知识图有助于对药物和化学毒性的全面解释
Yun Hao1, Joseph D Romano2,3, Jason H Moore4
1Genomics and Computational Biology (GCB) Graduate Program, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
CPT: pharmacometrics & systems pharmacology
|July 21, 2023
概括
可解释的深度学习模型AIDTox准确预测细胞毒性,并解释细胞机制. 这种计算毒理学方法克服了复杂毒性终点的黑盒模型的局限性.
科学领域:
- 计算毒理学计算毒理学
- 化学信息学 化学信息学
- 生物信息学是一种生物信息学.
背景情况:
- 由于多种潜在机制,预测复杂的毒理终点是具有挑战性的.
- 当前的计算模型往往缺乏准确性或解释性,阻碍了机械的理解.
研究的目的:
- 引入AIDTox,这是一个可解释的深度学习模型,用于预测细胞毒性.
- 通过综合生物知识,提供对复杂毒性终点的机制性见解.
主要方法:
- 开发了一个可解释的深度学习框架 (AIDTox).
- 综合化学基因相互作用,基因路径注释和路径等级.
- 训练并验证了对 HepG2 和 HEK293 细胞的细胞毒性数据的模型.
主要成果:
- 艾迪托克斯 (AIDTox) 能够准确预测细胞毒性结果.
- 该模型提供了对毒性机制的全面解释.
- 解释涵盖了药物向相互作用,新陈代谢和排泄途径.
结论:
- AIDTox提供了一个新的计算框架,用于理解复杂的毒性.
- 鉴于AIDTox的可解释性,可以更轻松地进行机械毒理学研究.
- 这种方法提高了毒性预测的可靠性和透明度.
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