共识建模用于预测化学结合到Transhyretin作为Tox24挑战的获胜解决方案的共识建模
Dmitriy M Makarov1, Alexander A Ksenofontov1, Yury A Budkov1,2,3
1G. A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Ivanovo 153045, Russia.
Chemical research in toxicology
|February 19, 2025
概括
预测性毒理学模型识别安全化学品,降低成本. 赢得Tox24挑战的模型,一个共识的方法,实现了20.3%的RMSE transthyretin (TTR) 的结合预测.
科学领域:
- 计算毒理学计算毒理学
- 化学信息学 化学信息学
- 发现药物的发现.
背景情况:
- 预测毒理学为评估化学品安全的传统方法提供了一种具有成本效益的替代方案.
- 托克斯24挑战旨在评估计算方法,以预测化学结合到TTR.
- 使用了1512种不同的化学物质的数据集,由环境保护局提供.
研究的目的:
- 介绍Tox24挑战赛中获胜的模型,用于预测TTR结合活性.
- 详细介绍了用于提高预测准确性的方法和改进.
- 评估化学安全评估的计算毒理学的进展.
主要方法:
- 开发了一个共识模型,结合了两个catBoost模型 (OEstate,Mold2描述符) 和两个基于变压器的模型.
- 变压器卷积神经网络 (CNN) 作为独立模型表现出强的性能.
- 一个基于CNN的多任务模型,结合了11个额外的毒性数据集,取得了类似的结果.
主要成果:
- 获胜的共识模型在Tox24挑战赛中取得了最佳表现.
- 将图形CNN多任务模型纳入其中改善了解决方案,在盲目测试组中,根平均平方误差 (RMSE) 降低到20.3%.
- 变压器CNN和图形CNN多任务模型在预测TTR结合方面显示出显著的潜力.
结论:
- 计算方法,特别是共识和多任务学习方法,在预测毒理学中显示出重大前景.
- 开发的模型为评估与TTR的化学结合提供了一个强大而准确的工具,有助于识别更安全的化合物.
- 获奖解决方案在OCHEM平台上提供,有助于推进化学信息学和药物发现.
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