推进药物发现预训练模型的边界:可解释的微调,由分子物理化学特性赋能
IEEE journal of biomedical and health informatics
|June 18, 2024
概括
本研究介绍了头部功能并行适配器 (HFPA),这是一种用于药物发现中预训练模型的新型微调方法. HFPA提高了模型的解释性和性能,同时减少了参数数量,以实现高效的集成.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 机器学习用于分子建模.
背景情况:
- 预先训练有素的模型在药物发现方面表现有前途,但由于大尺寸和解释性差,它们受到影响.
- 目前的微调方法限制了这些强大的模型在药物开发管道中的实际应用.
研究的目的:
- 为在药物发现中预先训练的模型设计一种新,可解释和参数高效的微调范式.
- 提高在分子研发中大型预训练模型的整合性和适用性.
主要方法:
- 引入了头部功能并行适配器 (HFPA),这是一种微调方法,可以结骨干模型.
- 在特征空间内使用小型适配器,同时探索各种原子和化学键知识.
- 专注于保持少量的可训练参数,并提高模型的可解释性.
主要成果:
- 在七个生理学和毒性预测任务中,HFPA表现出卓越的性能.
- 在三个物理化学任务中取得了最先进的结果.
- 在10个额外的分子数据集中验证了HFPA的稳定性和广泛适用性.
结论:
- HFPA提供了一种高度可解释和高效的方法,用于微调药物发现的预训练模型.
- 该方法显著提高了各种分子任务的性能,为更广泛的实地采用铺平了道路.
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