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Jiang Z, Gong Z, Dai X, Zhang H, Ding P, Shen C. Deep graph contrastive learning model for drug-drug interaction prediction. PLoS One 2024; 19:e0304798. [PMID: 38885206 PMCID: PMC11182529 DOI: 10.1371/journal.pone.0304798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/17/2024] [Indexed: 06/20/2024] Open
Abstract
Drug-drug interaction (DDI) is the combined effects of multiple drugs taken together, which can either enhance or reduce each other's efficacy. Thus, drug interaction analysis plays an important role in improving treatment effectiveness and patient safety. It has become a new challenge to use computational methods to accelerate drug interaction time and reduce its cost-effectiveness. The existing methods often do not fully explore the relationship between the structural information and the functional information of drug molecules, resulting in low prediction accuracy for drug interactions, poor generalization, and other issues. In this paper, we propose a novel method, which is a deep graph contrastive learning model for drug-drug interaction prediction (DeepGCL for brevity). DeepGCL incorporates a contrastive learning component to enhance the consistency of information between different views (molecular structure and interaction network), which means that the DeepGCL model predicts drug interactions by integrating molecular structure features and interaction network topology features. Experimental results show that DeepGCL achieves better performance than other methods in all datasets. Moreover, we conducted many experiments to analyze the necessity of each component of the model and the robustness of the model, which also showed promising results. The source code of DeepGCL is freely available at https://github.com/jzysj/DeepGCL.
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Affiliation(s)
- Zhenyu Jiang
- College of Information and Intelligence, Hunan Agricultural University, Changsha, China
| | - Zhi Gong
- School of Computer Science and Engineering, Hunan University of Information Technology, Changsha, China
- Key Laboratory of Intelligent Perception and Computing, Hunan University of Information Technology, Changsha, China
| | - Xiaopeng Dai
- College of Information and Intelligence, Hunan Agricultural University, Changsha, China
- School of Computer Science and Engineering, Hunan University of Information Technology, Changsha, China
- Key Laboratory of Intelligent Perception and Computing, Hunan University of Information Technology, Changsha, China
| | - Hongyan Zhang
- College of Information and Intelligence, Hunan Agricultural University, Changsha, China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang, China
| | - Cong Shen
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
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2
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Zhu J, Che C, Jiang H, Xu J, Yin J, Zhong Z. SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug-drug interaction prediction. BMC Bioinformatics 2024; 25:39. [PMID: 38262923 PMCID: PMC10810255 DOI: 10.1186/s12859-024-05654-4] [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: 10/20/2023] [Accepted: 01/12/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Drug-drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict and identify potential DDI, they often overlook the sequence information of drug molecules and fail to comprehensively consider the contribution of molecular substructures to DDI. RESULTS In this paper, we proposed a novel model for DDI prediction based on sequence and substructure features (SSF-DDI) to address these issues. Our model integrates drug sequence features and structural features from the drug molecule graph, providing enhanced information for DDI prediction and enabling a more comprehensive and accurate representation of drug molecules. CONCLUSION The results of experiments and case studies have demonstrated that SSF-DDI significantly outperforms state-of-the-art DDI prediction models across multiple real datasets and settings. SSF-DDI performs better in predicting DDI involving unknown drugs, resulting in a 5.67% improvement in accuracy compared to state-of-the-art methods.
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Affiliation(s)
- Jing Zhu
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian, 116000, China
| | - Chao Che
- School of Software Engineering, Dalian University, Dalian, 116000, China
| | - Hao Jiang
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian, 116000, China
| | - Jian Xu
- General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116000, China
| | - Jiajun Yin
- General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116000, China
| | - Zhaoqian Zhong
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian, 116000, China.
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3
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Gao J, Shen Z, Xie Y, Lu J, Lu Y, Chen S, Bian Q, Guo Y, Shen L, Wu J, Zhou B, Hou T, He Q, Che J, Dong X. TransFoxMol: predicting molecular property with focused attention. Brief Bioinform 2023; 24:bbad306. [PMID: 37605947 DOI: 10.1093/bib/bbad306] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/17/2023] [Accepted: 08/04/2023] [Indexed: 08/23/2023] Open
Abstract
Predicting the biological properties of molecules is crucial in computer-aided drug development, yet it's often impeded by data scarcity and imbalance in many practical applications. Existing approaches are based on self-supervised learning or 3D data and using an increasing number of parameters to improve performance. These approaches may not take full advantage of established chemical knowledge and could inadvertently introduce noise into the respective model. In this study, we introduce a more elegant transformer-based framework with focused attention for molecular representation (TransFoxMol) to improve the understanding of artificial intelligence (AI) of molecular structure property relationships. TransFoxMol incorporates a multi-scale 2D molecular environment into a graph neural network + Transformer module and uses prior chemical maps to obtain a more focused attention landscape compared to that obtained using existing approaches. Experimental results show that TransFoxMol achieves state-of-the-art performance on MoleculeNet benchmarks and surpasses the performance of baselines that use self-supervised learning or geometry-enhanced strategies on small-scale datasets. Subsequent analyses indicate that TransFoxMol's predictions are highly interpretable and the clever use of chemical knowledge enables AI to perceive molecules in a simple but rational way, enhancing performance.
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Affiliation(s)
- Jian Gao
- Hangzhou Institute of Innovative Medicine, Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Zheyuan Shen
- Hangzhou Institute of Innovative Medicine, Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yufeng Xie
- School of Software Technology, Zhejiang University, Hangzhou, China
| | - Jialiang Lu
- Hangzhou Institute of Innovative Medicine, Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yang Lu
- Hangzhou Institute of Innovative Medicine, Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Sikang Chen
- Hangzhou Institute of Innovative Medicine, Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Qingyu Bian
- Hangzhou Institute of Innovative Medicine, Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yue Guo
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Liteng Shen
- Hangzhou Institute of Innovative Medicine, Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jian Wu
- School of Software Technology, Zhejiang University, Hangzhou, China
| | - Binbin Zhou
- Department of Computer Science and Computing, Zhejiang University City College, Hangzhou, China
| | - Tingjun Hou
- State Key Lab of CAD&CG, College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Qiaojun He
- Institute of Pharmacology & Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, PR China
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
- Centre for Drug Safety Evaluation and Research of ZJU, Hangzhou, 310058, PR China
- Cancer Center of Zhejiang University, Hangzhou, China
| | - Jinxin Che
- Hangzhou Institute of Innovative Medicine, Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xiaowu Dong
- Hangzhou Institute of Innovative Medicine, Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
- Cancer Center of Zhejiang University, Hangzhou, China
- Department of Pharmacy, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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4
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Babaiha NS, Aghdam R, Ghiam S, Eslahchi C. NN-RNALoc: Neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profiles. PLoS One 2023; 18:e0258793. [PMID: 37708177 PMCID: PMC10501558 DOI: 10.1371/journal.pone.0258793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 05/12/2023] [Indexed: 09/16/2023] Open
Abstract
The localization of messenger RNAs (mRNAs) is a frequently observed phenomenon and a crucial aspect of gene expression regulation. It is also a mechanism for targeting proteins to a specific cellular region. Moreover, prior research and studies have shown the significance of intracellular RNA positioning during embryonic and neural dendrite formation. Incorrect RNA localization, which can be caused by a variety of factors, such as mutations in trans-regulatory elements, has been linked to the development of certain neuromuscular diseases and cancer. In this study, we introduced NN-RNALoc, a neural network-based method for predicting the cellular location of mRNA using novel features extracted from mRNA sequence data and protein interaction patterns. In fact, we developed a distance-based subsequence profile for RNA sequence representation that is more memory and time-efficient than well-known k-mer sequence representation. Combining protein-protein interaction data, which is essential for numerous biological processes, with our novel distance-based subsequence profiles of mRNA sequences produces more accurate features. On two benchmark datasets, CeFra-Seq and RNALocate, the performance of NN-RNALoc is compared to powerful predictive models proposed in previous works (mRNALoc, RNATracker, mLoc-mRNA, DM3Loc, iLoc-mRNA, and EL-RMLocNet), and a ground neural (DNN5-mer) network. Compared to the previous methods, NN-RNALoc significantly reduces computation time and also outperforms them in terms of accuracy. This study's source code and datasets are freely accessible at https://github.com/NeginBabaiha/NN-RNALoc.
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Affiliation(s)
- Negin Sadat Babaiha
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
| | - Rosa Aghdam
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, United States of America
| | - Shokoofeh Ghiam
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Changiz Eslahchi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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Masumshah R, Eslahchi C. DPSP: a multimodal deep learning framework for polypharmacy side effects prediction. BIOINFORMATICS ADVANCES 2023; 3:vbad110. [PMID: 37701676 PMCID: PMC10493180 DOI: 10.1093/bioadv/vbad110] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/01/2023] [Accepted: 08/15/2023] [Indexed: 09/14/2023]
Abstract
Motivation Because unanticipated drug-drug interactions (DDIs) can result in severe bodily harm, identifying the adverse effects of polypharmacy is one of the most important tasks in human health. Over the past few decades, computational methods for predicting the adverse effects of polypharmacy have been developed. Results This article presents DPSP, a framework for predicting polypharmacy side effects based on the construction of novel drug features and the application of a deep neural network to predict DDIs. In the first step, a variety of drug information is evaluated, and a feature extraction method and the Jaccard similarity are used to determine similarities between two drugs. By combining these similarities, a novel feature vector is generated for each drug. In the second step, the method predicts DDIs for specific DDI events using a multimodal framework and drug feature vectors. On three benchmark datasets, the performance of DPSP is measured by comparing its results to those of several well-known methods, such as GNN-DDI, MSTE, MDF-SA-DDI, NNPS, DDIMDL, DNN, DeepDDI, KNN, LR, and RF. DPSP outperforms these classification methods based on a variety of classification metrics. The results indicate that the use of diverse drug information is effective and efficient for identifying DDI adverse effects. Availability and implementation The source code and datasets are available at https://github.com/raziyehmasumshah/DPSP.
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Affiliation(s)
- Raziyeh Masumshah
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran 1983969411, Iran
| | - Changiz Eslahchi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran 1983969411, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran 193955746, Iran
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Wang H, Dai C, Wen Y, Wang X, Liu W, He S, Bo X, Peng S. GADRP: graph convolutional networks and autoencoders for cancer drug response prediction. Brief Bioinform 2023; 24:6865039. [PMID: 36460622 DOI: 10.1093/bib/bbac501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/19/2022] [Accepted: 10/22/2022] [Indexed: 12/04/2022] Open
Abstract
Drug response prediction in cancer cell lines is of great significance in personalized medicine. In this study, we propose GADRP, a cancer drug response prediction model based on graph convolutional networks (GCNs) and autoencoders (AEs). We first use a stacked deep AE to extract low-dimensional representations from cell line features, and then construct a sparse drug cell line pair (DCP) network incorporating drug, cell line, and DCP similarity information. Later, initial residual and layer attention-based GCN (ILGCN) that can alleviate over-smoothing problem is utilized to learn DCP features. And finally, fully connected network is employed to make prediction. Benchmarking results demonstrate that GADRP can significantly improve prediction performance on all metrics compared with baselines on five datasets. Particularly, experiments of predictions of unknown DCP responses, drug-cancer tissue associations, and drug-pathway associations illustrate the predictive power of GADRP. All results highlight the effectiveness of GADRP in predicting drug responses, and its potential value in guiding anti-cancer drug selection.
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Affiliation(s)
- Hong Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Chong Dai
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.,Department of Bioinformatics, Beijing Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Bioinformatics, Beijing Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaoqi Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Wenjuan Liu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Song He
- Department of Bioinformatics, Beijing Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Bioinformatics, Beijing Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.,The State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan University, Changsha 410082, China
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7
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Hybrid sampling-based contrastive learning for imbalanced node classification. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01677-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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8
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Lin S, Zhang G, Wei DQ, Xiong Y. DeepPSE: Prediction of polypharmacy side effects by fusing deep representation of drug pairs and attention mechanism. Comput Biol Med 2022; 149:105984. [PMID: 35994933 DOI: 10.1016/j.compbiomed.2022.105984] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/17/2022] [Accepted: 08/14/2022] [Indexed: 11/18/2022]
Abstract
Polypharmacy (multiple use of drugs) is an effective strategy for combating complex or co-existing diseases. However, a major consequence of polypharmacy is a higher risk of adverse side effects due to drug-drug interactions, which are rare and observed in relatively small clinical testing. Thus, identification of polypharmacy side effects remains challenging. Here, we propose a deep learning-based method, DeepPSE, to predict polypharmacy side effects in an end-to-end way. DeepPSE is composed of two main modules. First, multiple types of neural networks are constructed and fused to learn the deep representation of a drug pair. Second, the encoder block of transformer that includes self-attention mechanism is built to get latent features, which are further fed into the fully connected layer to predict polypharmacy side effects of drug pairs. Further, DeepPSE is compared with five baseline or state-of-the-art methods on a benchmark dataset of 964 types of polypharmacy side effects across 63473 drug pairs. Experimental results demonstrate that DeepPSE achieves better performance than that of all five methods. The source codes and data are available at https://github.com/ShenggengLin/DeepPSE.
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Affiliation(s)
- Shenggeng Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Guangwei Zhang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, 510275, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nayang, Henan, 473006, China; Peng Cheng National Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, China.
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
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Vo TH, Nguyen NTK, Kha QH, Le NQK. On the road to explainable AI in drug-drug interactions prediction: a systematic review. Comput Struct Biotechnol J 2022; 20:2112-2123. [PMID: 35832629 PMCID: PMC9092071 DOI: 10.1016/j.csbj.2022.04.021] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/15/2022] [Accepted: 04/15/2022] [Indexed: 12/26/2022] Open
Abstract
A systematic review on applications of explainable AI in drug-drug interaction prediction. Review is conducted on a comprehensive set of 94 papers from five prestigious databases. Discussions on the promises and challenges of explainable AI algorithms for drug-drug interaction prediction.
Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions. However, even though DDI prediction models have great potential for assisting physicians in polypharmacy decisions, there are still concerns regarding the reliability of AI models due to their black-box nature. Building AI models with explainable mechanisms can augment their transparency to address the above issue. Explainable AI (XAI) promotes safety and clarity by showing how decisions are made in AI models, especially in critical tasks like DDI predictions. In this review, a comprehensive overview of AI-based DDI prediction, including the publicly available source for AI-DDIs studies, the methods used in data manipulation and feature preprocessing, the XAI mechanisms to promote trust of AI, especially for critical tasks as DDIs prediction, the modeling methods, is provided. Limitations and the future directions of XAI in DDIs are also discussed.
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Affiliation(s)
- Thanh Hoa Vo
- Master Program in Clinical Genomics and Proteomics, College of Pharmacy, Taipei Medical University, Taipei 110, Taiwan
| | - Ngan Thi Kim Nguyen
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan
| | - Quang Hien Kha
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Corresponding author at: Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan.
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Nyamabo AK, Yu H, Liu Z, Shi JY. Drug-drug interaction prediction with learnable size-adaptive molecular substructures. Brief Bioinform 2021; 23:6409692. [PMID: 34695842 DOI: 10.1093/bib/bbab441] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/12/2021] [Accepted: 09/26/2021] [Indexed: 11/14/2022] Open
Abstract
Drug-drug interactions (DDIs) are interactions with adverse effects on the body, manifested when two or more incompatible drugs are taken together. They can be caused by the chemical compositions of the drugs involved. We introduce gated message passing neural network (GMPNN), a message passing neural network which learns chemical substructures with different sizes and shapes from the molecular graph representations of drugs for DDI prediction between a pair of drugs. In GMPNN, edges are considered as gates which control the flow of message passing, and therefore delimiting the substructures in a learnable way. The final DDI prediction between a drug pair is based on the interactions between pairs of their (learned) substructures, each pair weighted by a relevance score to the final DDI prediction output. Our proposed method GMPNN-CS (i.e. GMPNN + prediction module) is evaluated on two real-world datasets, with competitive results on one, and improved performance on the other compared with previous methods. Source code is freely available at https://github.com/kanz76/GMPNN-CS.
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Affiliation(s)
- Arnold K Nyamabo
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
| | - Hui Yu
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
| | - Zun Liu
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
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