Property-aware Adaptive Relation Networks for Molecular Property Prediction

• URL: http://arxiv.org/abs/2107.07994v1
• Date: Fri, 16 Jul 2021 16:22:30 GMT
• Title: Property-aware Adaptive Relation Networks for Molecular Property Prediction
• Authors: Yaqing Wang, Abulikemu Abuduweili, Dejing Dou
• Abstract summary: We propose a property-aware adaptive relation networks (PAR) for the few-shot molecular property prediction problem. Our PAR is compatible with existing graph-based molecular encoders, and are further equipped with the ability to obtain property-aware molecular embedding and model molecular relation graph.
• Score: 34.13439007658925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecular property prediction plays a fundamental role in drug discovery to discover candidate molecules with target properties. However, molecular property prediction is essentially a few-shot problem which makes it hard to obtain regular models. In this paper, we propose a property-aware adaptive relation networks (PAR) for the few-shot molecular property prediction problem. In comparison to existing works, we leverage the facts that both substructures and relationships among molecules are different considering various molecular properties. Our PAR is compatible with existing graph-based molecular encoders, and are further equipped with the ability to obtain property-aware molecular embedding and model molecular relation graph adaptively. The resultant relation graph also facilitates effective label propagation within each task. Extensive experiments on benchmark molecular property prediction datasets show that our method consistently outperforms state-of-the-art methods and is able to obtain property-aware molecular embedding and model molecular relation graph properly.

Related papers

• MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis [18.940529282539842]
  We construct a large-scale and precise molecular representation dataset of approximately 140,000 small molecules. Our dataset offers significant physicochemical interpretability to guide model development and design. We believe this dataset will serve as a more accurate and reliable benchmark for molecular representation learning.
  arXiv  Detail & Related papers  (2024-06-13T02:50:23Z)
• Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry [6.049566024728809]
  Deep learning-based molecular property prediction has emerged as a solution to the resource-intensive nature of traditional methods. In this paper, we propose a novel multi-modal representation learning model, called SGGRL, for molecular property prediction. To ensure consistency across modalities, SGGRL is trained to maximize the similarity of representations for the same molecule while minimizing similarity for different molecules.
  arXiv  Detail & Related papers  (2024-01-07T02:18:00Z)
• MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures [2.5563339057415218]
  MolIG is a novel MultiModaL molecular pre-training framework for predicting molecular properties based on Image and Graph structures. It amalgamates the strengths of both molecular representation forms. It exhibits enhanced performance in downstream tasks pertaining to molecular property prediction within benchmark groups.
  arXiv  Detail & Related papers  (2023-11-28T10:28:35Z)
• Atom-Motif Contrastive Transformer for Molecular Property Prediction [68.85399466928976]
  Graph Transformer (GT) models have been widely used in the task of Molecular Property Prediction (MPP) We propose a novel Atom-Motif Contrastive Transformer (AMCT) which explores atom-level interactions and considers motif-level interactions. Our proposed AMCT is extensively evaluated on seven popular benchmark datasets, and both quantitative and qualitative results firmly demonstrate its effectiveness.
  arXiv  Detail & Related papers  (2023-10-11T10:03:10Z)
• Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting [53.44684898432997]
  Generation of molecules with desired chemical and biological properties is critical for drug discovery. We propose a probabilistic generative model to capture the joint distribution of molecules and their properties. Our method achieves very strong performances on various molecule design tasks.
  arXiv  Detail & Related papers  (2023-06-09T03:04:21Z)
• 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)
• 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)
• Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction [53.06671763877109]
  We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations. On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
  arXiv  Detail & Related papers  (2023-02-04T01:32:40Z)
• 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)
• Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation [16.08677447593939]
  We propose a dual-branched neural network for molecular property prediction based on message-passing framework. Our model learns heterogeneous molecular features with different scales, which are trained flexibly according to each prediction target.
  arXiv  Detail & Related papers  (2021-06-14T10:00:39Z)

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.