Qian Xiaoliang, Ju Bin, Shen Ping, Yang Keda, Li Li, Liu Qi
Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
SanOmics AI Co., Ltd., Hangzhou 311103, China.
ACS Omega. 2024 May 23;9(22):23940-23948. doi: 10.1021/acsomega.4c02147. eCollection 2024 Jun 4.
Molecular property prediction holds significant importance in drug discovery, enabling the identification of biologically active compounds with favorable drug-like properties. However, the low data problem, arising from the scarcity of labeled data in drug discovery, poses a substantial obstacle for accurate predictions. To address this challenge, we introduce a novel architecture, AttFPGNN-MAML, for few-shot molecular property prediction. The proposed approach incorporates a hybrid feature representation to enrich molecular representations and model intermolecular relationships specific to the task. By leveraging ProtoMAML, a meta-learning strategy, our model is trained and adapted to new tasks. Evaluation on two few-shot data sets, MoleculeNet and FS-Mol, demonstrates our method's superior performance in three out of four tasks and across various support set sizes. These results convincingly validate the effectiveness of our method in the realm of few-shot molecular property prediction. The source code is publicly available at https://github.com/sanomics-lab/AttFPGNN-MAML.
分子性质预测在药物发现中具有重要意义，能够识别具有良好类药物性质的生物活性化合物。然而，由于药物发现中标记数据稀缺而产生的低数据问题，对准确预测构成了重大障碍。为应对这一挑战，我们引入了一种用于少样本分子性质预测的新型架构AttFPGNN-MAML。所提出的方法结合了混合特征表示，以丰富分子表示并对特定任务的分子间关系进行建模。通过利用元学习策略ProtoMAML，我们的模型得到训练并适应新任务。在两个少样本数据集MoleculeNet和FS-Mol上的评估表明，我们的方法在四项任务中的三项以及各种支持集大小下均表现出卓越性能。这些结果令人信服地验证了我们的方法在少样本分子性质预测领域的有效性。源代码可在https://github.com/sanomics-lab/AttFPGNN-MAML上公开获取。
