HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks

• URL: http://arxiv.org/abs/2504.02014v1
• Date: Wed, 02 Apr 2025 06:46:28 GMT
• Title: HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks
• Authors: Jiannuo Li, Lan Yao,
• Abstract summary: We propose a drug-target association prediction model based on cross-attention fusion hypergraph neural network.<n>In the prediction stage, a bidirectional multi-head cross-attention mechanism is designed to model intermolecular interactions.<n>Experiments on benchmark datasets show that HCAF-DTA outperforms state of the arts in all three performance evaluation metrics.
• Score: 0.6906005491572401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of the binding affinity between drugs and target proteins is a core task in computer-aided drug design. Existing deep learning methods tend to ignore the information of internal sub-structural features of drug molecules and drug-target interactions, resulting in limited prediction performance. In this paper, we propose a drug-target association prediction model HCAF-DTA based on cross-attention fusion hypergraph neural network. The model innovatively introduces hypergraph representation in the feature extraction stage: drug molecule hypergraphs are constructed based on the tree decomposition algorithm, and the sub-structural and global features extracted by fusing the hypergraph neural network with the graphical neural network through hopping connections, in which the hyper edges can efficiently characterise the functional functional groups and other key chemical features; for the protein feature extraction, a weighted graph is constructed based on the residues predicted by the ESM model contact maps to construct weighted graphs, and multilayer graph neural networks were used to capture spatial dependencies. In the prediction stage, a bidirectional multi-head cross-attention mechanism is designed to model intermolecular interactions from the dual viewpoints of atoms and amino acids, and cross-modal features with correlated information are fused by attention. Experiments on benchmark datasets such as Davis and KIBA show that HCAF-DTA outperforms state of the arts in all three performance evaluation metrics, with the MSE metrics reaching 0.198 and 0.122, respectively, with an improvement of up to 4% from the optimal baseline.

Related papers

• Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction [0.6906005491572401]
  Molecular featurisation refers to the transformation of molecular data into numerical feature vectors. Message-passing graph neural networks (GNNs) have emerged as a novel method to learn differentiable features directly from molecular graphs.
  arXiv  Detail & Related papers  (2024-11-20T20:07:48Z)
• Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure [53.76752789814785]
  DumplingGNN is a hybrid Graph Neural Network architecture specifically designed for predicting ADC payload activity based on chemical structure. We evaluate it on a comprehensive ADC payload dataset focusing on DNA Topoisomerase I inhibitors. It demonstrates exceptional accuracy (91.48%), sensitivity (95.08%), and specificity (97.54%) on our specialized ADC payload dataset.
  arXiv  Detail & Related papers  (2024-09-23T17:11:04Z)
• MKDTI: Predicting drug-target interactions via multiple kernel fusion on graph attention network [37.40418564922425]
  We formulate a model called MKDTI by extracting kernel information from various layer embeddings of a graph attention network. We use a Dual Laplacian Regularized Least Squares framework to forecast novel drug-target entity connections.
  arXiv  Detail & Related papers  (2024-07-14T02:53:25Z)
• A deep graph model for the signed interaction prediction in biological network [1.03121181235382]
  Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing.<n>textbfRGCNTD is designed to predict both polar (e.g. activation, inhibition) and non-polar (e.g. binding, affect) chemical-gene interactions.<n>We introduce new evaluation metrics, textitAUCtextsubscriptpolarity and textitCP@500, to assess the model's ability to differentiate interaction types.
  arXiv  Detail & Related papers  (2024-07-10T04:28:21Z)
• 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)
• Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity [7.023929372010717]
  We propose a novel hierarchical graph representation learning model for the drug-target binding affinity prediction, namely HGRL-DTA. In this paper, we adopt a message broadcasting mechanism to integrate the hierarchical representations learned from the global-level affinity graph and the local-level molecular graph. Besides, we design a similarity-based embedding map to solve the cold start problem of inferring representations for unseen drugs and targets.
  arXiv  Detail & Related papers  (2022-03-22T04:50:16Z)
• MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy [2.446672595462589]
  We propose a multimodal graph neural network that can predict the synergistic effect of drug combinations for cancer treatment. Our model captures the representation based on the context of drugs at multiple scales based on a drug-protein interaction network and metadata. We demonstrate that the model makes high-quality predictions over a wide range of cancer cell line tissues.
  arXiv  Detail & Related papers  (2021-10-28T13:10:25Z)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)
• Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity [52.67037774136973]
  Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. We propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool)
  arXiv  Detail & Related papers  (2021-07-21T03:34:09Z)
• 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)
• Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts [80.69440684790925]
  DeepRelations is a physics-inspired deep relational network with intrinsically explainable architecture. It shows superior interpretability to the state-of-the-art. It boosts the AUPRC of contact prediction 9.5, 16.9, 19.3 and 5.7-fold for the test, compound-unique, protein-unique, and both-unique sets.
  arXiv  Detail & Related papers  (2019-12-29T00:14:07Z)

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.