基于机器学习的化学信息学的最新进展:全面审查
Sarfaraz K Niazi1, Zamara Mariam2
1College of Pharmacy, University of Illinois, Chicago, IL 61820, USA.
International journal of molecular sciences
|July 29, 2023
概括
化学信息学和定量结构-活性关系 (QSAR) 建模结合机器学习 (ML) 加快药物发现. 这种方法使用分子描述器和ML算法进行预测分子设计,帮助寻找新药.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 药理学 药理学是指药理学的学科.
背景情况:
- 现代药物发现越来越依赖于计算方法.
- 化学信息学和定量结构-活性关系 (QSAR) 建模是关键学科.
- 机器学习 (ML) 为分析复杂的化学数据提供了强大的工具.
研究的目的:
- 审查化学信息学,QSAR和ML在药物发现中的整合.
- 解释分子描述符和结构-活性关系 (SAR) 的作用.
- 引导研究人员开发和应用ML-QSAR模型.
主要方法:
- 讨论化学信息学原理和化学数据表示.
- 分子描述符的解释 (例如,2D指纹,拓索引).
- ML-QSAR模型开发的概述,包括特征选择和验证.
主要成果:
- 像回归和支向量机器这样的ML算法可以从分子结构中预测生物活动.
- 强大的ML-QSAR模型能够进行预测分子分析.
- 这些领域之间的协同作用促进了对结构-活动关系的理解.
结论:
- 化学信息学,QSAR和ML的结合是药物发现的强大策略.
- 使用这些技术的预测分子分析可以加快新型治疗剂的识别.
- 这种综合方法对制药科学具有重大前景.
关键词:
人工智能/MLML在QSAR中使用QSAR.这就是QSPR.这就是为什么SAR SAR SAR.生物活动是生物活动.化学信息学 化学信息学计算验证的计算验证分子描述器 分子描述器预测建模预测建模小分子的小分子.更多相关视频
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