Learning Universal and Robust 3D Molecular Representations with Graph Convolutional Networks

• URL: http://arxiv.org/abs/2307.12491v1
• Date: Mon, 24 Jul 2023 02:50:19 GMT
• Title: Learning Universal and Robust 3D Molecular Representations with Graph Convolutional Networks
• Authors: Shuo Zhang, Yang Liu, Li Xie, Lei Xie
• Abstract summary: We propose a universal and robust Directional Node Pair (DNP) descriptor based on the graph representations of 3D molecules. Our DNP descriptor is robust compared to previous ones and can be applied to multiple molecular types. We construct the Robust Molecular Graph Convolutional Network (RoM-GCN) which is capable to take both node and edge features into consideration.
• Score: 19.647002925751774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To learn accurate representations of molecules, it is essential to consider both chemical and geometric features. To encode geometric information, many descriptors have been proposed in constrained circumstances for specific types of molecules and do not have the properties to be ``robust": 1. Invariant to rotations and translations; 2. Injective when embedding molecular structures. In this work, we propose a universal and robust Directional Node Pair (DNP) descriptor based on the graph representations of 3D molecules. Our DNP descriptor is robust compared to previous ones and can be applied to multiple molecular types. To combine the DNP descriptor and chemical features in molecules, we construct the Robust Molecular Graph Convolutional Network (RoM-GCN) which is capable to take both node and edge features into consideration when generating molecule representations. We evaluate our model on protein and small molecule datasets. Our results validate the superiority of the DNP descriptor in incorporating 3D geometric information of molecules. RoM-GCN outperforms all compared baselines.

Related papers

• UniIF: Unified Molecule Inverse Folding [67.60267592514381]
  We propose a unified model UniIF for inverse folding of all molecules. Our proposed method surpasses state-of-the-art methods on all tasks.
  arXiv  Detail & Related papers  (2024-05-29T10:26:16Z)
• 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)
• MUDiff: Unified Diffusion for Complete Molecule Generation [104.7021929437504]
  We present a new model for generating a comprehensive representation of molecules, including atom features, 2D discrete molecule structures, and 3D continuous molecule coordinates. We propose a novel graph transformer architecture to denoise the diffusion process. Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
  arXiv  Detail & Related papers  (2023-04-28T04:25:57Z)
• An Equivariant Generative Framework for Molecular Graph-Structure Co-Design [54.92529253182004]
  We present MolCode, a machine learning-based generative framework for underlineMolecular graph-structure underlineCo-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Our investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design.
  arXiv  Detail & Related papers  (2023-04-12T13:34:22Z)
• 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)
• MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties [1.231476564107544]
  graph neural network (GNN) has been a powerful deep-learning tool in chemistry domain. MolNet model is chemically intuitive, accommodating the 3D non-bond information in a molecule. MolNet gives a state-of-the-art performance in the classification task of BACE dataset and regression task of ESOL dataset.
  arXiv  Detail & Related papers  (2022-02-01T20:47:28Z)
• Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning [68.8204255655161]
  We introduce a novel framework for scalable 3D design that uses a hierarchical agent to build molecules. In a variety of experiments, we show that our agent, guided only by energy considerations, can efficiently learn to produce molecules with over 100 atoms.
  arXiv  Detail & Related papers  (2022-02-01T18:54:24Z)
• MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks [11.994553575596228]
  MolCLR is a self-supervised learning framework for large unlabeled molecule datasets. We propose three novel molecule graph augmentations: atom masking, bond deletion, and subgraph removal. Our method achieves state-of-the-art performance on many challenging datasets.
  arXiv  Detail & Related papers  (2021-02-19T17:35:18Z)
• Investigating 3D Atomic Environments for Enhanced QSAR [0.0]
  Predicting bioactivity and physical properties of molecules is a longstanding challenge in drug design. Most approaches use molecular descriptors based on a 2D representation of molecules as a graph of atoms and bonds, abstracting away the molecular shape. We describe a novel alignment-free 3D QSAR method using Smooth Overlap of Atomic Positions (SOAP), a well-established formalism developed for interpolating potential energy surfaces.
  arXiv  Detail & Related papers  (2020-10-24T10:04:48Z)
• Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties [12.897488702184306]
  We introduce a novel graph representation of molecules, heterogeneous molecular graph (HMG) HMGNN incorporates global molecule representations and an attention mechanism into the prediction process. Our model achieves state-of-the-art performance in 9 out of 12 tasks on the QM9 dataset.
  arXiv  Detail & Related papers  (2020-09-26T23:29:41Z)

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.