在临床前药物发现中的机器学习.
Denise B Catacutan1,2,3, Jeremie Alexander1,2,3, Autumn Arnold1,2,3
1Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.
Nature chemical biology
|July 19, 2024
概括
机器学习 (ML) 可以显著改善昂贵且耗时的药物发现. 整合ML算法加快了在临床前药物开发中的成功发现,作用机制阐明和化学优化.
科学领域:
- 药理学 药理学是指药理学的学科.
- 生物技术是生物技术.
- 计算化学计算化学
背景情况:
- 药物发现是一个漫长,昂贵的过程,失败率很高.
- 传统药物开发在效率和成本方面面临重大挑战.
- 随着大型生物和化学数据集的日益普及,计算方法的机会越来越大.
研究的目的:
- 讨论机器学习 (ML) 方法在临床前药物发现中的整合.
- 突出ML的应用,以加速药物开发的早期关键阶段.
- 探索ML的潜力,以彻底改变药物发现管道.
主要方法:
- 在药物发现中审查现有的机器学习应用程序.
- 分析跨多个治疗领域的基于ML的努力.
- 讨论ML在击中发现,MOA阐明和属性优化中的作用.
主要成果:
- 机器学习技术非常适合用于增强传统药物开发.
- ML加速了初始命中发现,作用机制 (MOA) 阐明和化学性质优化.
- 基于ML的努力在各种疾病领域显示出希望.
结论:
- 在临床前药物发现过程中整合算法方法至关重要.
- 完全与ML集成的药物发现管道准备好定义药物开发的未来.
- 机器学习提供了一个强大的工具包,可以提高药物发现计划的效率和成功率.
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