Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules

• URL: http://arxiv.org/abs/2107.06773v1
• Date: Sun, 4 Jul 2021 15:56:02 GMT
• Title: Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules
• Authors: Yan Ding, Xiaoqian Jiang and Yejin Kim
• Abstract summary: The evaluation of the BBB penetrating ability of drug molecules is a critical step in brain drug development. We employ the relational graph convolutional network (RGCN) to handle the drug-protein relations as well as the features of each individual drug. The performance was already promising, demonstrating the significant role of the drug-protein/drug relations in the prediction of BBB permeability.
• Score: 12.041672273431994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evaluation of the BBB penetrating ability of drug molecules is a critical step in brain drug development. Computational prediction based on machine learning has proved to be an efficient way to conduct the evaluation. However, performance of the established models has been limited by their incapability of dealing with the interactions between drugs and proteins, which play an important role in the mechanism behind BBB penetrating behaviors. To address this issue, we employed the relational graph convolutional network (RGCN) to handle the drug-protein (denoted by the encoding gene) relations as well as the features of each individual drug. In addition, drug-drug similarity was also introduced to connect structurally similar drugs in the graph. The RGCN model was initially trained without input of any drug features. And the performance was already promising, demonstrating the significant role of the drug-protein/drug-drug relations in the prediction of BBB permeability. Moreover, molecular embeddings from a pre-trained knowledge graph were used as the drug features to further enhance the predictive ability of the model. Finally, the best performing RGCN model was built with a large number of unlabeled drugs integrated into the graph.

Related papers

• GramSeq-DTA: A grammar-based drug-target affinity prediction approach fusing gene expression information [1.2289361708127877]
  We propose GramSeq-DTA, which integrates chemical perturbation information with the structural information of drugs and targets. Our approach outperforms the current state-of-the-art DTA prediction models when validated on widely used DTA datasets.
  arXiv  Detail & Related papers  (2024-11-03T03:17:09Z)
• Regressor-free Molecule Generation to Support Drug Response Prediction [83.25894107956735]
  Conditional generation based on the target IC50 score can obtain a more effective sampling space. Regressor-free guidance combines a diffusion model's score estimation with a regression controller model's gradient based on number labels.
  arXiv  Detail & Related papers  (2024-05-23T13:22:17Z)
• drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network [9.637695046701493]
  drGAT is a graph deep learning model that can predict sensitivity to drugs. drGAT has superior performance over existing models, achieving 78% accuracy and 76% F1 score for 269 DNA-damaging compounds. Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients.
  arXiv  Detail & Related papers  (2024-05-14T22:16:52Z)
• Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction [50.7901190642594]
  We propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction. BioKDN refines the reliable structure of local subgraphs by denoising noisy links in a learnable manner. It maintains consistent and robust semantics by smoothing relations around the target interaction.
  arXiv  Detail & Related papers  (2023-12-09T07:08:00Z)
• Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network [69.16939798838159]
  We propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.
  arXiv  Detail & Related papers  (2023-11-15T06:34:00Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension [18.34651382394962]
  This paper presents a medical knowledge graph question answering model, dubbed MedKGQA. It predicts drug-drug interaction by employing machine reading comprehension from closed-domain literature and constructing a knowledge graph of drug-protein triplets from open-domain documents. The proposed model achieved a 4.5% improvement in terms of drug-drug interaction prediction accuracy compared to previous state-of-the-art models on the Qangaroo MedHop dataset.
  arXiv  Detail & Related papers  (2022-12-19T12:24:32Z)
• DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm [0.521420263116111]
  We introduce a Graph Neural Network (textitGNN) based model for drug synergy prediction. In contrast to conventional models, our GNN-based approach learns task-specific drug representations directly from the graph structure of the drugs. Our work suggests that learning task-specific drug representations and leveraging a diverse dataset is a promising approach to advancing our understanding of drug-drug interaction and synergy.
  arXiv  Detail & Related papers  (2022-10-03T10:16:29Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)
• 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)
• DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations [0.9854322576538699]
  computational screening has become an important way to prioritize drug combinations. DeepDDS was superior to competitive methods by more than 16% predictive precision.
  arXiv  Detail & Related papers  (2021-07-06T08:25:43Z)

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.