HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise Attention

• URL: http://arxiv.org/abs/2208.13994v1
• Date: Tue, 30 Aug 2022 05:16:15 GMT
• Title: HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise Attention
• Authors: Weimin Zhu, Yi Zhang, DuanCheng Zhao, Jianrong Xu, and Ling Wang
• Abstract summary: We propose a well-designed hierarchical informative graph neural networks framework (termed HiGNN) for predicting molecular property. Experiments demonstrate that HiGNN achieves state-of-the-art predictive performance on many challenging drug discovery-associated benchmark datasets.
• Score: 5.735627221409312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Elucidating and accurately predicting the druggability and bioactivities of molecules plays a pivotal role in drug design and discovery and remains an open challenge. Recently, graph neural networks (GNN) have made remarkable advancements in graph-based molecular property prediction. However, current graph-based deep learning methods neglect the hierarchical information of molecules and the relationships between feature channels. In this study, we propose a well-designed hierarchical informative graph neural networks framework (termed HiGNN) for predicting molecular property by utilizing a co-representation learning of molecular graphs and chemically synthesizable BRICS fragments. Furthermore, a plug-and-play feature-wise attention block is first designed in HiGNN architecture to adaptively recalibrate atomic features after the message passing phase. Extensive experiments demonstrate that HiGNN achieves state-of-the-art predictive performance on many challenging drug discovery-associated benchmark datasets. In addition, we devise a molecule-fragment similarity mechanism to comprehensively investigate the interpretability of HiGNN model at the subgraph level, indicating that HiGNN as a powerful deep learning tool can help chemists and pharmacists identify the key components of molecules for designing better molecules with desired properties or functions. The source code is publicly available at https://github.com/idruglab/hignn.

Related papers

• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [55.42602325017405]
  We propose a novel method called GODE, which takes into account the two-level structure of individual molecules. By pre-training two graph neural networks (GNNs) on different graph structures, combined with contrastive learning, GODE fuses molecular structures with their corresponding knowledge graph substructures. When fine-tuned across 11 chemical property tasks, our model outperforms existing benchmarks, registering an average ROC-AUC uplift of 13.8% for classification tasks and an average RMSE/MAE enhancement of 35.1% for regression tasks.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning [77.31492888819935]
  We propose a novel paradigm of "pre-train, prompt, fine-tune" for molecular representation learning, named molecule continuous prompt tuning (MolCPT) MolCPT defines a motif prompting function that uses the pre-trained model to project the standalone input into an expressive prompt. Experiments on several benchmark datasets show that MolCPT efficiently generalizes pre-trained GNNs for molecular property prediction.
  arXiv  Detail & Related papers  (2022-12-20T19:32:30Z)
• Graph neural networks for the prediction of molecular structure-property relationships [59.11160990637615]
  Graph neural networks (GNNs) are a novel machine learning method that directly work on the molecular graph. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
  arXiv  Detail & Related papers  (2022-07-25T11:30:44Z)
• FunQG: Molecular Representation Learning Via Quotient Graphs [0.0]
  We propose a novel molecular graph coarsening framework named FunQG. FunQG uses Functional groups as influential building blocks of a molecule to determine its properties. We show that the resulting informative graphs are much smaller than the molecular graphs and thus are good candidates for training GNNs.
  arXiv  Detail & Related papers  (2022-07-18T13:36:20Z)
• Molecular Graph Generation via Geometric Scattering [7.796917261490019]
  Graph neural networks (GNNs) have been used extensively for addressing problems in drug design and discovery. We propose a representation-first approach to molecular graph generation. We show that our architecture learns meaningful representations of drug datasets and provides a platform for goal-directed drug synthesis.
  arXiv  Detail & Related papers  (2021-10-12T18:00:23Z)
• Few-Shot Graph Learning for Molecular Property Prediction [46.60746023179724]
  We propose Meta-MGNN, a novel model for few-shot molecular property prediction. To exploit unlabeled molecular information, Meta-MGNN further incorporates molecular structure, attribute based self-supervised modules and self-attentive task weights. Extensive experiments on two public multi-property datasets demonstrate that Meta-MGNN outperforms a variety of state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-16T01:55:34Z)
• Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction [54.93890176891602]
  We propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored to drug-target binding affinity prediction. As a dedicated solution, we first propose a position encoding mechanism to integrate the topological structure and spatial position information into the constructed pocket-ligand graph. We also propose a novel edge-node hierarchical attentive aggregation structure which has edge-level aggregation and node-level aggregation.
  arXiv  Detail & Related papers  (2020-12-17T17:44:01Z)
• Graph Neural Network Architecture Search for Molecular Property Prediction [1.0965065178451106]
  We develop an NAS approach to automate the design and development of graph neural networks (GNNs) for molecular property prediction. Specifically, we focus on automated development of message-passing neural networks (MPNNs) to predict the molecular properties of small molecules in quantum mechanics and physical chemistry data sets.
  arXiv  Detail & Related papers  (2020-08-27T15:30:57Z)
• Self-Supervised Graph Transformer on Large-Scale Molecular Data [73.3448373618865]
  We propose a novel framework, GROVER, for molecular representation learning. GROVER can learn rich structural and semantic information of molecules from enormous unlabelled molecular data. We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.
  arXiv  Detail & Related papers  (2020-06-18T08:37:04Z)
• Multi-View Graph Neural Networks for Molecular Property Prediction [67.54644592806876]
  We present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing architecture. In MV-GNN, we introduce a shared self-attentive readout component and disagreement loss to stabilize the training process. We further boost the expressive power of MV-GNN by proposing a cross-dependent message passing scheme.
  arXiv  Detail & Related papers  (2020-05-17T04:46:07Z)

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.