通过应用机器学习模型来预测人工液态的绝对水溶性,作为固态的代理.
Sadra Kashef Ol Gheta1, Anne Bonin1, Thomas Gerlach2,3
1Bayer AG, Pharmaceuticals, R&D, Computational Molecular Design, 42096, Wuppertal, Germany.
Journal of computer-aided molecular design
|October 25, 2023
概括
机器学习模型使用量子力学 (QM) 衍生描述符预测药物溶解度,优于传统的QSAR模型. 这些基于QM的方法为药物发现中的可溶性预测提供了更准确和更具成本效益的方法.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习是机器学习.
背景情况:
- 对水溶性的准确预测对于药物开发至关重要.
- 传统的定量结构-活性关系 (QSAR) 模型经常与绝对可溶性预测作斗争.
- 量子力学 (QM) 衍生描述符提供了更详细的分子属性的表征.
研究的目的:
- 开发和评估机器学习模型,用于预测类似药物化合物的绝对可溶性.
- 将QM衍生描述符与传统化学信息学描述符的性能进行比较.
- 探索可溶性预测的经济有效的替代方案.
主要方法:
- 利用机器学习算法 (例如,随机森林,梯度提升) 与QM衍生的COSMO-RS描述符和摩根指纹相结合.
- 采用了两种预测策略:一个涉及人工液态的假设路径和直接可溶性预测.
- 在Bayer内部化合物数据上训练模型,并使用外部可溶性挑战数据集进行验证.
主要成果:
- 与现有的QSAR模型相比,使用QM衍生描述符的机器学习模型显著提高了绝对溶解度预测的准确性.
- 来自QM的描述符在标准化学信息学描述符上表现出优越性.
- 基于碎片的COSMOquick计算提供了一个低成本的替代方案,预测质量仅略有下降.
结论:
- 与QM衍生描述符集成的机器学习代表了准确的绝对溶解率预测的强大方法.
- 在这个任务中,QM衍生的描述符比化学信息学描述符更有效.
- 基于QM的成本效益高的方法可以在没有大幅损失预测性能的情况下使用.
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