A Multi-view Molecular Pre-training with Generative Contrastive Learning.

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yunwu Liu, Ruisheng Zhang, Yongna Yuan, Jun Ma, Tongfeng Li, Zhixuan Yu
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引用次数: 0

Abstract

Molecular representation learning can preserve meaningful molecular structures as embedding vectors, which is a necessary prerequisite for molecular property prediction. Yet, learning how to accurately represent molecules remains challenging. Previous approaches to learning molecular representations in an end-to-end manner potentially suffered information loss while neglecting the utilization of molecular generative representations. To obtain rich molecular feature information, the pre-training molecular representation model utilized different molecular representations to reduce information loss caused by a single molecular representation. Therefore, we provide the MVGC, a unique multi-view generative contrastive learning pre-training model. Our pre-training framework specifically acquires knowledge of three fundamental feature representations of molecules and effectively integrates them to predict molecular properties on benchmark datasets. Comprehensive experiments on seven classification tasks and three regression tasks demonstrate that our proposed MVGC model surpasses the majority of state-of-the-art approaches. Moreover, we explore the potential of the MVGC model to learn the representation of molecules with chemical significance.

利用生成式对比学习进行多视角分子预训练。
分子表征学习可以保留有意义的分子结构作为嵌入向量,这是分子特性预测的必要前提。然而,学习如何准确地表示分子仍然具有挑战性。以往以端到端方式学习分子表征的方法可能会造成信息丢失,同时忽略了对分子生成表征的利用。为了获得丰富的分子特征信息,预训练分子表征模型利用了不同的分子表征,以减少单一分子表征造成的信息损失。因此,我们提供了一种独特的多视图生成对比学习预训练模型--MVGC。我们的预训练框架专门获取了分子的三种基本特征表征知识,并将它们有效地整合到基准数据集上预测分子特性。七项分类任务和三项回归任务的综合实验表明,我们提出的 MVGC 模型超越了大多数最先进的方法。此外,我们还探索了 MVGC 模型学习具有化学意义的分子表征的潜力。
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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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