ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction

• URL: http://arxiv.org/abs/2106.06130v1
• Date: Fri, 11 Jun 2021 02:35:53 GMT
• Title: ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction
• Authors: Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang
• Abstract summary: We propose a novel Geometry Enhanced Molecular representation learning method (GEM) for Chemical Representation Learning (ChemRL) At first, we design a geometry-based GNN architecture that simultaneously models atoms, bonds, and bond angles in a molecule. On top of the devised GNN architecture, we propose several novel geometry-level self-supervised learning strategies to learn spatial knowledge.
• Score: 25.49976851499949
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Effective molecular representation learning is of great importance to facilitate molecular property prediction, which is a fundamental task for the drug and material industry. Recent advances in graph neural networks (GNNs) have shown great promise in applying GNNs for molecular representation learning. Moreover, a few recent studies have also demonstrated successful applications of self-supervised learning methods to pre-train the GNNs to overcome the problem of insufficient labeled molecules. However, existing GNNs and pre-training strategies usually treat molecules as topological graph data without fully utilizing the molecular geometry information. Whereas, the three-dimensional (3D) spatial structure of a molecule, a.k.a molecular geometry, is one of the most critical factors for determining molecular physical, chemical, and biological properties. To this end, we propose a novel Geometry Enhanced Molecular representation learning method (GEM) for Chemical Representation Learning (ChemRL). At first, we design a geometry-based GNN architecture that simultaneously models atoms, bonds, and bond angles in a molecule. To be specific, we devised double graphs for a molecule: The first one encodes the atom-bond relations; The second one encodes bond-angle relations. Moreover, on top of the devised GNN architecture, we propose several novel geometry-level self-supervised learning strategies to learn spatial knowledge by utilizing the local and global molecular 3D structures. We compare ChemRL-GEM with various state-of-the-art (SOTA) baselines on different molecular benchmarks and exhibit that ChemRL-GEM can significantly outperform all baselines in both regression and classification tasks. For example, the experimental results show an overall improvement of $8.8\%$ on average compared to SOTA baselines on the regression tasks, demonstrating the superiority of the proposed method.

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