机器学习方法研究血管激素转化酶抑制剂的结构-活性关系
Tianshi Yu1, Chanin Nantasenamat2, Nuttapat Anuwongcharoen1
1Center of Data Mining and Biomedical informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
ACS omega
|November 29, 2023
概括
这项研究利用机器学习优化血管激素转化酶抑制剂 (ACEI),以改善药物发现. 结果显示,支架多样性是活跃ACEI的关键,指导未来的优化工作.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药理学 药理学 是一个学科.
背景情况:
- ангиотензин转化酶抑制剂 (ACEI) 对于治疗高血压,心力衰竭和脏疾病至关重要.
- 目前的ACEI具有不良影响,特别是功能衰竭,需要优化.
- 显著需要改进的ACEI疗法,具有更好的安全性.
研究的目的:
- 使用机器学习对ACEI进行结构-活动关系 (SAR) 调查.
- 确定有助于ACEI活动并减少不利影响的关键结构特征和支架.
- 开发用于新型ACEI设计的预测定量结构-活动关系 (QSAR) 模型.
主要方法:
- 利用机器学习算法来分析来自ChEMBL数据库的ACEI数据.
- 进行了探索性数据分析和支架分析,以确定显著的Murcko支架.
- 使用Mordred描述符和随机森林/极端梯度提升算法开发和验证了QSAR模型.
主要成果:
- 确定了9个代表的Murcko支架,其中活跃的ACEI显示出更大的支架多样性.
- 发现2,3,5,7和9的支架比1,3,6和8的支架更有利.
- 开发了具有高精度 (高达0.981) 和MCC (高达0.972) 的强大的QSAR模型,用于预测ACEI活动.
- 通过SALI图谱分析确定了活动悬崖,突出了影响有效性的特定结构变异.
结论:
- 脚手架的多样性对于优化ACEI活动和减少副作用至关重要.
- 机器学习和QSAR建模为推进ACEI药物发现提供了强大的工具.
- 确定了有利的支架和SAR洞察力,可以指导下一代ACEI的设计.
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