1
|
Tang X, Lei X, Zhang Y. Prediction of Drug-Target Affinity Using Attention Neural Network. Int J Mol Sci 2024; 25:5126. [PMID: 38791165 PMCID: PMC11121300 DOI: 10.3390/ijms25105126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/19/2024] [Accepted: 04/27/2024] [Indexed: 05/26/2024] Open
Abstract
Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational DTA prediction methods have emerged as indispensable tools for this research. In this article, we propose a novel deep learning algorithm named GRA-DTA, for DTA prediction. Specifically, we introduce Bidirectional Gated Recurrent Unit (BiGRU) combined with a soft attention mechanism to learn target representations. We employ Graph Sample and Aggregate (GraphSAGE) to learn drug representation, especially to distinguish the different features of drug and target representations and their dimensional contributions. We merge drug and target representations by an attention neural network (ANN) to learn drug-target pair representations, which are fed into fully connected layers to yield predictive DTA. The experimental results showed that GRA-DTA achieved mean squared error of 0.142 and 0.225 and concordance index reached 0.897 and 0.890 on the benchmark datasets KIBA and Davis, respectively, surpassing the most state-of-the-art DTA prediction algorithms.
Collapse
Affiliation(s)
- Xin Tang
- School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
| | - Yuchen Zhang
- College of Information Engineering, Northwest A&F University, Xianyang 712199, China;
| |
Collapse
|
2
|
Kpanou R, Dallaire P, Rousseau E, Corbeil J. Learning self-supervised molecular representations for drug-drug interaction prediction. BMC Bioinformatics 2024; 25:47. [PMID: 38291362 PMCID: PMC10829170 DOI: 10.1186/s12859-024-05643-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/05/2024] [Indexed: 02/01/2024] Open
Abstract
Drug-drug interactions (DDI) are a critical concern in healthcare due to their potential to cause adverse effects and compromise patient safety. Supervised machine learning models for DDI prediction need to be optimized to learn abstract, transferable features, and generalize to larger chemical spaces, primarily due to the scarcity of high-quality labeled DDI data. Inspired by recent advances in computer vision, we present SMR-DDI, a self-supervised framework that leverages contrastive learning to embed drugs into a scaffold-based feature space. Molecular scaffolds represent the core structural motifs that drive pharmacological activities, making them valuable for learning informative representations. Specifically, we pre-trained SMR-DDI on a large-scale unlabeled molecular dataset. We generated augmented views for each molecule via SMILES enumeration and optimized the embedding process through contrastive loss minimization between views. This enables the model to capture relevant and robust molecular features while reducing noise. We then transfer the learned representations for the downstream prediction of DDI. Experiments show that the new feature space has comparable expressivity to state-of-the-art molecular representations and achieved competitive DDI prediction results while training on less data. Additional investigations also revealed that pre-training on more extensive and diverse unlabeled molecular datasets improved the model's capability to embed molecules more effectively. Our results highlight contrastive learning as a promising approach for DDI prediction that can identify potentially hazardous drug combinations using only structural information.
Collapse
Affiliation(s)
- Rogia Kpanou
- Département d'informatique et Génie Logiciel, Université Laval, Québec City, QC, Canada.
| | - Patrick Dallaire
- Département d'informatique et Génie Logiciel, Université Laval, Québec City, QC, Canada
| | - Elsa Rousseau
- Département d'informatique et Génie Logiciel, Université Laval, Québec City, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Québec City, QC, Canada
- Centre Nutrition, Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec City, QC, Canada
| | - Jacques Corbeil
- Centre de Recherche en Données Massives de l'Université Laval, Québec City, QC, Canada.
- Centre de Recherche en Infectiologie de l'Université Laval, Axe Maladies Infectieuses et Immunitaires, Centre de Recherche du CHU de Québec-Université Laval, Québec City, QC, Canada.
- Département de Médecine Moléculaire, Faculté de Médecine, Université Laval, Québec City, QC, Canada.
| |
Collapse
|
3
|
Zhang R, Wang X, Wang P, Meng Z, Cui W, Zhou Y. HTCL-DDI: a hierarchical triple-view contrastive learning framework for drug-drug interaction prediction. Brief Bioinform 2023; 24:bbad324. [PMID: 37742052 DOI: 10.1093/bib/bbad324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/26/2023] [Accepted: 08/24/2023] [Indexed: 09/25/2023] Open
Abstract
Drug-drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interact with each other, sparking an increasing demand for computational methods of DDI prediction. However, existing computational DDI methods mostly rely on the single-view paradigm, failing to handle the complex features and intricate patterns of DDIs due to the limited expressiveness of the single view. To this end, we propose a Hierarchical Triple-view Contrastive Learning framework for Drug-Drug Interaction prediction (HTCL-DDI), leveraging the molecular, structural and semantic views to model the complicated information involved in DDI prediction. To aggregate the intra-molecular compositional and structural information, we present a dual attention-aware network in the molecular view. Based on the molecular view, to further capture inter-molecular information, we utilize the one-hop neighboring information and high-order semantic relations in the structural view and semantic view, respectively. Then, we introduce contrastive learning to enhance drug representation learning from multifaceted aspects and improve the robustness of HTCL-DDI. Finally, we conduct extensive experiments on three real-world datasets. All the experimental results show the significant improvement of HTCL-DDI over the state-of-the-art methods, which also demonstrates that HTCL-DDI opens new avenues for ensuring medication safety and identifying synergistic drug combinations.
Collapse
Affiliation(s)
- Ran Zhang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xuezhi Wang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Pengfei Wang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhen Meng
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wenjuan Cui
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuanchun Zhou
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| |
Collapse
|
4
|
Zhang M, Gao H, Liao X, Ning B, Gu H, Yu B. DBGRU-SE: predicting drug-drug interactions based on double BiGRU and squeeze-and-excitation attention mechanism. Brief Bioinform 2023:7176312. [PMID: 37225428 DOI: 10.1093/bib/bbad184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/03/2023] [Accepted: 04/23/2023] [Indexed: 05/26/2023] Open
Abstract
The prediction of drug-drug interactions (DDIs) is essential for the development and repositioning of new drugs. Meanwhile, they play a vital role in the fields of biopharmaceuticals, disease diagnosis and pharmacological treatment. This article proposes a new method called DBGRU-SE for predicting DDIs. Firstly, FP3 fingerprints, MACCS fingerprints, Pubchem fingerprints and 1D and 2D molecular descriptors are used to extract the feature information of the drugs. Secondly, Group Lasso is used to remove redundant features. Then, SMOTE-ENN is applied to balance the data to obtain the best feature vectors. Finally, the best feature vectors are fed into the classifier combining BiGRU and squeeze-and-excitation (SE) attention mechanisms to predict DDIs. After applying five-fold cross-validation, The ACC values of DBGRU-SE model on the two datasets are 97.51 and 94.98%, and the AUC are 99.60 and 98.85%, respectively. The results showed that DBGRU-SE had good predictive performance for drug-drug interactions.
Collapse
Affiliation(s)
| | - Hongli Gao
- Qingdao University of Science and Technology, China
| | - Xin Liao
- Qingdao University of Science and Technology, China
| | - Baoxing Ning
- Qingdao University of Science and Technology, China
| | - Haiming Gu
- Qingdao University of Science and Technology, China
| | - Bin Yu
- Qingdao University of Science and Technology, China
| |
Collapse
|