piscesCSM:预测抗癌协同药物组合
Raghad AlJarf1,2,3, Carlos H M Rodrigues1,2,3,4, Yoochan Myung1,2,3,4
1Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia.
Journal of cheminformatics
|July 19, 2024
概括
我们开发了 piscesCSM,这是一种机器学习工具,可以使用基于图的化学结构准确预测协同作用的抗癌药物组合. 这种计算方法通过识别有效的药物对,提高预测准确度来增强癌症治疗.
科学领域:
- 计算化学和化学信息学
- 机器学习在药物发现中的作用
- 癌症药理学 癌症药理学
背景情况:
- 确定协同作用的药物组合对于有效的癌症治疗至关重要.
- 预测药物协同作用的现有计算方法在性能和解释性方面存在局限性.
- 需要新的计算工具来加快抗癌药物组合的发现.
研究的目的:
- 开发一种新的预测工具,piscesCSM,用于识别协同作用的抗癌药物组合.
- 利用基于图形的小分子表示来准确预测协同效应.
- 提供一个用户友好的平台,用于选潜在的协同作用药物组合.
主要方法:
- 使用基于图形的小分子化学结构的表示.
- 开发了一个受监督的机器学习模型,在30多个癌症细胞系上进行训练.
- 在独立,非冗余的盲测试集上验证了模型,包括来自AstraZeneca的数据.
- 使用物理化学性质和基于图形的签名探索模型的解释性.
主要成果:
- piscesCSM在接收器运行特征曲线 (AUROC) 下的面积达到了0.89.
- 在独立测试中,在预测准确度方面,超越了最先进的方法超过16%.
- 确定了物理化学性质和基于图形的特征,以预测化疗协同作用.
- 物理化学性质与基于图形的签名相结合,改善了分类预测任务.
结论:
- piscesCSM是一个强大的机器学习框架,用于预测协同作用的抗癌药物组合.
- 与现有方法相比,该工具提供了更高的预测准确度.
- piscesCSM为研究人员提供了一个有价值的,可访问的资源,以加快有效和安全的组合疗法的识别.
- 该模型的可解释性突出了与药物协同作用相关的关键分子特征.
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