自动机器学习方法用于开发心脏类固醇抑制Na+/K+-ATPase定量结构-活性关系模型
Yohei Takada1, Kazuhiro Kaneko2
1Corporate Planning Department, Otsuka Holdings Co., Ltd, Shinagawa Grand Central Tower 2-16-4 Konan, Minato-ku, Tokyo, 108-8241, Japan. takaday@otsuka.jp.
Pharmacological reports : PR
|June 24, 2023
概括
自动机器学习创建了一个定量结构-活性关系 (QSAR) 模型来预测心脏类固醇 (CS) 活性. 这种方法可以有效地识别潜在的候选药物,而无需进行广泛的实验.
科学领域:
- 化学信息学是一种化学信息学.
- 计算化学是一种计算化学.
- 药物发现 药物发现
背景情况:
- 定量结构-活性关系 (QSAR) 建模将化学结构与生物活动联系起来.
- 心脏类固醇 (CSs) 抑制Na+/K+-ATPase (NKA),在心脏病和癌症治疗中具有应用.
- 自动机器学习 (AutoML) 加快了大型数据集的化学信息学分析.
研究的目的:
- 开发基于机器学习的自动化QSAR模型,用于预测新型心脏类固醇 (CSs) 的抑制活性.
- 通过在没有实验测试的情况下预测化合物活性来快速识别潜在的候选药物.
主要方法:
- 从科学文献中收集了215种CS衍生物的数据,包括化学结构和抑制作用.
- 使用分子描述符,指纹和生物活动数据构建预测性QSAR模型.
- 采用自动机器学习,以基于LogLoss值进行高效的模型开发和选择.
主要成果:
- 根据LogLoss值选择了最好的预测性QSAR模型.
- 在测试数据集上获得了0.6729的马修斯相关系数,0.8813的F1得分和0.8812的AUC.
- 证明了对CS衍生物的预测模型的自动化构建,促进了新药候选药物的识别.
结论:
- 开发的基于机器学习的自动化QSAR方法允许节省时间的模型构建.
- 这种方法即使在有限数量的化合物上也有效,加速了药物发现管道.
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