使用QSAR模型开发转移学习的标准化方法:用于源任务选择的纯数据驱动方法
L Melo1, L Scotti1, M T Scotti1
1Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, Brazil.
SAR and QSAR in environmental research
|February 5, 2024
概括
本研究引入了一种基于数据的方法,用于选择化学应用的转移学习中的源任务,提高精度135倍. 这种方法消除了对领域专业知识的需求,使转移学习更容易获得和更有效.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的应用.
背景情况:
- 转移学习对于化学终点是有效的,但受到在源任务选择中需要领域专业知识的限制.
- 现有的方法需要大量的先验知识,这阻碍了转移学习的更广泛应用.
研究的目的:
- 开发一种纯粹基于数据的方法来选择转移学习中的最佳源任务,消除对领域知识的需求.
- 创建可转移性的预测模型,以指导有效的源任务的选择.
主要方法:
- 建立了一个监督学习框架,以预测转移结果 (积极/消极).
- 使用源和目标数据集信息作为预测特征计算了六个可转移性指标.
- 一个可转移性预测模型 (TP-Model) 在100,000个来自ChEMBL数据库的随机转移上进行了训练和评估.
主要成果:
- TP模型的精度提高了135倍,灵敏度达到92%,显著超过随机搜索.
- 转移学习应用程序导致马修斯相关系数 (MCC) 平均增加0.19 (单源) 和0.44 (多源).
结论:
- 开发的数据驱动TP模型有效地确定了适合转移化学信息学学习的源任务.
- 这种方法提高了转移学习的效率和适用性,导致化学终点预测的性能大幅提高.
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