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.

Related papers

• Knowledge-aware contrastive heterogeneous molecular graph learning [77.94721384862699]
  We propose a paradigm shift by encoding molecular graphs into Heterogeneous Molecular Graph Learning (KCHML) KCHML conceptualizes molecules through three distinct graph views-molecular, elemental, and pharmacological-enhanced by heterogeneous molecular graphs and a dual message-passing mechanism. This design offers a comprehensive representation for property prediction, as well as for downstream tasks such as drug-drug interaction (DDI) prediction.
  arXiv  Detail & Related papers  (2025-02-17T11:53:58Z)
• Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks [44.934084652800976]
  We introduce the first MoleculAR Conformer Ensemble Learning benchmark to thoroughly evaluate the potential of learning on conformer ensembles. Our findings reveal that direct learning from an conformer space can improve performance on a variety of tasks and models.
  arXiv  Detail & Related papers  (2023-09-29T20:06:46Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [68.32093648671496]
  We introduce GODE, which accounts for the dual-level structure inherent in molecules. Molecules possess an intrinsic graph structure and simultaneously function as nodes within a broader molecular knowledge graph. By pre-training two GNNs on different graph structures, GODE effectively fuses molecular structures with their corresponding knowledge graph substructures.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning [57.670845619155195]
  We introduce a new model for molecular representation learning called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA) ASBA addresses the limitations of previous atom-wise and subgraph-wise models by incorporating both types of information. Our method offers a more comprehensive way to learn representations for molecular property prediction and has broad potential in drug and material discovery applications.
  arXiv  Detail & Related papers  (2023-05-22T00:56:00Z)
• Learning Harmonic Molecular Representations on Riemannian Manifold [18.49126496517951]
  Molecular representation learning plays a crucial role in AI-assisted drug discovery research. We propose a Harmonic Molecular Representation learning framework, which represents a molecule using the Laplace-Beltrami eigenfunctions of its molecular surface.
  arXiv  Detail & Related papers  (2023-03-27T18:02:47Z)
• Graph Neural Networks for Molecules [9.04563945965023]
  This review introduces GNNs and their various applications for small organic molecules. GNNs rely on message-passing operations, a generic yet powerful framework, to update node features iteratively.
  arXiv  Detail & Related papers  (2022-09-12T20:10:07Z)
• Graph-based Molecular Representation Learning [59.06193431883431]
  Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning.
  arXiv  Detail & Related papers  (2022-07-08T17:43:20Z)
• GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction [47.70253904390288]
  We propose a novel graph contrastive learning method utilizing the geometry of a molecule across 2D and 3D views. Specifically, we first devise a dual-view geometric message passing network (GeomMPNN) to adaptively leverage the rich information of both 2D and 3D graphs of a molecule.
  arXiv  Detail & Related papers  (2021-09-24T03:55:27Z)
• Chemical-Reaction-Aware Molecule Representation Learning [88.79052749877334]
  We propose using chemical reactions to assist learning molecule representation. Our approach is proven effective to 1) keep the embedding space well-organized and 2) improve the generalization ability of molecule embeddings. Experimental results demonstrate that our method achieves state-of-the-art performance in a variety of downstream tasks.
  arXiv  Detail & Related papers  (2021-09-21T00:08:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.