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通过顺序模型优化,RECOVER在体外识别了协同作用的药物组合
Paul Bertin1, Jarrid Rector-Brooks1, Deepak Sharma1
1Mila, the Quebec AI Institute, Montreal, QC, Canada.
深度学习模型有效地识别了用于癌症治疗的协同药物组合. 这种方法通过仅探索化学空间的一小部分,显著提高了有效药物对的发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 在药理学中的机器学习.
背景情况:
- 大小分子库的详尽选对于药物发现是不可行的.
- 深度学习模型擅长预测药物协同效应得分在 silico.
- 现有的药物组合数据库是有偏见的,限制了概括性.
研究的目的:
- 开发和应用一个使用深度学习的序列模型优化策略.
- 为了有效地识别具有针对癌症细胞系的协同活性的药物组合.
- 改进强效药物组合的选择过程.
主要方法:
- 在5个实验回合中使用深度学习模型进行序列模型优化.
- 专注于选择富含协同作用和抗癌活性的药物组合.
- 评估了大约5%的总化学搜索空间.
主要成果:
- 确定了具有增强协同作用和抗癌活性的药物组合.
- 学习的药物嵌入,来自结构信息,反映生物机制.
- 与随机选择相比,in silico基准测试显示了协同组合的5-10倍丰富.
结论:
- 序列模型优化是发现协同药物组合的高效策略.
- 深度学习模型可以通过学习有意义的药物表示来指导药物发现.
- 这种方法显著加速了有效的抗癌药物组合的识别.
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