图形神经网络的自适应转移用于短时间的分子性质预测
IEEE/ACM transactions on computational biology and bioinformatics
|October 25, 2023
概括
少数射击分子性质预测 (FSMPP) 面临着数据稀缺. 本研究介绍了ATGNN,这是图形神经网络 (GNN) 的自适应转移框架,以改善药物发现中的新特性知识转移.
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作用
背景情况:
- 短拍子分子性质预测 (FSMPP) 对于药物发现至关重要,其目的是用有限的数据预测新的分子性质.
- 预训练的图形神经网络 (GNN) 在FSMPP中使用,但由于过度适应基础属性,微调可能会降低性能.
研究的目的:
- 为了解决FSMPP中GNN微调的局限性.
- 提出一个新的框架,适应性地从预训练和微调的GNN转移知识,以改进新的财产预测.
主要方法:
- 提出了FSMPP (ATGNN) 的GNN适应转移框架.
- 使用预训练和微调的GNN作为目标属性GNN的模型先验.
- 开发了一个适应任务的重量预测网络,以生成新型属性的目标GNN重量.
主要成果:
- ATGNN有效地以任务适应的方式从GNN先例转移知识.
- 该框架可以提高FSMPP中新型物业预测任务的性能.
- 对Tox21,SIDER,MUV和ToxCast数据集的实验证明了ATGNN的有效性.
结论:
- 拟议的ATGNN框架增强了对短拍子分子性质预测的知识转移.
- ATGNN减轻了GNN微调造成的性能下降,提高了新特性预测的准确性.
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