting

|December 2, 2024

概括

人工智能模型可以预测药物诱导的光敏感性,这是一个常见的不良药物反应. 机器学习方法,包括XGBoost和深度学习,在识别光敏化化合物方面显示出前景.

科学领域:

药理学药理学是一个学科.
计算化学计算化学
毒理学毒理学毒理学

背景情况:

药物诱导的光敏感性是医疗学科的重大不良事件.
识别药物和化学品的光敏化潜力对于患者安全至关重要.

研究的目的:

评估使用人工智能 (AI) 预测药物和化学化合物的光敏感作用的可行性.
为了比较不同的AI模型在预测光敏度方面的表现.

主要方法:

训练了三个AI模型:物流回归,XGBoost和一个深度学习模型 (Chemprop) 在2200种药物的数据集上.
使用已知光敏剂的精选清单进行模型培训和验证.
使用tox21数据集评估模型性能,分析ROC-AUC得分和重叠的预测.

主要成果:

在培训期间,XGBoost获得了最高的ROC-AUC (0.9525),而XGBoost和Chemprop在外部验证中表现相似 (ROC-AUC~0.79).
对顶级预测的分析揭示了外部验证集中的55种跨模型重叠的化合物.
诺基诺中预测的光敏化作用与已知的罪祸首基结构 (如化烯化物) 相相关.

结论:

这三种人工智能模型都表现出预测化学化合物的光敏感效应的能力.
与简单模型相比,复杂的人工智能模型对其预测的信心更高.
由人工智能驱动的工作流提供了一种有希望的方法来识别潜在的光敏化剂.

关键词:

机器学习是机器学习.不良反应不良反应不良反应人工智能是一种人工智能.深度学习是一种深度学习.光敏感性光敏感性摄影毒性摄影毒性

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