在数据的旅程中学到的经验:从实验到模型来预测酶亲和力,选择性,多药学和耐药性
Raquel López-Ríos de Castro1,2, Jaime Rodríguez-Guerra1,2, David Schaller1,2
1In silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, Germany.
bioRxiv : the preprint server for biology
|September 24, 2024
概括
机器学习 (ML) 通过使用基于结构的模型预测结合亲和关系来加速药物发现. 基诺ML框架为小型分子药物发现,特别是酶的可重现的ML实验提供了便利.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 生物技术是生物技术.
背景情况:
- 机器学习 (ML) 正在彻底改变药物发现,特别是基于结构的方法预测蛋白质:连接体结合亲和力.
- 对于个别目标的数据稀缺性阻碍了ML模型的概括性;整合跨相关目标的数据,如酶,提供了一个解决方案.
研究的目的:
- 报告开发KinoML的经验,这是基于目标的小分子药物发现中的结构启用ML的新框架.
- 要强调框架的重点是酶,利用它们的保存结构进行超级家族范围的预测.
- 在药物发现中共享用于构建可重复和可重复使用的ML平台的关键经验教训.
主要方法:
- 开发KinoML框架,用于访问,策划和展示分子数据.
- 实施结构支持的ML方法来预测连接体结合亲缘关系,选择性和耐药性.
- 专注于酶蛋白超级家族,因为它们保留了结构特征.
主要成果:
- KinoML使用户能够执行三个核心任务:数据访问/策划,适合ML的数据特色化和可重复的ML实验.
- 该框架促进了基因酶中结合亲缘关系,选择性和耐药性的结构信息预测.
- 确定了ML平台开发的关键教训:可重复的数据处理,数据协调和适当的数据格式.
结论:
- KinoML为基因酶药物发现中的结构启用ML提供了一个强大的平台,可适应其他蛋白质标.
- 该框架通过整合配体,蛋白质和试验数据来促进可重现的ML实验.
- 从KinoML开发中吸取的教训可以指导在药物发现中创建类似的ML平台.
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