基于Favorite负抑制和分解片段对比的分子对比学习改进
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast
摘要：
深度学习已经在计算化学中流行，并广泛应用于分子性质预测。近年来，自监督学习（SSL），特别是对比学习（CL），因其具有学习推广到巨大化学空间的分子表征的潜力而受到越来越多的关注。与监督学习不同，SSL可以直接利用大量未标记的数据，这大大减少了通过昂贵且耗时的模拟或实验来获取分子属性标签的工作。然而，大多数分子SSL方法借用了来自机器学习社区的见解，但忽视了独特的化学信息学（例如，分子指纹）和多级图形结构（例如，官能团）的分子。在这项工作中，我们提出了iMoltron：改进的分子对比学习的表示与图神经网络（GNNs）在两个方面，（1）减少错误的负面对比情况下，通过考虑分子对之间的化学信息学相似性;（2）片段水平之间的对比分子内和分子间的子结构分解。实验表明，所提出的策略显着提高GNN模型的性能，对各种具有挑战性的分子性质的预测。与之前的CL框架相比，iMoltron在7个分类基准上的ROC-AUC平均提高了1.3%，在5个回归基准上的误差平均降低了4.8%。在大多数基准测试中，由iMoltron预训练的通用GNN可以与具有复杂架构设计和工程特性的监督学习模型相媲美，甚至超过后者。进一步的研究表明，通过iMolybdenum学习的表征本质上嵌入了可以推理分子相似性的支架和官能团。
英文摘要：
Deep learning has been a prevalence in computational chemistry and widely implemented in molecule property predictions. Recently, self-supervised learning (SSL), especially contrastive learning (CL), gathers growing attention for the potential to learn molecular representations that generalize to the gigantic chemical space. Unlike supervised learning, SSL can directly leverage large unlabeled data, which greatly reduces the effort to acquire molecular property labels through costly and time-consuming simulations or experiments. However, most molecular SSL methods borrow the insights from the machine learning community but neglect the unique cheminformatics (e.g., molecular fingerprints) and multi-level graphical structures (e.g., functional groups) of molecules. In this work, we propose iMolCLR: improvement of Molecular Contrastive Learning of Representations with graph neural networks (GNNs) in two aspects, (1) mitigating faulty negative contrastive instances via considering cheminformatics similarities between molecule pairs; (2) fragment-level contrasting between intra- and inter-molecule substructures decomposed from molecules. Experiments have shown that the proposed strategies significantly improve the performance of GNN models on various challenging molecular property predictions. In comparison to the previous CL framework, iMolCLR demonstrates an averaged 1.3% improvement of ROC-AUC on 7 classification benchmarks and an averaged 4.8% decrease of the error on 5 regression benchmarks. On most benchmarks, the generic GNN pre-trained by iMolCLR rivals or even surpasses supervised learning models with sophisticated architecture designs and engineered features. Further investigations demonstrate that representations learned through iMolCLR intrinsically embed scaffolds and functional groups that can reason molecule similarities.
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