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Dong Y, Bai Y, Liu H, Yang Z, Chang Y, Li J, Han Q, Feng X, Fan X, Ren X. DKPE-GraphSYN: a drug synergy prediction model based on joint dual kernel density estimation and positional encoding for graph representation. Front Genet 2024; 15:1401544. [PMID: 38948360 PMCID: PMC11211516 DOI: 10.3389/fgene.2024.1401544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 05/24/2024] [Indexed: 07/02/2024] Open
Abstract
Introduction: Synergistic medication, a crucial therapeutic strategy in cancer treatment, involves combining multiple drugs to enhance therapeutic effectiveness and mitigate side effects. Current research predominantly employs deep learning models for extracting features from cell line and cancer drug structure data. However, these methods often overlook the intricate nonlinear relationships within the data, neglecting the distribution characteristics and weighted probability densities of gene expression data in multi-dimensional space. It also fails to fully exploit the structural information of cancer drugs and the potential interactions between drug molecules. Methods: To overcome these challenges, we introduce an innovative end-to-end learning model specifically tailored for cancer drugs, named Dual Kernel Density and Positional Encoding (DKPE) for Graph Synergy Representation Network (DKPEGraphSYN). This model is engineered to refine the prediction of drug combination synergy effects in cancer. DKPE-GraphSYN utilizes Dual Kernel Density Estimation and Positional Encoding techniques to effectively capture the weighted probability density and spatial distribution information of gene expression, while exploring the interactions and potential relationships between cancer drug molecules via a graph neural network. Results: Experimental results show that our prediction model achieves significant performance enhancements in forecasting drug synergy effects on a comprehensive cancer drug and cell line synergy dataset, achieving an AUPR of 0.969 and an AUC of 0.976. Discussion: These results confirm our model's superior accuracy in predicting cancer drug combinations, providing a supportive method for clinical medication strategy in cancer.
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Affiliation(s)
- Yunyun Dong
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yujie Bai
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Haitao Liu
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Ziting Yang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yunqing Chang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Jianguang Li
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Qixuan Han
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Xiufang Feng
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Xiaole Fan
- Information Management Department, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi, China
| | - Xiaoqiang Ren
- Information Management Department, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi, China
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2
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Abbasi F, Rousu J. New methods for drug synergy prediction: A mini-review. Curr Opin Struct Biol 2024; 86:102827. [PMID: 38705070 DOI: 10.1016/j.sbi.2024.102827] [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: 10/09/2023] [Revised: 04/12/2024] [Accepted: 04/12/2024] [Indexed: 05/07/2024]
Abstract
In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these articles under a unifying lens by highlighting the core technologies, the data sources, the input data types and synergy scores used in the methods, as well as the prediction scenarios and evaluation protocols that the articles deal with. Our finding is that the best methods accurately solve the synergy prediction scenarios involving known drugs or cell lines while the scenarios involving new drugs or cell lines still fall short of an accurate prediction level.
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Affiliation(s)
- Fatemeh Abbasi
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Juho Rousu
- Department of Computer Science, Aalto University, Espoo, Finland.
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Tang YC, Li R, Tang J, Zheng WJ, Jiang X. SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations. RESEARCH SQUARE 2024:rs.3.rs-4308618. [PMID: 38746131 PMCID: PMC11092851 DOI: 10.21203/rs.3.rs-4308618/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Background The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist: (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein-protein interactions, failing to adapt to dynamic and context-dependent networks. This limitation constrains the applicability of current methods. Results We introduced SAFER, a Sub-hypergraph Attention-based graph model, addressing these issues by incorporating complex relationships among biological knowledge networks and considering dosing effects on subject-specific networks. SAFER outperformed previous models on the benchmark and the independent test set. The analysis of subgraph attention weight for the lung cancer cell line highlighted JAK-STAT signaling pathway, PRDM12, ZNF781, and CDC5L that have been implicated in lung fibrosis. Conclusions SAFER presents an interpretable framework designed to identify drug-responsive signals. Tailored for comprehending dose effects on subject-specific molecular contexts, our model uniquely captures dose-level drug combination responses. This capability unlocks previously inaccessible avenues of investigation compared to earlier models. Finally, the SAFER framework can be leveraged by future inquiries to investigate molecular networks that uniquely characterize individual patients.
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Affiliation(s)
- Yi-Ching Tang
- Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States
| | - Rongbin Li
- Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - W Jim Zheng
- Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States
| | - Xiaoqian Jiang
- Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States
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Liu Y, Zhang P, Che C, Wei Z. SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction. J Chem Inf Model 2024. [PMID: 38687366 DOI: 10.1021/acs.jcim.4c00177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Drug combination therapies are well-established strategies for the treatment of cancer with low toxicity and fewer adverse effects. Computational drug synergy prediction approaches can accelerate the discovery of novel combination therapies, but the existing methods do not explicitly consider the key role of important substructures in producing synergistic effects. To this end, we propose a significant substructure-aware anticancer drug synergy prediction method, named SDDSynergy, to adaptively identify critical functional groups in drug synergy. SDDSynergy splits the task of predicting drug synergy into predicting the effect of individual substructures on cancer cell lines and highlights the impact of important substructures through a novel drug-cell line attention mechanism. And a substructure pair attention mechanism is incorporated to capture the information on internal substructure pairs interaction in drug combinations, which aids in predicting synergy. The substructures of different sizes and shapes are directly obtained from the molecular graph of the drugs by multilayer substructure information passing networks. Extensive experiments on three real-world data sets demonstrate that SDDSynergy outperforms other state-of-the-art methods. We also verify that many of the novel drug combinations predicted by SDDSynergy are supported by previous studies or clinical trials through an in-depth literature survey.
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Affiliation(s)
- Yunjiong Liu
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China
- School of Software Engineering, Dalian University, Dalian 116622, China
| | - Peiliang Zhang
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
| | - Chao Che
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China
- School of Software Engineering, Dalian University, Dalian 116622, China
| | - Ziqi Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100864, China
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5
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Zhao X, Xu J, Shui Y, Xu M, Hu J, Liu X, Che K, Wang J, Liu Y. PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction. J Cheminform 2024; 16:41. [PMID: 38622663 PMCID: PMC11017561 DOI: 10.1186/s13321-024-00839-8] [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: 09/19/2023] [Accepted: 04/03/2024] [Indexed: 04/17/2024] Open
Abstract
MOTIVATION Drug combination therapies have shown promise in clinical cancer treatments. However, it is hard to experimentally identify all drug combinations for synergistic interaction even with high-throughput screening due to the vast space of potential combinations. Although a number of computational methods for drug synergy prediction have proven successful in narrowing down this space, fusing drug pairs and cell line features effectively still lacks study, hindering current algorithms from understanding the complex interaction between drugs and cell lines. RESULTS In this paper, we proposed a Permutable feature fusion network for Drug-Drug Synergy prediction, named PermuteDDS. PermuteDDS takes multiple representations of drugs and cell lines as input and employs a permutable fusion mechanism to combine drug and cell line features. In experiments, PermuteDDS exhibits state-of-the-art performance on two benchmark data sets. Additionally, the results on independent test set grouped by different tissues reveal that PermuteDDS has good generalization performance. We believed that PermuteDDS is an effective and valuable tool for identifying synergistic drug combinations. It is publicly available at https://github.com/littlewei-lazy/PermuteDDS . SCIENTIFIC CONTRIBUTION First, this paper proposes a permutable feature fusion network for predicting drug synergy termed PermuteDDS, which extract diverse information from multiple drug representations and cell line representations. Second, the permutable fusion mechanism combine the drug and cell line features by integrating information of different channels, enabling the utilization of complex relationships between drugs and cell lines. Third, comparative and ablation experiments provide evidence of the efficacy of PermuteDDS in predicting drug-drug synergy.
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Affiliation(s)
- Xinwei Zhao
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Junqing Xu
- The Second Clinical Medical School, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Youyuan Shui
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Mengdie Xu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Jie Hu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
- Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 210029, Jiangsu, China
| | - Xiaoyan Liu
- Faculty of Computing, Harbin Institute of Technology, No. 92 West Da Zhi St, Harbin, 150001, Heilongjiang, China
| | - Kai Che
- Xi'an Aeronautics Computing Technique Research Institute, AVIC, No. 156, TaiBai Nroth Road, Xi'an, 710068, Shanxi, China
- Aviation Key Laboratory of Science and Technology on Airborne and Missleborne Computer, Xi'an, 710065, Shanxi, China
| | - Junjie Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
- Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 210029, Jiangsu, China.
| | - Yun Liu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
- Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 210029, Jiangsu, China.
- Department of Information, the First Affiliated Hospital, Nanjing Medical University, No. 300 Guang Zhou Road, Nanjing, 210029, Jiangsu, China.
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6
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Yan S, Zheng D. A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer. Interdiscip Sci 2024; 16:218-230. [PMID: 38183569 DOI: 10.1007/s12539-023-00596-6] [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: 08/13/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 01/08/2024]
Abstract
The exploration of drug combinations presents an opportunity to amplify therapeutic effectiveness while alleviating undesirable side effects. Nevertheless, the extensive array of potential combinations poses challenges in terms of cost and time constraints for experimental screening. Thus, it is crucial to narrow down the search space. Deep learning approaches have gained widespread popularity in predicting synergistic drug combinations tailored for specific cell lines in vitro settings. In the present study, we introduce a novel method termed GTextSyn, which utilizes the integration of gene expression data and chemical structure information for the prediction of synergistic effects in drug combinations. GTextSyn employs a sentence classification model within the domain of Natural Language Processing (NLP), wherein drugs and cell lines are regarded as entities possessing biochemical relevance. Meanwhile, combinations of drug pairs and cell lines are construed as sentences with biochemical relational significance. To assess the efficacy of GTextSyn, we conduct a comparative analysis with alternative deep learning approaches using a standard benchmark dataset. The results from a five-fold cross-validation demonstrate a 49.5% reduction in Mean Square Error (MSE) achieved by GTextSyn, surpassing the performance of the next best method in the regression task. Furthermore, we conduct a comprehensive literature survey on the predicted novel drug combinations and find substantial support from prior experimental studies for many of the combinations identified by GTextSyn.
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Affiliation(s)
- Shiyu Yan
- School of Computer, University of South China, West Changsheng Road, Hengyang, 421001, Hunan, China.
| | - Ding Zheng
- School of Computer, University of South China, West Changsheng Road, Hengyang, 421001, Hunan, China
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7
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Li T, Shetty S, Kamath A, Jaiswal A, Jiang X, Ding Y, Kim Y. CancerGPT for few shot drug pair synergy prediction using large pretrained language models. NPJ Digit Med 2024; 7:40. [PMID: 38374445 PMCID: PMC10876664 DOI: 10.1038/s41746-024-01024-9] [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: 05/19/2023] [Accepted: 02/02/2024] [Indexed: 02/21/2024] Open
Abstract
Large language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology and medicine has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases where structured data and sample size are limited, by extracting prior knowledge from text corpora. Here we report our proposed few-shot learning approach, which uses LLMs to predict the synergy of drug pairs in rare tissues that lack structured data and features. Our experiments, which involved seven rare tissues from different cancer types, demonstrate that the LLM-based prediction model achieves significant accuracy with very few or zero samples. Our proposed model, the CancerGPT (with ~ 124M parameters), is comparable to the larger fine-tuned GPT-3 model (with ~ 175B parameters). Our research contributes to tackling drug pair synergy prediction in rare tissues with limited data, and also advancing the use of LLMs for biological and medical inference tasks.
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Affiliation(s)
- Tianhao Li
- School of Information, University of Texas at Austin, Austin, TX, USA
| | - Sandesh Shetty
- Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Advaith Kamath
- Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Ajay Jaiswal
- School of Information, University of Texas at Austin, Austin, TX, USA
| | - Xiaoqian Jiang
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ying Ding
- School of Information, University of Texas at Austin, Austin, TX, USA
| | - Yejin Kim
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
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8
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Flanary VL, Fisher JL, Wilk EJ, Howton TC, Lasseigne BN. Computational Advancements in Cancer Combination Therapy Prediction. JCO Precis Oncol 2023; 7:e2300261. [PMID: 37824797 DOI: 10.1200/po.23.00261] [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: 05/24/2023] [Revised: 07/20/2023] [Accepted: 08/15/2023] [Indexed: 10/14/2023] Open
Abstract
Given the high attrition rate of de novo drug discovery and limited efficacy of single-agent therapies in cancer treatment, combination therapy prediction through in silico drug repurposing has risen as a time- and cost-effective alternative for identifying novel and potentially efficacious therapies for cancer. The purpose of this review is to provide an introduction to computational methods for cancer combination therapy prediction and to summarize recent studies that implement each of these methods. A systematic search of the PubMed database was performed, focusing on studies published within the past 10 years. Our search included reviews and articles of ongoing and retrospective studies. We prioritized articles with findings that suggest considerations for improving combination therapy prediction methods over providing a meta-analysis of all currently available cancer combination therapy prediction methods. Computational methods used for drug combination therapy prediction in cancer research include networks, regression-based machine learning, classifier machine learning models, and deep learning approaches. Each method class has its own advantages and disadvantages, so careful consideration is needed to determine the most suitable class when designing a combination therapy prediction method. Future directions to improve current combination therapy prediction technology include incorporation of disease pathobiology, drug characteristics, patient multiomics data, and drug-drug interactions to determine maximally efficacious and tolerable drug regimens for cancer. As computational methods improve in their capability to integrate patient, drug, and disease data, more comprehensive models can be developed to more accurately predict safe and efficacious combination drug therapies for cancer and other complex diseases.
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Affiliation(s)
- Victoria L Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL
| | - Jennifer L Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL
| | - Elizabeth J Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL
| | - Timothy C Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL
| | - Brittany N Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL
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Dong Z, Zhang H, Chen Y, Payne PRO, Li F. Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling. Cancers (Basel) 2023; 15:4210. [PMID: 37686486 PMCID: PMC10486573 DOI: 10.3390/cancers15174210] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 09/10/2023] Open
Abstract
Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations.
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Affiliation(s)
- Zehao Dong
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; (Z.D.); (Y.C.)
| | - Heming Zhang
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
| | - Yixin Chen
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; (Z.D.); (Y.C.)
| | - Philip R. O. Payne
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
| | - Fuhai Li
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
- Department of Pediatrics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA
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Liu H, Fan Z, Lin J, Yang Y, Ran T, Chen H. The recent progress of deep-learning-based in silico prediction of drug combination. Drug Discov Today 2023:103625. [PMID: 37236526 DOI: 10.1016/j.drudis.2023.103625] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/24/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023]
Abstract
Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.
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Affiliation(s)
- Haoyang Liu
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China; College of Life Sciences, Nankai University, Tianjin 300071, China
| | - Zhiguang Fan
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Jie Lin
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China.
| | - Ting Ran
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China.
| | - Hongming Chen
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China.
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