1
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Saranya KR, Vimina ER. DRN-CDR: A cancer drug response prediction model using multi-omics and drug features. Comput Biol Chem 2024; 112:108175. [PMID: 39191166 DOI: 10.1016/j.compbiolchem.2024.108175] [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: 02/20/2024] [Revised: 08/09/2024] [Accepted: 08/14/2024] [Indexed: 08/29/2024]
Abstract
Cancer drug response (CDR) prediction is an important area of research that aims to personalize cancer therapy, optimizing treatment plans for maximum effectiveness while minimizing potential negative effects. Despite the advancements in Deep learning techniques, the effective integration of multi-omics data for drug response prediction remains challenging. In this paper, a regression method using Deep ResNet for CDR (DRN-CDR) prediction is proposed. We aim to explore the potential of considering sole cancer genes in drug response prediction. Here the multi-omics data such as gene expressions, mutation data, and methylation data along with the molecular structural information of drugs were integrated to predict the IC50 values of drugs. Drug features are extracted by employing a Uniform Graph Convolution Network, while Cell line features are extracted using a combination of Convolutional Neural Network and Fully Connected Networks. These features are then concatenated and fed into a deep ResNet for the prediction of IC50 values between Drug - Cell line pairs. The proposed method yielded higher Pearson's correlation coefficient (rp) of 0.7938 with lowest Root Mean Squared Error (RMSE) value of 0.92 when compared with similar methods of tCNNS, MOLI, DeepCDR, TGSA, NIHGCN, DeepTTA, GraTransDRP and TSGCNN. Further, when the model is extended to a classification problem to categorize drugs as sensitive or resistant, we achieved AUC and AUPR measures of 0.7623 and 0.7691, respectively. The drugs such as Tivozanib, SNX-2112, CGP-60474, PHA-665752, Foretinib etc., exhibited low median IC50 values and were found to be effective anti-cancer drugs. The case studies with different TCGA cancer types also revealed the effectiveness of SNX-2112, CGP-60474, Foretinib, Cisplatin, Vinblastine etc. This consistent pattern strongly suggests the effectiveness of the model in predicting CDR.
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Affiliation(s)
- K R Saranya
- Department of Computer Science and IT, School of Computing, Amrita Vishwa Vidyapeetham, Kochi Campus, India
| | - E R Vimina
- Department of Computer Science and IT, School of Computing, Amrita Vishwa Vidyapeetham, Kochi Campus, India.
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2
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Chen J, Zhu Y, Yuan Q. Predicting potential microbe-disease associations based on dual branch graph convolutional network. J Cell Mol Med 2024; 28:e18571. [PMID: 39086148 PMCID: PMC11291560 DOI: 10.1111/jcmm.18571] [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: 05/15/2024] [Revised: 06/15/2024] [Accepted: 06/27/2024] [Indexed: 08/02/2024] Open
Abstract
Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time-consuming and costly nature of laboratory-based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe-disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe-disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine-tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five-fold cross-validation (5-fold-CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5-fold-CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe-disease associations.
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Affiliation(s)
- Jing Chen
- School of Electronic and Information EngineeringSuzhou University of Science and TechnologySuzhouChina
| | - Yongjun Zhu
- School of Electronic and Information EngineeringSuzhou University of Science and TechnologySuzhouChina
| | - Qun Yuan
- Department of Respiratory Medicine, The Affiliated Suzhou Hospital of NanjingUniversity Medical SchoolSuzhouChina
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3
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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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4
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Huangfu X, Zhang C, Li H, Li S, Li Y. SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations. Comput Biol Chem 2024; 110:108054. [PMID: 38522389 DOI: 10.1016/j.compbiolchem.2024.108054] [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: 10/25/2023] [Revised: 02/22/2024] [Accepted: 03/16/2024] [Indexed: 03/26/2024]
Abstract
The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predicting drug synergy, but high computational cost for training models as well as great diversity and limited size in screening data escalate the difficulty of prediction. To address this challenge, we propose a simple machine learning framework, namely Similarity Network-based Synergy prediction (SNSynergy), for predicting synergistic effects towards new cell lines and new drug combinations by two locally weighted models CLSN and DCSN. This framework only requires a small amount of auxiliary data, like genomics information of cell lines and the molecular fingerprints or targets of drugs. Based on the assumption that similar cell lines and similar drug combinations have similar synergistic effects, CLSN and DCSN predict synergy scores through capturing individual synergy contributions of nearest cell line and drug combination neighbors, respectively. High correlations between predicted and measured synergy scores on two leading cancer cell line pharmacogenomic screening datasets (the O'Neil dataset and the NCI-ALMANAC dataset) demonstrate the effectiveness and robustness of SNSynergy. Many of the identified drug combinations are consistent with previous studies, or have been explored in clinical settings against the specific cancer type, showing that SNSynergy has the potential to supply cost-saving and effective high-throughput screening for prioritizing the most applicable cell lines and the most promising drug combinations.
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Affiliation(s)
- Xiaosheng Huangfu
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Chengwei Zhang
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Hualong Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China; School of mathematics and statistics, Guangdong University of Technology, Guangzhou 510520, China
| | - Sile Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Yushuang Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China.
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5
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Cheng N, Wang L, Liu Y, Song B, Ding C. HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction. J Chem Inf Model 2024; 64:4334-4347. [PMID: 38709204 PMCID: PMC11135324 DOI: 10.1021/acs.jcim.4c00003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/07/2024]
Abstract
Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug-drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein-protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein-protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.
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Affiliation(s)
- Ning Cheng
- School
of Informatics, Hunan University of Chinese
Medicine, Changsha, Hunan 410208, China
| | - Li Wang
- Degree
Programs in Systems and information Engineering, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
| | - Yiping Liu
- College
of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Bosheng Song
- College
of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Changsong Ding
- School
of Informatics, Hunan University of Chinese
Medicine, Changsha, Hunan 410208, China
- Big
Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
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6
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Zhou L, Wang X, Peng L, Chen M, Wen H. SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference. J Cell Mol Med 2024; 28:e18372. [PMID: 38747737 PMCID: PMC11095317 DOI: 10.1111/jcmm.18372] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 04/10/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024] Open
Abstract
Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.
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Affiliation(s)
- Liqian Zhou
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Xiwen Wang
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Lihong Peng
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Min Chen
- School of Computer ScienceHunan Institute of TechnologyHengyangChina
| | - Hong Wen
- School of Computer ScienceHunan University of TechnologyHunanChina
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7
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Wang T, Wang R, Wei L. AttenSyn: An Attention-Based Deep Graph Neural Network for Anticancer Synergistic Drug Combination Prediction. J Chem Inf Model 2024; 64:2854-2862. [PMID: 37565997 DOI: 10.1021/acs.jcim.3c00709] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Identifying synergistic drug combinations is fundamentally important to treat a variety of complex diseases while avoiding severe adverse drug-drug interactions. Although several computational methods have been proposed, they highly rely on handcrafted feature engineering and cannot learn better interactive information between drug pairs, easily resulting in relatively low performance. Recently, deep-learning methods, especially graph neural networks, have been widely developed in this area and demonstrated their ability to address complex biological problems. In this study, we proposed AttenSyn, an attention-based deep graph neural network for accurately predicting synergistic drug combinations. In particular, we adopted a graph neural network module to extract high-latent features based on the molecular graphs only and exploited the attention-based pooling module to learn interactive information between drug pairs to strengthen the representations of drug pairs. Comparative results on the benchmark datasets demonstrated that our AttenSyn performs better than the state-of-the-art methods in the prediction of anticancer synergistic drug combinations. Additionally, to provide good interpretability of our model, we explored and visualized some crucial substructures in drugs through attention mechanisms. Furthermore, we also verified the effectiveness of our proposed AttenSyn on two cell lines by visualizing the features of drug combinations learnt from our model, exhibiting satisfactory generalization ability.
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Affiliation(s)
- Tianshuo Wang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Ruheng Wang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
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8
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He S, Yun L, Yi H. Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction. BMC Bioinformatics 2024; 25:79. [PMID: 38378479 PMCID: PMC10877759 DOI: 10.1186/s12859-024-05705-w] [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: 12/19/2023] [Accepted: 02/14/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Identification of potential drug-disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data. RESULTS In this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug-disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug-drug, drug-disease, and disease-disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation. CONCLUSIONS Rigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug-disease association prediction, which is beneficial for drug repositioning and drug safety research.
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Affiliation(s)
- Shihui He
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education, Kunming, 650500, China
| | - Lijun Yun
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China.
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education, Kunming, 650500, China.
| | - Haicheng Yi
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
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9
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Asfand-E-Yar M, Hashir Q, Shah AA, Malik HAM, Alourani A, Khalil W. Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events. Sci Rep 2024; 14:4076. [PMID: 38374325 PMCID: PMC10876630 DOI: 10.1038/s41598-024-54409-x] [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: 12/05/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024] Open
Abstract
Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs' effectiveness. The drug-to-drug interactions are detected based on the interactions of chemical substructures, targets, pathways, and enzymes; therefore, machine learning (ML) and deep learning (DL) techniques are used to find the associated DDI events. The DL model, i.e., Convolutional Neural Network (CNN), is used to analyze the DDI. DDI is based on the 65 different drug-associated events, which is present in the drug bank database. Our model uses the inputs, which are chemical structures (i.e., smiles of drugs), enzymes, pathways, and the target of the drug. Therefore, for the multi-model CNN, we use several layers, activation functions, and features of drugs to achieve better accuracy as compared to traditional prediction algorithms. We perform different experiments on various hyperparameters. We have also carried out experiments on various iterations of drug features in different sets. Our Multi-Modal Convolutional Neural Network - Drug to Drug Interaction (MCNN-DDI) model achieved an accuracy of 90.00% and an AUPR of 94.78%. The results showed that a combination of the drug's features (i.e., chemical substructure, target, and enzyme) performs better in DDIs-associated events prediction than other features.
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Affiliation(s)
- Muhammad Asfand-E-Yar
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
| | - Qadeer Hashir
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
| | - Asghar Ali Shah
- Department of Computer Science, Bahria University, Islamabad , Pakistan
| | | | - Abdullah Alourani
- Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, Buraydah 51452, Saudi Arabia.
| | - Waqar Khalil
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
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10
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Abd El-Hafeez T, Shams MY, Elshaier YAMM, Farghaly HM, Hassanien AE. Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs. Sci Rep 2024; 14:2428. [PMID: 38287066 PMCID: PMC10825182 DOI: 10.1038/s41598-024-52814-w] [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: 11/09/2023] [Accepted: 01/24/2024] [Indexed: 01/31/2024] Open
Abstract
Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.
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Affiliation(s)
- Tarek Abd El-Hafeez
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt.
- Computer Science Unit, Deraya University, El-Minia, Egypt.
| | - Mahmoud Y Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Yaseen A M M Elshaier
- Department of Organic and Medicinal Chemistry, Faculty of Pharmacy, University of Sadat City, Sadat City, Menoufia, Egypt
| | - Heba Mamdouh Farghaly
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt.
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt.
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11
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Wang W, Yuan G, Wan S, Zheng Z, Liu D, Zhang H, Li J, Zhou Y, Wang X. A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs. Brief Bioinform 2023; 25:bbad522. [PMID: 38243692 PMCID: PMC10796255 DOI: 10.1093/bib/bbad522] [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: 09/02/2023] [Revised: 11/08/2023] [Accepted: 12/19/2023] [Indexed: 01/21/2024] Open
Abstract
Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy. Currently, numerous computational methods have been developed to predict the synergistic effects of anticancer drugs. However, there has been insufficient exploration of how to mine drug and cell line data at different granularity levels for predicting synergistic anticancer drug combinations. Therefore, this study proposes a granularity-level information fusion strategy based on the hypergraph transformer, named HypertranSynergy, to predict synergistic effects of anticancer drugs. HypertranSynergy introduces synergistic connections between cancer cell lines and drug combinations using hypergraph. Then, the Coarse-grained Information Extraction (CIE) module merges the hypergraph with a transformer for node embeddings. In the CIE module, Contranorm is a normalization layer that mitigates over-smoothing, while Gaussian noise addresses local information gaps. Additionally, the Fine-grained Information Extraction (FIE) module assesses fine-grained information's impact on predictions by employing similarity-aware matrices from drug/cell line features. Both CIE and FIE modules are integrated into HypertranSynergy. In addition, HypertranSynergy achieved the AUC of 0.93${\pm }$0.01 and the AUPR of 0.69${\pm }$0.02 in 5-fold cross-validation of classification task, and the RMSE of 13.77${\pm }$0.07 and the PCC of 0.81${\pm }$0.02 in 5-fold cross-validation of regression task. These results are better than most of the state-of-the-art models.
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Affiliation(s)
- Wei Wang
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province 453007, China
| | - Gaolin Yuan
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
| | - Shitong Wan
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
| | - Ziwei Zheng
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
| | - Dong Liu
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province 453007, China
| | - Hongjun Zhang
- Hebi Instiute of Engineering and Technology, Henan Polytechnic University, 458030, China
| | - Juntao Li
- School of Mathematics and Information Science, Henan Normal University, 453007 Xinxiang, China
| | - Yun Zhou
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province 453007, China
| | - Xianfang Wang
- College of Computer Science and Technology Engineering, Henan Institute of Technology, 453000, China
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12
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Pan J, You Z, You W, Zhao T, Feng C, Zhang X, Ren F, Ma S, Wu F, Wang S, Sun Y. PTBGRP: predicting phage-bacteria interactions with graph representation learning on microbial heterogeneous information network. Brief Bioinform 2023; 24:bbad328. [PMID: 37742053 DOI: 10.1093/bib/bbad328] [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: 06/05/2023] [Revised: 08/14/2023] [Accepted: 08/30/2023] [Indexed: 09/25/2023] Open
Abstract
Identifying the potential bacteriophages (phage) candidate to treat bacterial infections plays an essential role in the research of human pathogens. Computational approaches are recognized as a valid way to predict bacteria and target phages. However, most of the current methods only utilize lower-order biological information without considering the higher-order connectivity patterns, which helps to improve the predictive accuracy. Therefore, we developed a novel microbial heterogeneous interaction network (MHIN)-based model called PTBGRP to predict new phages for bacterial hosts. Specifically, PTBGRP first constructs an MHIN by integrating phage-bacteria interaction (PBI) and six bacteria-bacteria interaction networks with their biological attributes. Then, different representation learning methods are deployed to extract higher-level biological features and lower-level topological features from MHIN. Finally, PTBGRP employs a deep neural network as the classifier to predict unknown PBI pairs based on the fused biological information. Experiment results demonstrated that PTBGRP achieves the best performance on the corresponding ESKAPE pathogens and PBI dataset when compared with state-of-art methods. In addition, case studies of Klebsiella pneumoniae and Staphylococcus aureus further indicate that the consideration of rich heterogeneous information enables PTBGRP to accurately predict PBI from a more comprehensive perspective. The webserver of the PTBGRP predictor is freely available at http://120.77.11.78/PTBGRP/.
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Affiliation(s)
- Jie Pan
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Zhuhong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
| | - Wencai You
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Tian Zhao
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Chenlu Feng
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Xuexia Zhang
- North China Pharmaceutical Group, Shijiazhuang 050015, Hebei, China
- National Microbial Medicine Engineering & Research Center, Shijiazhuang 050015, Hebei, China
| | - Fengzhi Ren
- North China Pharmaceutical Group, Shijiazhuang 050015, Hebei, China
- National Microbial Medicine Engineering & Research Center, Shijiazhuang 050015, Hebei, China
| | - Sanxing Ma
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Fan Wu
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Shiwei Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
| | - Yanmei Sun
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China
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13
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Peng L, Tan J, Xiong W, Zhang L, Wang Z, Yuan R, Li Z, Chen X. Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data. Comput Biol Med 2023; 163:107137. [PMID: 37364528 DOI: 10.1016/j.compbiomed.2023.107137] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 05/18/2023] [Accepted: 06/04/2023] [Indexed: 06/28/2023]
Abstract
BACKGROUND Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis. METHODS Focusing on ligand-receptor co-expressions, in this study, we developed an ensemble deep learning framework, CellComNet, to decipher ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data. First, credible LRIs are captured by integrating data arrangement, feature extraction, dimension reduction, and LRI classification based on an ensemble of heterogeneous Newton boosting machine and deep neural network. Next, known and identified LRIs are screened based on single-cell RNA sequencing (scRNA-seq) data in certain tissues. Finally, cell-cell communication is inferred by incorporating scRNA-seq data, the screened LRIs, a joint scoring strategy that combines expression thresholding and expression product of ligands and receptors. RESULTS The proposed CellComNet framework was compared with four competing protein-protein interaction prediction models (PIPR, XGBoost, DNNXGB, and OR-RCNN) and obtained the best AUCs and AUPRs on four LRI datasets, elucidating the optimal LRI classification ability. CellComNet was further applied to analyze intercellular communication in human melanoma and head and neck squamous cell carcinoma (HNSCC) tissues. The results demonstrate that cancer-associated fibroblasts highly communicate with melanoma cells and endothelial cells strong communicate with HNSCC cells. CONCLUSIONS The proposed CellComNet framework efficiently identified credible LRIs and significantly improved cell-cell communication inference performance. We anticipate that CellComNet can contribute to anticancer drug design and tumor-targeted therapy.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China; College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Jingwei Tan
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Wei Xiong
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
| | - Zhao Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Ruya Yuan
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Hengyang, 421002, Hunan, China.
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
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14
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Oloulade BM, Gao J, Chen J, Al-Sabri R, Wu Z. Cancer drug response prediction with surrogate modeling-based graph neural architecture search. Bioinformatics 2023; 39:btad478. [PMID: 37555809 PMCID: PMC10432359 DOI: 10.1093/bioinformatics/btad478] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/01/2023] [Accepted: 08/08/2023] [Indexed: 08/10/2023] Open
Abstract
MOTIVATION Understanding drug-response differences in cancer treatments is one of the most challenging aspects of personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods in many graph representation learning scenarios in bioinformatics. However, building an optimal handcrafted GNN model for a particular drug sensitivity dataset requires manual design and fine-tuning of the hyperparameters for the GNN model, which is time-consuming and requires expert knowledge. RESULTS In this work, we propose AutoCDRP, a novel framework for automated cancer drug-response predictor using GNNs. Our approach leverages surrogate modeling to efficiently search for the most effective GNN architecture. AutoCDRP uses a surrogate model to predict the performance of GNN architectures sampled from a search space, allowing it to select the optimal architecture based on evaluation performance. Hence, AutoCDRP can efficiently identify the optimal GNN architecture by exploring the performance of all GNN architectures in the search space. Through comprehensive experiments on two benchmark datasets, we demonstrate that the GNN architecture generated by AutoCDRP surpasses state-of-the-art designs. Notably, the optimal GNN architecture identified by AutoCDRP consistently outperforms the best baseline architecture from the first epoch, providing further evidence of its effectiveness. AVAILABILITY AND IMPLEMENTATION https://github.com/BeObm/AutoCDRP.
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Affiliation(s)
| | - Jianliang Gao
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jiamin Chen
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Raeed Al-Sabri
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Zhenpeng Wu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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15
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Rafiei F, Zeraati H, Abbasi K, Ghasemi JB, Parsaeian M, Masoudi-Nejad A. DeepTraSynergy: drug combinations using multimodal deep learning with transformers. Bioinformatics 2023; 39:btad438. [PMID: 37467066 PMCID: PMC10397534 DOI: 10.1093/bioinformatics/btad438] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 06/27/2023] [Accepted: 07/17/2023] [Indexed: 07/21/2023] Open
Abstract
MOTIVATION Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. RESULTS Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug-target interaction, protein-protein interaction, and cell-target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug-target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug-protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug-protein interaction significantly improves the prediction of synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy.
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Affiliation(s)
- Fatemeh Rafiei
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
| | - Hojjat Zeraati
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
| | - Karim Abbasi
- Laboratory of System Biology, Bioinformatics & Artificial Intelligent in Medicine (LBB&AI), Faculty of Mathematics and Computer Science, Kharazmi University, Tehran 1571914911, Iran
| | - Jahan B Ghasemi
- Chemistry Department, Faculty of Chemistry, School of Sciences, University of Tehran, Tehran 1417614411, Iran
| | - Mahboubeh Parsaeian
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1417613151, Iran
- Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London W21PG, United Kingdom
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran 1417614411, Iran
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16
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Wang F, Yang H, Wu Y, Peng L, Li X. SAELGMDA: Identifying human microbe-disease associations based on sparse autoencoder and LightGBM. Front Microbiol 2023; 14:1207209. [PMID: 37415823 PMCID: PMC10320730 DOI: 10.3389/fmicb.2023.1207209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 05/18/2023] [Indexed: 07/08/2023] Open
Abstract
Introduction Identification of complex associations between diseases and microbes is important to understand the pathogenesis of diseases and design therapeutic strategies. Biomedical experiment-based Microbe-Disease Association (MDA) detection methods are expensive, time-consuming, and laborious. Methods Here, we developed a computational method called SAELGMDA for potential MDA prediction. First, microbe similarity and disease similarity are computed by integrating their functional similarity and Gaussian interaction profile kernel similarity. Second, one microbe-disease pair is presented as a feature vector by combining the microbe and disease similarity matrices. Next, the obtained feature vectors are mapped to a low-dimensional space based on a Sparse AutoEncoder. Finally, unknown microbe-disease pairs are classified based on Light Gradient boosting machine. Results The proposed SAELGMDA method was compared with four state-of-the-art MDA methods (MNNMDA, GATMDA, NTSHMDA, and LRLSHMDA) under five-fold cross validations on diseases, microbes, and microbe-disease pairs on the HMDAD and Disbiome databases. The results show that SAELGMDA computed the best accuracy, Matthews correlation coefficient, AUC, and AUPR under the majority of conditions, outperforming the other four MDA prediction models. In particular, SAELGMDA obtained the best AUCs of 0.8358 and 0.9301 under cross validation on diseases, 0.9838 and 0.9293 under cross validation on microbes, and 0.9857 and 0.9358 under cross validation on microbe-disease pairs on the HMDAD and Disbiome databases. Colorectal cancer, inflammatory bowel disease, and lung cancer are diseases that severely threat human health. We used the proposed SAELGMDA method to find possible microbes for the three diseases. The results demonstrate that there are potential associations between Clostridium coccoides and colorectal cancer and one between Sphingomonadaceae and inflammatory bowel disease. In addition, Veillonella may associate with autism. The inferred MDAs need further validation. Conclusion We anticipate that the proposed SAELGMDA method contributes to the identification of new MDAs.
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Affiliation(s)
- Feixiang Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Huandong Yang
- Department of Gastrointestinal Surgery, Yidu Central Hospital of Weifang, Weifang, China
| | - Yan Wu
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Xiaoling Li
- The Second Department of Oncology, Beidahuang Industry Group General Hospital, Harbin, China
- The Second Department of Oncology, Heilongjiang Second Cancer Hospital, Harbin, China
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17
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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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