复合活动预测与剂量依赖的转录基因特征和深度学习
William J Godinez1, Vladimir Trifonov2, Bin Fang2
1Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States.
Journal of chemical information and modeling
|January 31, 2024
概括
转录组学到活动转换器 (TAT) 模型使用基因表达特征预测化合物生物活性. 这些计算模型成功识别了疟疾抑制剂,证明了药物发现的成本高效方法.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 系统药理学 系统药理学
背景情况:
- 预测各种测试中的化合物活性在药物发现中至关重要.
- 来自分析分析的基因表达特征可以预测其他分析中的化合物活性.
- 应用包括预测作用机制 (MoA),非目标效应和多药学.
研究的目的:
- 引入转录组学到活动变压器 (TAT) 模型,用于预测生物化学和细胞分析中的化合物活性.
- 为了利用多种化合物度的基因表达特征,提高预测准确度.
主要方法:
- 使用RASL-seq试验中的基因表达数据开发了TAT模型.
- 训练模型预测262个剂量反应测试中的2692种化合物的活性.
- 使用持有数据集和前性实验测试验证实模型实用性.
主要成果:
- 在51%的测试中获得了有用的预测模型.
- 在疟疾抑制试验中对TAT预测进行了前性验证,达到63%的成功率.
- 通过前性验证确定了几种亚微分子疟疾抑制剂.
结论:
- 通过TAT建模的跨化合物度的转录基因反应提供了一个强大的预测框架.
- TAT模型提供了一种具有成本效益的方法,用于在各种测试中识别化合物生物活性.
- 这种方法具有加速药物发现和开发的巨大潜力.
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