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用ToxCast/Tox21生物试验数据识别可解释毒性预测模型的最佳机器学习算法和分子指纹

Donghyeon Kim¹, Jaeseong Jeong¹, Jinhee Choi¹

¹School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.

|September 16, 2024

概括

简单的机器学习模型,如随机森林与MACCS指纹,提供可解释的毒性预测. 这种方法平衡了化学安全评估中的监管用途的性能和可解释性.

科学领域:

计算毒理学计算毒理学
化学信息学化学信息学
机器学习是机器学习.

背景情况:

当前的毒性预测模型往往缺乏解释性,阻碍了监管采用.
需要平衡模型性能与可解释性,以进行可靠的化学安全评估.

研究的目的:

确定分子指纹和机器学习算法的最佳组合,用于可解释的毒性预测.
使用ToxCast/Tox21数据调查模型复杂性,性能和可解释性之间的权衡.

主要方法:

评估了五个分子指纹 (MACCS,摩根,RDKit,分层,模式) 和六个算法 (MLP,GBT,随机森林,kNN,后勤回归,天真贝叶斯).
在ToxCast/Tox21生物测试数据集上训练和评估了1092个模型.
专注于实现F1分数或0.8或更高准确度的模型.

主要成果:

35个模型表现出可接受的性能 (F1分数/准确度>=0.8).
将MACCS或摩根指纹与随机森林算法的组合显示出强大的性能.
马克斯和随机森林为与毒性相关的化学结构提供了有价值的,可解释的见解.

结论:

简单的模型,特别是MACCS-Random Forest,对于可解释的毒性预测是有效的.
优先考虑可解释性并不一定要牺牲预测性能.
这项研究强调了简单模型在基于化学特征的Tox21数据分析中对监管应用的价值.

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