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MolToxPred:使用机器学习方法进行小分子毒性预测

Anjali Setiya¹, Vinod Jani¹, Uddhavesh Sonavane¹

¹HPC-Medical & Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC) Innovation Park, Panchawati, Pashan Pune 411008 India rajendra@cdac.in.

|January 31, 2024

概括

机器学习工具MolToxPred使用堆叠模型方法预测化学毒性. 这种人工智能驱动的方法降低了药物开发成本和动物试验,提高了化学安全和药物发现管道.

科学领域:

计算化学是一种计算化学.
毒理学毒理学毒理学
机器学习在药物发现中的作用

背景情况:

药物开发面临的挑战是由于化学毒性,成本和时间的增加.
机器学习 (ML) 为预测毒性提供了解决方案,减少了实验需求和道德问题.

研究的目的:

开发MolToxPred,一种基于ML的工具,用于预测小分子和代谢物的毒性.
提高药物发现和化学品评估的效率和安全性.

主要方法:

开发了一个堆叠的ML模型,使用随机森林,多层感知器和LightGBM作为基础分类器,用后勤回归作为元分类器.
利用多种分子描述符和指纹,加上特征选择和贝叶斯优化.
员工分层5倍交叉验证培训和验证.

主要成果:

在测试组件上,MolToxPred 实现了 87.76% 的接收器运行特征曲线 (AUROC) 下面面积,在外部验证组件上达到 88.84%.
堆叠模型与单个基准分类器和现有工具相比,表现优越.
确定了与毒性终点相关的结构性警报,用于途径分析.

结论:

MolToxPred提供了一种强大而高效的in silico方法,用于毒性预测.
该工具可以通过尽量减少实验性毒性测试来帮助药物发现和监管过程.
堆叠组合方法和全面的特征工程有助于其高预测准确度.

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