梅洛迪:在前所未有的规模上跨制药联合学习,在不损害专有信息的情况下,在QSAR释放好处
Wouter Heyndrickx1, Lewis Mervin2, Tobias Morawietz3
1Janssen Pharmaceutica NV, Turnhoutseweg 30, Beerse 2340, Belgium.
Journal of chemical information and modeling
|August 29, 2023
概括
联合学习通过利用大量的机密数据集,显著改善了10家制药公司的预测模型. 这种方法提高了模型的预测性,特别是对于复杂的任务,如药理动力学和安全性.
科学领域:
- 计算化学是一种计算化学.
- 机器学习 机器学习
- 药物发现 药物发现
背景情况:
- 联合学习提供了一种有效的方法来增加训练数据量,以提高模型预测性,特别是当数据生成资源密集时.
- 梅洛迪 (MELLODDY) 项目旨在探索在制药领域的联合学习的潜力.
研究的目的:
- 评估联合多合作伙伴机器学习在改善制药行业内预测模型方面的有效性.
- 评估跨多个制药合作伙伴的新型多任务学习实施对模型性能的影响.
- 分析联合学习的预测性表现和适用性领域,使用大规模的跨药物数据集.
主要方法:
- 在十家制药公司中使用了一种新的联合多任务学习实现.
- 利用安全和隐私审计的平台进行数据聚合和模型培训.
- 采用了超过26亿个实验活动数据点的综合数据集,包括小分子和检测结果.
- 开发了补充的指标来评估联邦环境中的预测性绩效.
主要成果:
- 在每个参与制药公司的分类和回归模型中实现了总体改进.
- 通过联合学习,在标记空间中表现出更高的预测性能.
- 观察到联邦学习模型的扩展适用性领域.
- 报告指出,随着集体培训数据量不断增加,预测性绩效的和回报率会增加.
- 报告了基于药理动力学和安全性小组测定任务的明显更高的改善.
结论:
- 联合学习,特别是多任务学习,是提高制药行业预测模型性能的有效策略.
- 该方法成功利用了大量的,机密的跨药品数据集,证明了可扩展性和隐私性.
- 联合学习扩大了机器学习在药物发现中的实用性,特别是在复杂的药理动力学和安全性预测方面.
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