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Pan F, Wang CN, Yu ZH, Wu ZR, Wang Z, Lou S, Li WH, Liu GX, Li T, Zhao YZ, Tang Y. NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods. Acta Pharmacol Sin 2024; 45:2199-2211. [PMID: 38902503 PMCID: PMC11420228 DOI: 10.1038/s41401-024-01324-6] [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: 01/18/2024] [Accepted: 05/26/2024] [Indexed: 06/22/2024] Open
Abstract
Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 μM, with Oxyberberine showing promising effects even at 10 μM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.
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Affiliation(s)
- Fei Pan
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Cheng-Nuo Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Zhuo-Hang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Zeng-Rui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Shang Lou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Wei-Hua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Gui-Xia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Ting Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
| | - Yu-Zheng Zhao
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
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E U, T M, A V G, D P. A comprehensive survey of drug-target interaction analysis in allopathy and siddha medicine. Artif Intell Med 2024; 157:102986. [PMID: 39326289 DOI: 10.1016/j.artmed.2024.102986] [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: 10/20/2023] [Revised: 08/13/2024] [Accepted: 09/18/2024] [Indexed: 09/28/2024]
Abstract
Effective drug delivery is the cornerstone of modern healthcare, ensuring therapeutic compounds reach their intended targets efficiently. This paper explores the potential of personalized and holistic healthcare, driven by the synergy between traditional and allopathic medicine systems, with a specific focus on the vast reservoir of medicinal compounds found in plants rooted in the historical legacy of traditional medicine. Motivated by the desire to unlock the therapeutic potential of medicinal plants and bridge the gap between traditional and allopathic medicine, this survey delves into in-silico computational approaches for studying Drug-Target Interactions (DTI) within the contexts of allopathy and siddha medicine. The contributions of this survey are multifaceted: it offers a comprehensive overview of in-silico methods for DTI analysis in both systems, identifies common challenges in DTI studies, provides insights into future directions to advance DTI analysis, and includes a comparative analysis of DTI in allopathy and siddha medicine. The findings of this survey highlight the pivotal role of in-silico computational approaches in advancing drug research and development in both allopathy and siddha medicine, emphasizing the importance of integrating these methods to drive the future of personalized healthcare.
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Affiliation(s)
- Uma E
- Department of Information Science and Technology, College of Engineering Guindy, Chennai, India.
| | - Mala T
- Department of Information Science and Technology, College of Engineering Guindy, Chennai, India
| | - Geetha A V
- Department of Information Science and Technology, College of Engineering Guindy, Chennai, India
| | - Priyanka D
- Department of Information Science and Technology, College of Engineering Guindy, Chennai, India
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3
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Manen-Freixa L, Antolin AA. Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery. Expert Opin Drug Discov 2024; 19:1043-1069. [PMID: 39004919 DOI: 10.1080/17460441.2024.2376643] [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: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology. AREAS COVERED This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples. EXPERT OPINION Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.
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Affiliation(s)
- Leticia Manen-Freixa
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Albert A Antolin
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Center for Cancer Drug Discovery, The Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
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Muthuramalingam P, Jeyasri R, Varadharajan V, Priya A, Dhanapal AR, Shin H, Thiruvengadam M, Ramesh M, Krishnan M, Omosimua RO, Sathyaseelan DD, Venkidasamy B. Network pharmacology: an efficient but underutilized approach in oral, head and neck cancer therapy-a review. Front Pharmacol 2024; 15:1410942. [PMID: 39035991 PMCID: PMC11257993 DOI: 10.3389/fphar.2024.1410942] [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: 04/02/2024] [Accepted: 06/05/2024] [Indexed: 07/23/2024] Open
Abstract
The application of network pharmacology (NP) has advanced our understanding of the complex molecular mechanisms underlying diseases, including neck, head, and oral cancers, as well as thyroid carcinoma. This review aimed to explore the therapeutic potential of natural network pharmacology using compounds and traditional Chinese medicines for combating these malignancies. NP serves as a pivotal tool that provides a comprehensive view of the interactions among compounds, genes, and diseases, thereby contributing to the advancement of disease treatment and management. In parallel, this review discusses the significance of publicly accessible databases in the identification of oral, head, and neck cancer-specific genes. These databases, including those for head and neck oral cancer, head and neck cancer, oral cancer, and genomic variants of oral cancer, offer valuable insights into the genes, miRNAs, drugs, and genetic variations associated with these cancers. They serve as indispensable resources for researchers, clinicians, and drug developers, contributing to the pursuit of precision medicine and improved treatment of these challenging malignancies. In summary, advancements in NP could improve the globalization and modernization of traditional medicines and prognostic targets as well as aid in the development of innovative drugs. Furthermore, this review will be an eye-opener for researchers working on drug development from traditional medicines by applying NP approaches.
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Affiliation(s)
- Pandiyan Muthuramalingam
- Division of Horticultural Science, College of Agriculture and Life Sciences, Gyeongsang National University, Jinju, Republic of Korea
| | - Rajendran Jeyasri
- Department of Biotechnology, Science Campus, Alagappa University, Karaikudi, India
| | | | - Arumugam Priya
- Department of Medicine, Division of Gastroenterology and Hepatology, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Anand Raj Dhanapal
- Chemistry and Bioprospecting Division, Institute of Forest Genetics and Tree Breeding (IFGTB), Coimbatore, India
| | - Hyunsuk Shin
- Division of Horticultural Science, College of Agriculture and Life Sciences, Gyeongsang National University, Jinju, Republic of Korea
| | - Muthu Thiruvengadam
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, Republic of Korea
| | - Manikandan Ramesh
- Department of Biotechnology, Science Campus, Alagappa University, Karaikudi, India
| | - Murugesan Krishnan
- Department of Oral and Maxillofacial Surgery, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India
| | | | - Divyan Devasir Sathyaseelan
- Department of General Surgery, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India
| | - Baskar Venkidasamy
- Department of Oral and Maxillofacial Surgery, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India
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Yuan Y, Hu R, Chen S, Zhang X, Liu Z, Zhou G. CKG-IMC: An inductive matrix completion method enhanced by CKG and GNN for Alzheimer's disease compound-protein interactions prediction. Comput Biol Med 2024; 177:108612. [PMID: 38838556 DOI: 10.1016/j.compbiomed.2024.108612] [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] [Revised: 04/17/2024] [Accepted: 05/11/2024] [Indexed: 06/07/2024]
Abstract
Alzheimer's disease (AD) is one of the most prevalent chronic neurodegenerative disorders globally, with a rapidly growing population of AD patients and currently no effective therapeutic interventions available. Consequently, the development of therapeutic anti-AD drugs and the identification of AD targets represent one of the most urgent tasks. In this study, in addition to considering known drugs and targets, we explore compound-protein interactions (CPIs) between compounds and proteins relevant to AD. We propose a deep learning model called CKG-IMC to predict Alzheimer's disease compound-protein interaction relationships. CKG-IMC comprises three modules: a collaborative knowledge graph (CKG), a principal neighborhood aggregation graph neural network (PNA), and an inductive matrix completion (IMC). The collaborative knowledge graph is used to learn semantic associations between entities, PNA is employed to extract structural features of the relationship network, and IMC is utilized for CPIs prediction. Compared with a total of 16 baseline models based on similarities, knowledge graphs, and graph neural networks, our model achieves state-of-the-art performance in experiments of 10-fold cross-validation and independent test. Furthermore, we use CKG-IMC to predict compounds interacting with two confirmed AD targets, 42-amino-acid β-amyloid (Aβ42) protein and microtubule-associated protein tau (tau protein), as well as proteins interacting with five FDA-approved anti-AD drugs. The results indicate that the majority of predictions are supported by literature, and molecular docking experiments demonstrate a strong affinity between the predicted compounds and targets.
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Affiliation(s)
- Yongna Yuan
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China.
| | - Rizhen Hu
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Siming Chen
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Xiaopeng Zhang
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
| | - Zhenyu Liu
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China; School of Cyberspace Security, Gansu University of Political Science and Law, Anning West Road, Lanzhou, 730070, Gansu, China
| | - Gonghai Zhou
- School of Information Science & Engineering, Lanzhou University, South Tianshui Road, Lanzhou, 730000, Gansu, China
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Li Y, Liang W, Peng L, Zhang D, Yang C, Li KC. Predicting Drug-Target Interactions Via Dual-Stream Graph Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:948-958. [PMID: 36074878 DOI: 10.1109/tcbb.2022.3204188] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Drug target interaction prediction is a crucial stage in drug discovery. However, brute-force search over a compound database is financially infeasible. We have witnessed the increasing measured drug-target interactions records in recent years, and the rich drug/protein-related information allows the usage of graph machine learning. Despite the advances in deep learning-enabled drug-target interaction, there are still open challenges: (1) rich and complex relationship between drugs and proteins can be explored; (2) the intermediate node is not calibrated in the heterogeneous graph. To tackle with above issues, this paper proposed a framework named DSG-DTI. Specifically, DSG-DTI has the heterogeneous graph autoencoder and heterogeneous attention network-based Matrix Completion. Our framework ensures that the known types of nodes (e.g., drug, target, side effects, diseases) are precisely embedded into high-dimensional space with our pretraining skills. Also, the attention-based heterogeneous graph-based matrix completion achieves highly competitive results via effective long-range dependencies extraction. We verify our model on two public benchmarks. The result of two publicly available benchmark application programs show that the proposed scheme effectively predicts drug-target interactions and can generalize to newly registered drugs and targets with slight performance degradation, outperforming the best accuracy compared with other baselines.
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7
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He H, Xie J, Huang D, Zhang M, Zhao X, Ying Y, Wang J. DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network. J Mol Graph Model 2024; 130:108783. [PMID: 38677034 DOI: 10.1016/j.jmgm.2024.108783] [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: 01/09/2024] [Revised: 04/21/2024] [Accepted: 04/23/2024] [Indexed: 04/29/2024]
Abstract
Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging. This study proposes a drug repurposing method based on the ternary heterogeneous graph attention network (DRTerHGAT). DRTerHGAT designs a novel protein feature extraction process consisting of a large-scale protein language model and a multi-task autoencoder, so that protein features can be extracted accurately and efficiently from amino acid sequences. The ternary heterogeneous graph of drug-protein-disease comprehensively considering the relationships among the three types of nodes, including three homogeneous and three heterogeneous relationships. Based on the graph and the extracted protein features, the deep features of the drugs and the diseases are extracted by graph convolutional networks (GCN) and heterogeneous graph node attention networks (HGNA). In the experiments, DRTerHGAT is proven superior to existing advanced methods and DRTerHGAT variants. DRTerHGAT's powerful ability for drug repurposing is also demonstrated in Alzheimer's disease.
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Affiliation(s)
- Hongjian He
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jiang Xie
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Dingkai Huang
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Mengfei Zhang
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Xuyu Zhao
- School of Life Sciences,Shanghai University, Shanghai, China
| | - Yiwei Ying
- School of Life Sciences,Shanghai University, Shanghai, China
| | - Jiao Wang
- School of Life Sciences,Shanghai University, Shanghai, China.
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Wang S, Yin N, Li Y, Ma Z, Lin W, Zhang L, Cui Y, Xia J, Geng L. Molecular mechanism of the treatment of lung adenocarcinoma by Hedyotis Diffusa: an integrative study with real-world clinical data and experimental validation. Front Pharmacol 2024; 15:1355531. [PMID: 38903989 PMCID: PMC11187350 DOI: 10.3389/fphar.2024.1355531] [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: 12/14/2023] [Accepted: 05/15/2024] [Indexed: 06/22/2024] Open
Abstract
Background With a variety of active ingredients, Hedyotis Diffusa (H. diffusa) can treat a variety of tumors. The purpose of our study is based on real-world data and experimental level, to double demonstrate the efficacy and possible molecular mechanism of H. diffusa in the treatment of lung adenocarcinom (LUAD). Methods Phenotype-genotype and herbal-target associations were extracted from the SymMap database. Disease-gene associations were extracted from the MalaCards database. A molecular network-based correlation analysis was further conducted on the collection of genes associated with TCM and the collection of genes associated with diseases and symptoms. Then, the network separation SAB metrics were applied to evaluate the network proximity relationship between TCM and symptoms. Finally, cell apoptosis experiment, Western blot, and Real-time PCR were used for biological experimental level validation analysis. Results Included in the study were 85,437 electronic medical records (318 patients with LUAD). The proportion of prescriptions containing H. diffusa in the LUAD group was much higher than that in the non-LUAD group (p < 0.005). We counted the symptom relief of patients in the group and the group without the use of H. diffusa: except for symptoms such as fatigue, palpitations, and dizziness, the improvement rate of symptoms in the user group was higher than that in the non-use group. We selected the five most frequently occurring symptoms in the use group, namely, cough, expectoration, fatigue, chest tightness and wheezing. We combined the above five symptom genes into one group. The overlapping genes obtained were CTNNB1, STAT3, CASP8, and APC. The selection of CTNNB1 target for biological experiments showed that the proliferation rate of LUAD A549 cells in the drug intervention group was significantly lower than that in the control group, and it was concentration-dependent. H. diffusa can promote the apoptosis of A549 cells, and the apoptosis rate of the high-concentration drug group is significantly higher than that of the low-concentration drug group. The transcription and expression level of CTNNB1 gene in the drug intervention group were significantly decreased. Conclusion H. diffusa inhibits the proliferation and promotes apoptosis of LUAD A549 cells, which may be related to the fact that H. diffusa can regulate the expression of CTNNB1.
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Affiliation(s)
- Sheng Wang
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Na Yin
- School of Medicine, Henan University of Chinese Medicine, Zhengzhou, China
| | - Yingyue Li
- Medical Engineering Technology and Data Mining Institute, Zhengzhou University, Zhengzhou, China
| | - Zhaohang Ma
- School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China
| | - Wei Lin
- School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China
| | - Lihong Zhang
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Yun Cui
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Jianan Xia
- School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China
| | - Liang Geng
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
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Yang Z, Liu J, Yang F, Zhang X, Zhang Q, Zhu X, Jiang P. Advancing Drug-Target Interaction prediction with BERT and subsequence embedding. Comput Biol Chem 2024; 110:108058. [PMID: 38593480 DOI: 10.1016/j.compbiolchem.2024.108058] [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: 08/08/2023] [Revised: 02/01/2024] [Accepted: 03/12/2024] [Indexed: 04/11/2024]
Abstract
Exploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding proteins by amino acids, we use amino acid subsequence to encode proteins, which simulates the biological process of DTI better. For this research purpose, we proposed a novel deep learning framework based on Bidirectional Encoder Representation from Transformers (BERT), which integrates high-frequency subsequence embedding and transfer learning methods to complete the DTI prediction task. As the first key module, subsequence embedding allows to explore the functional interaction units from drug and protein sequences and then contribute to finding DTI modules. As the second key module, transfer learning promotes the model learn the common DTI features from protein and drug sequences in a large dataset. Overall, the BERT-based model can learn two kinds features through the multi-head self-attention mechanism: internal features of sequence and interaction features of both proteins and drugs, respectively. Compared with other methods, BERT-based methods enable more DTI-related features to be discovered by means of attention scores which associated with tokenized protein/drug subsequences. We conducted extensive experiments for the DTI prediction task on three different benchmark datasets. The experimental results show that the model achieves an average prediction metrics higher than most baseline methods. In order to verify the importance of transfer learning, we conducted an ablation study on datasets, and the results show the superiority of transfer learning. In addition, we test the scalability of the model on the dataset in unseen drugs and proteins, and the results of the experiments show that it is acceptable in scalability.
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Affiliation(s)
- Zhihui Yang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, Hubei province, China
| | - Juan Liu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, Hubei province, China.
| | - Feng Yang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, Hubei province, China
| | - Xiaolei Zhang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, Hubei province, China
| | - Qiang Zhang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, Hubei province, China
| | - Xuekai Zhu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, Hubei province, China
| | - Peng Jiang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, Hubei province, China
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Zhao W, Yu Y, Liu G, Liang Y, Xu D, Feng X, Guan R. MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention. Brief Bioinform 2024; 25:bbae238. [PMID: 38762789 PMCID: PMC11102638 DOI: 10.1093/bib/bbae238] [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: 01/29/2024] [Revised: 04/09/2024] [Accepted: 05/03/2024] [Indexed: 05/20/2024] Open
Abstract
Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.
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Affiliation(s)
- Wenchuan Zhao
- Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin, China
| | - Yufeng Yu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin, China
| | - Guosheng Liu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin, China
| | - Yanchun Liang
- Zhuhai Laboratory of the Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Zhuhai College of Science and Technology, Zhuhai 519041, China
| | - Dong Xu
- Department of Computer Science, Informatics Institute, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Xiaoyue Feng
- Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin, China
| | - Renchu Guan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin, China
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Qi H, Yu T, Yu W, Liu C. Drug-target affinity prediction with extended graph learning-convolutional networks. BMC Bioinformatics 2024; 25:75. [PMID: 38365583 PMCID: PMC10874073 DOI: 10.1186/s12859-024-05698-6] [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: 01/15/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research. The prediction of drug-target affinity (DTA) is a crucial stage in this process, potentially accelerating drug development through rapid and extensive preliminary compound screening, while also minimizing resource utilization and costs. Recently, the incorporation of deep learning into DTA prediction and the enhancement of its accuracy have emerged as key areas of interest in the research community. Drugs and targets can be characterized through various methods, including structure-based, sequence-based, and graph-based representations. Despite the progress in structure and sequence-based techniques, they tend to provide limited feature information. Conversely, graph-based approaches have risen to prominence, attracting considerable attention for their comprehensive data representation capabilities. Recent studies have focused on constructing protein and drug molecular graphs using sequences and SMILES, subsequently deriving representations through graph neural networks. However, these graph-based approaches are limited by the use of a fixed adjacent matrix of protein and drug molecular graphs for graph convolution. This limitation restricts the learning of comprehensive feature representations from intricate compound and protein structures, consequently impeding the full potential of graph-based feature representation in DTA prediction. This, in turn, significantly impacts the models' generalization capabilities in the complex realm of drug discovery. RESULTS To tackle these challenges, we introduce GLCN-DTA, a model specifically designed for proficiency in DTA tasks. GLCN-DTA innovatively integrates a graph learning module into the existing graph architecture. This module is designed to learn a soft adjacent matrix, which effectively and efficiently refines the contextual structure of protein and drug molecular graphs. This advancement allows for learning richer structural information from protein and drug molecular graphs via graph convolution, specifically tailored for DTA tasks, compared to the conventional fixed adjacent matrix approach. A series of experiments have been conducted to validate the efficacy of the proposed GLCN-DTA method across diverse scenarios. The results demonstrate that GLCN-DTA possesses advantages in terms of robustness and high accuracy. CONCLUSIONS The proposed GLCN-DTA model enhances DTA prediction performance by introducing a novel framework that synergizes graph learning operations with graph convolution operations, thereby achieving richer representations. GLCN-DTA does not distinguish between different protein classifications, including structurally ordered and intrinsically disordered proteins, focusing instead on improving feature representation. Therefore, its applicability scope may be more effective in scenarios involving structurally ordered proteins, while potentially being limited in contexts with intrinsically disordered proteins.
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Affiliation(s)
- Haiou Qi
- Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Ting Yu
- Operating Room Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.
| | - Wenwen Yu
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Chenxi Liu
- School of Medicine and Health Management, Tongji Medical School, Huazhong University of Science and Technology, Wuhan, 430030, China
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12
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Baptista A, Brière G, Baudot A. Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond. BMC Bioinformatics 2024; 25:70. [PMID: 38355439 PMCID: PMC10865648 DOI: 10.1186/s12859-024-05683-z] [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: 10/18/2023] [Accepted: 01/29/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank. RESULTS We first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics. CONCLUSION Overall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.
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Affiliation(s)
- Anthony Baptista
- School of Mathematical Sciences, Queen Mary University of London, London, UK.
- The Alan Turing Institute, London, UK.
| | | | - Anaïs Baudot
- INSERM, MMG, Turing Center for Living Systems, Aix-Marseille Univ, Marseille, France.
- Barcelona Supercomputing Center, Barcelona, Spain.
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13
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Hu L, Zhang M, Hu P, Zhang J, Niu C, Lu X, Jiang X, Ma Y. Dual-channel hypergraph convolutional network for predicting herb-disease associations. Brief Bioinform 2024; 25:bbae067. [PMID: 38426326 PMCID: PMC10939431 DOI: 10.1093/bib/bbae067] [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: 09/11/2023] [Revised: 01/26/2024] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.
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Affiliation(s)
- Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Menglong Zhang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Pengwei Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Jun Zhang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
| | - Chao Niu
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory Basis of Xinjiang Indigenous Medicinal Plants Resource Utilization, Key Laboratory of Chemistry of Plant Resources in Arid Regions, Xinjiang Technical Institute of Physicsand Chemistry,Chinese Academy of Sciences Urumqi, China
| | - Xueying Lu
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory Basis of Xinjiang Indigenous Medicinal Plants Resource Utilization, Key Laboratory of Chemistry of Plant Resources in Arid Regions, Xinjiang Technical Institute of Physicsand Chemistry,Chinese Academy of Sciences Urumqi, China
| | - Xiangrui Jiang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica,Chinese Academy of Sciences Shanghai, China
| | - Yupeng Ma
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi China
- University of Chinese Academy of Sciences, Beijing, China
- Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China
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14
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Yu Z, Wu Z, Wang Z, Wang Y, Zhou M, Li W, Liu G, Tang Y. Network-Based Methods and Their Applications in Drug Discovery. J Chem Inf Model 2024; 64:57-75. [PMID: 38150548 DOI: 10.1021/acs.jcim.3c01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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15
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Arora S, Satija S, Mittal A, Solanki S, Mohanty SK, Srivastava V, Sengupta D, Rout D, Arul Murugan N, Borkar RM, Ahuja G. Unlocking The Mysteries of DNA Adducts with Artificial Intelligence. Chembiochem 2024; 25:e202300577. [PMID: 37874183 DOI: 10.1002/cbic.202300577] [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: 08/16/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 10/25/2023]
Abstract
Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.
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Affiliation(s)
- Sakshi Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Shiva Satija
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Saveena Solanki
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Sanjay Kumar Mohanty
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Vaibhav Srivastava
- Division of Glycoscience, Department of Chemistry CBH School, Royal Institute of Technology (KTH) AlbaNova University Center, 10691, Stockholm, Sweden
| | - Debarka Sengupta
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Diptiranjan Rout
- Department of Transfusion Medicine National Cancer Institute, AIIMS, New Delhi, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110608, India
| | - Natarajan Arul Murugan
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Roshan M Borkar
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER)-Guwahati, Sila Katamur Halugurisuk P.O.: Changsari, Dist, Guwahati, Assam, 781101, India
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
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16
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Son J, Kim D. Applying network link prediction in drug discovery: an overview of the literature. Expert Opin Drug Discov 2024; 19:43-56. [PMID: 37794688 DOI: 10.1080/17460441.2023.2267020] [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: 06/08/2023] [Accepted: 10/02/2023] [Indexed: 10/06/2023]
Abstract
INTRODUCTION Network representation can give a holistic view of relationships for biomedical entities through network topology. Link prediction estimates the probability of link formation between the pair of unconnected nodes. In the drug discovery process, the link prediction method not only enables the detection of connectivity patterns but also predicts the effects of one biomedical entity to multiple entities simultaneously and vice versa, which is useful for many applications. AREAS COVERED The authors provide a comprehensive overview of network link prediction in drug discovery. Link prediction methodologies such as similarity-based approaches, embedding-based approaches, probabilistic model-based approaches, and preprocessing methods are summarized with examples. In addition to describing their properties and limitations, the authors discuss the applications of link prediction in drug discovery based on the relationship between biomedical concepts. EXPERT OPINION Link prediction is a powerful method to infer the existence of novel relationships in drug discovery. However, link prediction has been hampered by the sparsity of data and the lack of negative links in biomedical networks. With preprocessing to balance positive and negative samples and the collection of more data, the authors believe it is possible to develop more reliable link prediction methods that can become invaluable tools for successful drug discovery.
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Affiliation(s)
- Jeongtae Son
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Dongsup Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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17
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Li H, Wang S, Zheng W, Yu L. Multi-dimensional search for drug-target interaction prediction by preserving the consistency of attention distribution. Comput Biol Chem 2023; 107:107968. [PMID: 37844375 DOI: 10.1016/j.compbiolchem.2023.107968] [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: 08/06/2023] [Revised: 09/27/2023] [Accepted: 10/05/2023] [Indexed: 10/18/2023]
Abstract
Predicting drug-target interaction (DTI) is a crucial step in the process of drug repurposing and new drug development. Although the attention mechanism has been widely used to capture the interactions between drugs and targets, it mainly uses the Simplified Molecular Input Line Entry System (SMILES) and two-dimensional (2D) molecular graph features of drugs. In this paper, we propose a neural network model called MdDTI for DTI prediction. The model searches for binding sites that may interact with the target from the multiple dimensions of drug structure, namely the 2D substructures and the three-dimensional (3D) spatial structure. For the 2D substructures, we have developed a novel substructure decomposition strategy based on drug molecular graphs and compared its performance with the SMILES-based decomposition method. For the 3D spatial structure of drugs, we constructed spatial feature representation matrices for drugs based on the Cartesian coordinates of heavy atoms (without hydrogen atoms) in each drug. Finally, to ensure the search results of the model are consistent across multiple dimensions, we construct a consistency loss function. We evaluate MdDTI on four drug-target interaction datasets and three independent compound-protein affinity test sets. The results indicate that our model surpasses a series of state-of-the-art models. Case studies demonstrate that our model is capable of capturing the potential binding regions between drugs and targets, and it shows efficacy in drug repurposing. Our code is available at https://github.com/lhhu1999/MdDTI.
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Affiliation(s)
- Huaihu Li
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
| | - Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China; The Key Lab of Intelligent Systems and Computing of Yunnan Province, Yunnan University, Kunming, Yunnan, China.
| | - Weihua Zheng
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
| | - Li Yu
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
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18
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Su D, Xiong Y, Wang S, Wei H, Ke J, Li H, Wang T, Zuo Y, Yang L. Structural deep clustering network for stratification of breast cancer patients through integration of somatic mutation profiles. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107808. [PMID: 37716222 DOI: 10.1016/j.cmpb.2023.107808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/15/2023] [Accepted: 09/10/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Breast cancer is among of the most malignant tumor that occurs in women and is one of the leading causes of death from gynecologic malignancy worldwide. The high degree of heterogeneity that characterizes breast cancer makes it challenging to devise effective therapeutic strategies. Accumulating evidence highlights the crucial role of stratifying breast cancer patients into clinically significant subtypes to achieve better prognoses and treatments. The structural deep clustering network is a graph convolutional network-based clustering algorithm that integrates structural information and has achieved state-of-the-art performance in various applications. METHODS In this study, we employed structural deep clustering network to integrate somatic mutation profiles for stratifying 2526 breast cancer patients from the Memorial Sloan Kettering Cancer Center into two clinically differentiable subtypes. RESULTS Breast cancer patients in cluster 1 exhibited better prognosis than breast cancer patients in cluster 2, and the difference between them was statistically significant. The immunogenomic landscape further demonstrated that cluster 1 was associated with remarkable infiltration of the tumor infiltrating lymphocytes. The clustering subtype could be used to evaluate the therapeutic benefit of immunotherapy and chemotherapy in breast cancer patients. Furthermore, our approach effectively classified patients from eight different cancer types, demonstrating its generalizability. CONCLUSIONS Our study represents a step towards a generic methodology for classifying cancer patients using only somatic mutation data and structural deep clustering network approaches. Employing structural deep clustering network to identify breast cancer subtypes is promising and can inform the development of more accurate and personalized therapies.
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Affiliation(s)
- Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Haodong Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Jiawei Ke
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Honghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Tao Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China; Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd. Hohhot, 010010, China; Inner Mongolia International Mongolian Hospital, Hohhot 010065, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
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Zhao Y, Yin J, Zhang L, Zhang Y, Chen X. Drug-drug interaction prediction: databases, web servers and computational models. Brief Bioinform 2023; 25:bbad445. [PMID: 38113076 PMCID: PMC10782925 DOI: 10.1093/bib/bbad445] [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: 07/21/2023] [Revised: 10/26/2023] [Accepted: 11/14/2023] [Indexed: 12/21/2023] Open
Abstract
In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.
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Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yong Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi 214122, China
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Su Y, Hu Z, Wang F, Bin Y, Zheng C, Li H, Chen H, Zeng X. AMGDTI: drug-target interaction prediction based on adaptive meta-graph learning in heterogeneous network. Brief Bioinform 2023; 25:bbad474. [PMID: 38145949 PMCID: PMC10749791 DOI: 10.1093/bib/bbad474] [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: 09/12/2023] [Revised: 11/10/2023] [Accepted: 11/30/2023] [Indexed: 12/27/2023] Open
Abstract
Prediction of drug-target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug-target relationship.
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Affiliation(s)
- Yansen Su
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Zhiyang Hu
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Fei Wang
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Yannan Bin
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Chunhou Zheng
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Haitao Li
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Haowen Chen
- College of Computer Science and Electronic Engineering, Hunan University, Hunan, 410082, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Hunan, 410082, China
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Xie S, Xie X, Zhao X, Liu F, Wang Y, Ping J, Ji Z. HNSPPI: a hybrid computational model combing network and sequence information for predicting protein-protein interaction. Brief Bioinform 2023; 24:bbad261. [PMID: 37480553 DOI: 10.1093/bib/bbad261] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 06/24/2023] [Accepted: 06/26/2023] [Indexed: 07/24/2023] Open
Abstract
Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein-Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data.
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Affiliation(s)
- Shijie Xie
- College of Artificial Intelligence, Nanjing Agricultural University, No. 1 Weigang Rd, Nanjing, Jiangsu 210095, China
| | - Xiaojun Xie
- College of Artificial Intelligence, Nanjing Agricultural University, No. 1 Weigang Rd, Nanjing, Jiangsu 210095, China
| | - Xin Zhao
- Department of Hepatobiliary Surgery, Beijing Chaoyang Hospital affiliated to Capital Medical University, Beijing 100020, China
| | - Fei Liu
- Joint International Research Laboratory of Animal Health and Food Safety of Ministry of Education & Single Molecule Nanometry Laboratory (Sinmolab), Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Yiming Wang
- Key Laboratory of Biological Interactions and Crop Health, Department of Plant Pathology, Nanjing Agricultural University, 210095, Nanjing, China
| | - Jihui Ping
- MOE International Joint Collaborative Research Laboratory for Animal Health and Food Safety & Jiangsu Engineering Laboratory of Animal Immunology, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Zhiwei Ji
- College of Artificial Intelligence, Nanjing Agricultural University, No. 1 Weigang Rd, Nanjing, Jiangsu 210095, China
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Qian Y, Li X, Wu J, Zhang Q. MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug-target interaction. BMC Bioinformatics 2023; 24:323. [PMID: 37633938 PMCID: PMC10463755 DOI: 10.1186/s12859-023-05447-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 08/15/2023] [Indexed: 08/28/2023] Open
Abstract
BACKGROUND Prediction of drug-target interaction (DTI) is an essential step for drug discovery and drug reposition. Traditional methods are mostly time-consuming and labor-intensive, and deep learning-based methods address these limitations and are applied to engineering. Most of the current deep learning methods employ representation learning of unimodal information such as SMILES sequences, molecular graphs, or molecular images of drugs. In addition, most methods focus on feature extraction from drug and target alone without fusion learning from drug-target interacting parties, which may lead to insufficient feature representation. MOTIVATION In order to capture more comprehensive drug features, we utilize both molecular image and chemical features of drugs. The image of the drug mainly has the structural information and spatial features of the drug, while the chemical information includes its functions and properties, which can complement each other, making drug representation more effective and complete. Meanwhile, to enhance the interactive feature learning of drug and target, we introduce a bidirectional multi-head attention mechanism to improve the performance of DTI. RESULTS To enhance feature learning between drugs and targets, we propose a novel model based on deep learning for DTI task called MCL-DTI which uses multimodal information of drug and learn the representation of drug-target interaction for drug-target prediction. In order to further explore a more comprehensive representation of drug features, this paper first exploits two multimodal information of drugs, molecular image and chemical text, to represent the drug. We also introduce to use bi-rectional multi-head corss attention (MCA) method to learn the interrelationships between drugs and targets. Thus, we build two decoders, which include an multi-head self attention (MSA) block and an MCA block, for cross-information learning. We use a decoder for the drug and target separately to obtain the interaction feature maps. Finally, we feed these feature maps generated by decoders into a fusion block for feature extraction and output the prediction results. CONCLUSIONS MCL-DTI achieves the best results in all the three datasets: Human, C. elegans and Davis, including the balanced datasets and an unbalanced dataset. The results on the drug-drug interaction (DDI) task show that MCL-DTI has a strong generalization capability and can be easily applied to other tasks.
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Affiliation(s)
- Ying Qian
- Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Computer Science and Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062 China
| | - Xinyi Li
- Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Computer Science and Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062 China
| | - Jian Wu
- Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Computer Science and Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062 China
| | - Qian Zhang
- Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Computer Science and Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062 China
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Ren X, Yan CX, Zhai RX, Xu K, Li H, Fu XJ. Comprehensive survey of target prediction web servers for Traditional Chinese Medicine. Heliyon 2023; 9:e19151. [PMID: 37664753 PMCID: PMC10468387 DOI: 10.1016/j.heliyon.2023.e19151] [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: 05/30/2022] [Revised: 07/27/2023] [Accepted: 08/14/2023] [Indexed: 09/05/2023] Open
Abstract
Traditional Chinese medicine (TCM) is characterized by multi-components, multiple targets, and complex mechanisms of action and therefore has significant advantages in treating diseases. However, the clinical application of TCM prescriptions is limited due to the difficulty in elucidating the effective substances and the lack of current scientific evidence on the mechanisms of action. In recent years, the development of network pharmacology based on drug systems research has provided a new approach for understanding the complex systems represented by TCM. The determination of drug targets is the core of TCM network pharmacology research. Over the past years, many web tools for drug targets with various features have been developed to facilitate target prediction, significantly promoting drug discovery. Therefore, this review introduces the widely used web tools for compound-target interaction prediction databases and web resources in TCM pharmacology research, and it compares and analyzes each web tool based on their basic properties, including the underlying theory, algorithms, datasets, and search results. Finally, we present the remaining challenges for the promising future of compound-target interaction prediction in TCM pharmacology research. This work may guide researchers in choosing web tools for target prediction and may also help develop more TCM tools based on these existing resources.
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Affiliation(s)
- Xia Ren
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
| | - Chun-Xiao Yan
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
| | - Run-Xiang Zhai
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
| | - Kuo Xu
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
| | - Hui Li
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
| | - Xian-Jun Fu
- Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Marine traditional Chinese medicine r research center, Qingdao Academy of Traditional Chinese medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266114, China
- Shandong Engineering and Technology Research Center of Traditional Chinese Medicine, Jinan 250355, China
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Zhang Y, Feng Y, Wu M, Deng Z, Wang S. VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder. BMC Bioinformatics 2023; 24:278. [PMID: 37415176 DOI: 10.1186/s12859-023-05387-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 06/16/2023] [Indexed: 07/08/2023] Open
Abstract
MOTIVATION Accurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the model. RESULTS To solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoencoder (VGAE) which is used to infer feature representations from drug and target spaces. The second is graph autoencoder (GAE) propagating labels between known DTIs. Experimental results on two public datasets show that the prediction accuracy of VGAEDTI is better than that of six DTIs prediction methods. These results indicate that model can predict new DTIs and provide an effective tool for accelerating drug development and repurposing.
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Affiliation(s)
- Yuanyuan Zhang
- Yinfei Feng Qingdao University of Technology, Qingdao, China
| | - Yinfei Feng
- Yinfei Feng Qingdao University of Technology, Qingdao, China.
| | - Mengjie Wu
- Yinfei Feng Qingdao University of Technology, Qingdao, China
| | - Zengqian Deng
- Yinfei Feng Qingdao University of Technology, Qingdao, China
| | - Shudong Wang
- School of Computer Science and Technology, China University of Petroleum, Qingdao, China
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Bolz SN, Schroeder M. Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances. Expert Opin Drug Discov 2023; 18:973-985. [PMID: 37489516 DOI: 10.1080/17460441.2023.2239700] [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: 06/09/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
INTRODUCTION Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks. AREAS COVERED This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets. EXPERT OPINION Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.
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Affiliation(s)
- Sarah Naomi Bolz
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
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Zhou L, Wang Y, Peng L, Li Z, Luo X. Identifying potential drug-target interactions based on ensemble deep learning. Front Aging Neurosci 2023; 15:1176400. [PMID: 37396659 PMCID: PMC10309650 DOI: 10.3389/fnagi.2023.1176400] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/10/2023] [Indexed: 07/04/2023] Open
Abstract
Introduction Drug-target interaction prediction is one important step in drug research and development. Experimental methods are time consuming and laborious. Methods In this study, we developed a novel DTI prediction method called EnGDD by combining initial feature acquisition, dimensional reduction, and DTI classification based on Gradient boosting neural network, Deep neural network, and Deep Forest. Results EnGDD was compared with seven stat-of-the-art DTI prediction methods (BLM-NII, NRLMF, WNNGIP, NEDTP, DTi2Vec, RoFDT, and MolTrans) on the nuclear receptor, GPCR, ion channel, and enzyme datasets under cross validations on drugs, targets, and drug-target pairs, respectively. EnGDD computed the best recall, accuracy, F1-score, AUC, and AUPR under the majority of conditions, demonstrating its powerful DTI identification performance. EnGDD predicted that D00182 and hsa2099, D07871 and hsa1813, DB00599 and hsa2562, D00002 and hsa10935 have a higher interaction probabilities among unknown drug-target pairs and may be potential DTIs on the four datasets, respectively. In particular, D00002 (Nadide) was identified to interact with hsa10935 (Mitochondrial peroxiredoxin3) whose up-regulation might be used to treat neurodegenerative diseases. Finally, EnGDD was used to find possible drug targets for Parkinson's disease and Alzheimer's disease after confirming its DTI identification performance. The results show that D01277, D04641, and D08969 may be applied to the treatment of Parkinson's disease through targeting hsa1813 (dopamine receptor D2) and D02173, D02558, and D03822 may be the clues of treatment for patients with Alzheimer's disease through targeting hsa5743 (prostaglandinendoperoxide synthase 2). The above prediction results need further biomedical validation. Discussion We anticipate that our proposed EnGDD model can help discover potential therapeutic clues for various diseases including neurodegenerative diseases.
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Affiliation(s)
- Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Yuzhuang Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
| | - Xueming Luo
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
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Zhao L, Zhang H, Li N, Chen J, Xu H, Wang Y, Liang Q. Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula. JOURNAL OF ETHNOPHARMACOLOGY 2023; 309:116306. [PMID: 36858276 DOI: 10.1016/j.jep.2023.116306] [Citation(s) in RCA: 167] [Impact Index Per Article: 167.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/06/2023] [Accepted: 02/19/2023] [Indexed: 05/20/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Network pharmacology is a new discipline based on systems biology theory, biological system network analysis, and multi-target drug molecule design specific signal node selection. The mechanism of action of TCM formula has the characteristics of multiple targets and levels. The mechanism is similar to the integrity, systematization and comprehensiveness of network pharmacology, so network pharmacology is suitable for the study of the pharmacological mechanism of Chinese medicine compounds. AIM OF THE STUDY The paper summarizes the present application status and existing problems of network pharmacology in the field of Chinese medicine formula, and formulates the research ideas, up-to-date key technology and application method and strategy of network pharmacology. Its purpose is to provide guidance and reference for using network pharmacology to reveal the modern scientific connotation of Chinese medicine. MATERIALS AND METHODS Literatures in this review were searched in PubMed, China National Knowledge Infrastructure (CNKI), Web of Science, ScienceDirect and Google Scholar using the keywords "traditional Chinese medicine", "Chinese herb medicine" and "network pharmacology". The literature cited in this review dates from 2002 to 2022. RESULTS Using network pharmacology methods to predict the basis and mechanism of pharmacodynamic substances of traditional Chinese medicines has become a trend. CONCLUSION Network pharmacology is a promising approach to reveal the pharmacology mechanism of Chinese medicine formula.
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Affiliation(s)
- Li Zhao
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Hong Zhang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Ning Li
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Jinman Chen
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Hao Xu
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Yongjun Wang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
| | - Qianqian Liang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
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28
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Tang C, Zhong C, Wang M, Zhou F. FMGNN: A Method to Predict Compound-Protein Interaction With Pharmacophore Features and Physicochemical Properties of Amino Acids. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1030-1040. [PMID: 35503835 DOI: 10.1109/tcbb.2022.3172340] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Identifying interactions between compounds and proteins is an essential task in drug discovery. To recommend compounds as new drug candidates, applying the computational approaches has a lower cost than conducting the wet-lab experiments. Machine learning-based methods, especially deep learning-based methods, have advantages in learning complex feature interactions between compounds and proteins. However, deep learning models will over-generalize and lead to the problem of predicting less relevant compound-protein pairs when the compound-protein feature interactions are high-dimensional sparse. This problem can be overcome by learning both low-order and high-order feature interactions. In this paper, we propose a novel hybrid model with Factorization Machines and Graph Neural Network called FMGNN to extract the low-order and high-order features, respectively. Then, we design a compound-protein interactions (CPIs) prediction method with pharmacophore features of compound and physicochemical properties of amino acids. The pharmacophore features can ensure that the prediction results much more fit the expectation of biological experiment and the physicochemical properties of amino acids are loaded into the embedding layer to improve the convergence speed and accuracy of protein feature learning. The experimental results on several datasets, especially on an imbalanced large-scale dataset, showed that our proposed method outperforms other existing methods for CPI prediction. The western blot experiment results on wogonin and its candidate target proteins also showed that our proposed method is effective and accurate for finding target proteins. The computer program of implementing the model FMGNN is available at https://github.com/tcygxu2021/FMGNN.
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Hu L, Fu C, Ren Z, Cai Y, Yang J, Xu S, Xu W, Tang D. SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction. BMC Bioinformatics 2023; 24:38. [PMID: 36737694 PMCID: PMC9896467 DOI: 10.1186/s12859-023-05153-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 01/18/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed. METHODS In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine. RESULTS The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively. CONCLUSION The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.
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Affiliation(s)
- Lingzhi Hu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Chengzhou Fu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Zhonglu Ren
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Yongming Cai
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Jin Yang
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Siwen Xu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Wenhua Xu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Deyu Tang
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,grid.79703.3a0000 0004 1764 3838School of Computer Science and Engineering, South China University of Technology, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
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30
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Zhang J, Chen M, Liu J, Peng D, Dai Z, Zou X, Li Z. A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug-Drug Interactions. Molecules 2023; 28:molecules28031490. [PMID: 36771157 PMCID: PMC9919258 DOI: 10.3390/molecules28031490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
The identification of drug-drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (KGCNs) with neural factorization machines (NFMs). A KGCN is used to learn the embedding representation containing high-order structural information and semantic information in the knowledge graph (KG). The embedding and the Morgan molecular fingerprint of drugs are then used as input of NFMs to predict DDIs. The performance and effectiveness of the current method have been evaluated and confirmed based on the two real-world datasets with different sizes, and the results demonstrate that KGCN_NFM outperforms the state-of-the-art algorithms. Moreover, the identified interactions between topotecan and dantron by KGCN_NFM were validated through MTT assays, apoptosis experiments, cell cycle analysis, and molecular docking. Our study shows that the combination therapy of the two drugs exerts a synergistic anticancer effect, which provides an effective treatment strategy against lung carcinoma. These results reveal that KGCN_NFM is a valuable tool for integrating heterogeneous information to identify potential DDIs.
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Affiliation(s)
- Jing Zhang
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China
| | - Meng Chen
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Jie Liu
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China
| | - Dongdong Peng
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China
| | - Zong Dai
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Xiaoyong Zou
- School of Chemistry, Sun Yat-sen University, Guangzhou 510275, China
- Correspondence: (X.Z.); (Z.L.)
| | - Zhanchao Li
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China
- Key Laboratory of Digital Quality Evaluation of Traditional Chinese Medicine, National Administration of Traditional Chinese Medicine, Guangzhou 510006, China
- Correspondence: (X.Z.); (Z.L.)
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Budak C, Mençik V, Gider V. Determining similarities of COVID-19 - lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA method. J Biomol Struct Dyn 2023; 41:659-671. [PMID: 34877907 DOI: 10.1080/07391102.2021.2010601] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
COVID-19 is a worldwide health crisis seriously endangering the arsenal of antiviral and antibiotic drugs. It is urgent to find an effective antiviral drug against pandemic caused by the severe acute respiratory syndrome (Sars-Cov-2), which increases global health concerns. As it can be expensive and time-consuming to develop specific antiviral drugs, reuse of FDA-approved drugs that provide an opportunity to rapidly distribute effective therapeutics can allow to provide treatments with known preclinical, pharmacokinetic, pharmacodynamic and toxicity profiles that can quickly enter in clinical trials. In this study, using the structural information of molecules and proteins, a list of repurposed drug candidates was prepared again with the graph neural network-based GEFA model. The data set from the public databases DrugBank and PubChem were used for analysis. Using the Tanimoto/jaccard similarity analysis, a list of similar drugs was prepared by comparing the drugs used in the treatment of COVID-19 with the drugs used in the treatment of other diseases. The resultant drugs were compared with the drugs used in lung cancer and repurposed drugs were obtained again by calculating the binding strength between a drug and a target. The kinase inhibitors (erlotinib, lapatinib, vandetanib, pazopanib, cediranib, dasatinib, linifanib and tozasertib) obtained from the study can be used as an alternative for the treatment of COVID-19, as a combination of blocking agents (gefitinib, osimertinib, fedratinib, baricitinib, imatinib, sunitinib and ponatinib) such as ABL2, ABL1, EGFR, AAK1, FLT3 and JAK1, or antiviral therapies (ribavirin, ritonavir-lopinavir and remdesivir).Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Cafer Budak
- Department of Biomedical Engineering, Dicle University, Diyarbakır, Turkey
| | - Vasfiye Mençik
- Department of Electric-Electronic Engineering, Dicle University, Diyarbakır, Turkey
| | - Veysel Gider
- Department of Electric-Electronic Engineering, Dicle University, Diyarbakır, Turkey
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Li G, Zhang P, Sun W, Xu J, Hu L, Zhang W. GA-ENs: A novel drug-target interactions prediction method by incorporating prior Knowledge Graph into dual Wasserstein Generative Adversarial Network with gradient penalty. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
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Ren ZH, You ZH, Zou Q, Yu CQ, Ma YF, Guan YJ, You HR, Wang XF, Pan J. DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis. J Transl Med 2023; 21:48. [PMID: 36698208 PMCID: PMC9876420 DOI: 10.1186/s12967-023-03876-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 01/05/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.
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Affiliation(s)
- Zhong-Hao Ren
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Zhu-Hong You
- grid.440588.50000 0001 0307 1240School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129 China
| | - Quan Zou
- grid.54549.390000 0004 0369 4060Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054 China
| | - Chang-Qing Yu
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Yan-Fang Ma
- grid.417234.70000 0004 1808 3203Department of Galactophore, The Third People’s Hospital of Gansu Province, Lanzhou, 730020 China
| | - Yong-Jian Guan
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Hai-Ru You
- grid.440588.50000 0001 0307 1240School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129 China
| | - Xin-Fei Wang
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Jie Pan
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
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Peng Y, Zhao S, Zeng Z, Hu X, Yin Z. LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions. Front Microbiol 2023; 13:1092467. [PMID: 36687573 PMCID: PMC9849804 DOI: 10.3389/fmicb.2022.1092467] [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: 11/08/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023] Open
Abstract
Prediction of drug-target interactions (DTIs) plays an important role in drug development. However, traditional laboratory methods to determine DTIs require a lot of time and capital costs. In recent years, many studies have shown that using machine learning methods to predict DTIs can speed up the drug development process and reduce capital costs. An excellent DTI prediction method should have both high prediction accuracy and low computational cost. In this study, we noticed that the previous research based on deep forests used XGBoost as the estimator in the cascade, we applied LightGBM instead of XGBoost to the cascade forest as the estimator, then the estimator group was determined experimentally as three LightGBMs and three ExtraTrees, this new model is called LGBMDF. We conducted 5-fold cross-validation on LGBMDF and other state-of-the-art methods using the same dataset, and compared their Sn, Sp, MCC, AUC and AUPR. Finally, we found that our method has better performance and faster calculation speed.
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Lei S, Lei X, Liu L. Drug repositioning based on heterogeneous networks and variational graph autoencoders. Front Pharmacol 2022; 13:1056605. [PMID: 36618933 PMCID: PMC9812491 DOI: 10.3389/fphar.2022.1056605] [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: 09/29/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
Predicting new therapeutic effects (drug repositioning) of existing drugs plays an important role in drug development. However, traditional wet experimental prediction methods are usually time-consuming and costly. The emergence of more and more artificial intelligence-based drug repositioning methods in the past 2 years has facilitated drug development. In this study we propose a drug repositioning method, VGAEDR, based on a heterogeneous network of multiple drug attributes and a variational graph autoencoder. First, a drug-disease heterogeneous network is established based on three drug attributes, disease semantic information, and known drug-disease associations. Second, low-dimensional feature representations for heterogeneous networks are learned through a variational graph autoencoder module and a multi-layer convolutional module. Finally, the feature representation is fed to a fully connected layer and a Softmax layer to predict new drug-disease associations. Comparative experiments with other baseline methods on three datasets demonstrate the excellent performance of VGAEDR. In the case study, we predicted the top 10 possible anti-COVID-19 drugs on the existing drug and disease data, and six of them were verified by other literatures.
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Wang M, Wang J, Weng G, Kang Y, Pan P, Li D, Deng Y, Li H, Hsieh CY, Hou T. ReMODE: a deep learning-based web server for target-specific drug design. J Cheminform 2022; 14:84. [PMID: 36510307 PMCID: PMC9743675 DOI: 10.1186/s13321-022-00665-w] [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: 09/14/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022] Open
Abstract
Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior developments in de novo drug design. However, data availability, deep data processing, and the lack of user-friendly DL tools and interfaces make it difficult to apply these DL techniques to drug design. We hereby present ReMODE (Receptor-based MOlecular DEsign), a new web server based on DL algorithm for target-specific ligand design, which integrates different functional modules to enable users to develop customizable drug design tasks. As designed, the ReMODE sever can construct the target-specific tasks toward the protein targets selected by users. Meanwhile, the server also provides some extensions: users can optimize the drug-likeness or synthetic accessibility of the generated molecules, and control other physicochemical properties; users can also choose a sub-structure/scaffold as a starting point for fragment-based drug design. The ReMODE server also enables users to optimize the pharmacophore matching and docking conformations of the generated molecules. We believe that the ReMODE server will benefit researchers for drug discovery. ReMODE is publicly available at http://cadd.zju.edu.cn/relation/remode/ .
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Affiliation(s)
- Mingyang Wang
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China ,CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Jike Wang
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China ,CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Gaoqi Weng
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China ,CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Yu Kang
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China
| | - Peichen Pan
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China
| | - Dan Li
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Honglin Li
- grid.28056.390000 0001 2163 4895Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237 People’s Republic of China
| | - Chang-Yu Hsieh
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China
| | - Tingjun Hou
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058 Zhejiang People’s Republic of China
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37
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A deep learning method for predicting molecular properties and compound-protein interactions. J Mol Graph Model 2022; 117:108283. [PMID: 35994925 DOI: 10.1016/j.jmgm.2022.108283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/19/2022] [Accepted: 07/26/2022] [Indexed: 01/14/2023]
Abstract
Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep learning have demonstrated excellent performance in various challenges. It is imperative to develop efficient computational methods to predict accurately both molecular properties and CPIs in drug research using deep learning techniques. In this paper, we propose a deep learning method applicable to both molecular property prediction and CPI prediction based on the idea that both are generally influenced by chemical structure and sequence information of compounds and proteins. Molecular properties are inferred by integrating the molecular structure and sequence information of compounds, and CPIs are predicted by integrating protein sequence and compound structure. The method combines topological structure and sequence fingerprint information of molecules, extracts adequately raw data features, and generates highly representative features for prediction. Molecular property prediction experiments were conducted on BACE, P53 and hERG datasets, and CPI prediction experiments were conducted on Human, C. elegans and KIBA datasets. MG-S achieves outperformance in molecular property prediction on P53, the differences in AUC, Precision and MCC are 0.030, 0.050 and 0.100, respectively, over the suboptimal baseline model, and provides consistently good results on BACE and hERG.The model also achieves impressive performance in CPI prediction, the differences in AUC, Precision and MCC on KIBA are 0.141, 0.138, 0.090 and 0.082, respectively, compared with the state-of-the-art models. The comprehensive results show that the MG-S model has higher performance, better classification ability, and faster convergence. MG-S will serve as a useful method to predict compound properties and CPIs in the early stages of drug design and discovery.Our code and datasets are available at: https://github.com/happay-ending/cpi_cpp.
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38
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Tian Z, Peng X, Fang H, Zhang W, Dai Q, Ye Y. MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms. Brief Bioinform 2022; 23:6761042. [PMID: 36242566 DOI: 10.1093/bib/bbac434] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/19/2022] [Accepted: 09/08/2022] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Discovering the drug-target interactions (DTIs) is a crucial step in drug development such as the identification of drug side effects and drug repositioning. Since identifying DTIs by web-biological experiments is time-consuming and costly, many computational-based approaches have been proposed and have become an efficient manner to infer the potential interactions. Although extensive effort is invested to solve this task, the prediction accuracy still needs to be improved. More especially, heterogeneous network-based approaches do not fully consider the complex structure and rich semantic information in these heterogeneous networks. Therefore, it is still a challenge to predict DTIs efficiently. RESULTS In this study, we develop a novel method via Multiview heterogeneous information network embedding with Hierarchical Attention mechanisms to discover potential Drug-Target Interactions (MHADTI). Firstly, MHADTI constructs different similarity networks for drugs and targets by utilizing their multisource information. Combined with the known DTI network, three drug-target heterogeneous information networks (HINs) with different views are established. Secondly, MHADTI learns embeddings of drugs and targets from multiview HINs with hierarchical attention mechanisms, which include the node-level, semantic-level and graph-level attentions. Lastly, MHADTI employs the multilayer perceptron to predict DTIs with the learned deep feature representations. The hierarchical attention mechanisms could fully consider the importance of nodes, meta-paths and graphs in learning the feature representations of drugs and targets, which makes their embeddings more comprehensively. Extensive experimental results demonstrate that MHADTI performs better than other SOTA prediction models. Moreover, analysis of prediction results for some interested drugs and targets further indicates that MHADTI has advantages in discovering DTIs. AVAILABILITY AND IMPLEMENTATION https://github.com/pxystudy/MHADTI.
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Affiliation(s)
- Zhen Tian
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Xiangyu Peng
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Haichuan Fang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Wenjie Zhang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Qiguo Dai
- School of Computer Science and Engineering, Dalian Minzu University, Dalian,116600, China
| | - Yangdong Ye
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
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Askr H, Elgeldawi E, Aboul Ella H, Elshaier YAMM, Gomaa MM, Hassanien AE. Deep learning in drug discovery: an integrative review and future challenges. Artif Intell Rev 2022; 56:5975-6037. [PMID: 36415536 PMCID: PMC9669545 DOI: 10.1007/s10462-022-10306-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2022] [Indexed: 11/18/2022]
Abstract
Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.
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Affiliation(s)
- Heba Askr
- Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City, Egypt
| | - Enas Elgeldawi
- Computer Science Department, Faculty of Science, Minia University, Minia, Egypt
| | - Heba Aboul Ella
- Faculty of Pharmacy and Drug Technology, Chinese University in Egypt (CUE), Cairo, Egypt
| | | | - Mamdouh M. Gomaa
- Computer Science Department, Faculty of Science, Minia University, Minia, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt
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40
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Sales de Queiroz A, Sales Santa Cruz G, Jean-Marie A, Mazauric D, Roux J, Cazals F. Gene prioritization based on random walks with restarts and absorbing states, to define gene sets regulating drug pharmacodynamics from single-cell analyses. PLoS One 2022; 17:e0268956. [PMID: 36342924 PMCID: PMC9639845 DOI: 10.1371/journal.pone.0268956] [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: 08/27/2021] [Accepted: 05/12/2022] [Indexed: 11/09/2022] Open
Abstract
Prioritizing genes for their role in drug sensitivity, is an important step in understanding drugs mechanisms of action and discovering new molecular targets for co-treatment. To formalize this problem, we consider two sets of genes X and P respectively composing the gene signature of cell sensitivity at the drug IC50 and the genes involved in its mechanism of action, as well as a protein interaction network (PPIN) containing the products of X and P as nodes. We introduce Genetrank, a method to prioritize the genes in X for their likelihood to regulate the genes in P. Genetrank uses asymmetric random walks with restarts, absorbing states, and a suitable renormalization scheme. Using novel so-called saturation indices, we show that the conjunction of absorbing states and renormalization yields an exploration of the PPIN which is much more progressive than that afforded by random walks with restarts only. Using MINT as underlying network, we apply Genetrank to a predictive gene signature of cancer cells sensitivity to tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL), performed in single-cells. Our ranking provides biological insights on drug sensitivity and a gene set considerably enriched in genes regulating TRAIL pharmacodynamics when compared to the most significant differentially expressed genes obtained from a statistical analysis framework alone. We also introduce gene expression radars, a visualization tool embedded in MA plots to assess all pairwise interactions at a glance on graphical representations of transcriptomics data. Genetrank is made available in the Structural Bioinformatics Library (https://sbl.inria.fr/doc/Genetrank-user-manual.html). It should prove useful for mining gene sets in conjunction with a signaling pathway, whenever other approaches yield relatively large sets of genes.
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Affiliation(s)
| | | | | | | | - Jérémie Roux
- CNRS UMR 7284, Inserm U 1081, Institut de Recherche sur le Cancer et le Vieillissement de Nice, Centre Antoine Lacassagne, Universite Côte d’Azur, Nice, France
- * E-mail: (FC); (JR)
| | - Frédéric Cazals
- Inria, Université Côte d’Azur, Nice, France
- * E-mail: (FC); (JR)
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Aleb N. A Mutual Attention Model for Drug Target Binding Affinity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3224-3232. [PMID: 34665738 DOI: 10.1109/tcbb.2021.3121275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Vrious machine learning approaches have been developed for drug-target interaction (DTI) prediction. One class of these approaches, DTBA, is interested in Drug-Target Binding Affinity strength, rather than focusing merely on the presence or absence of interaction. Several machine learning methods have been developed for this purpose. However, almost all depend heavily on the use of increasingly sophisticated inputs to improve their performance. In addition, these methods do not allow any analysis or interpretation due to their black-box characteristic. This work is an attempt to overcome these limitations by taking advantage of the use of attention mechanisms with convolution models. In this paper, we define a new mutual attention based model for DTBA prediction. We represent both compounds and targets by sequences. Our model starts by aligning the drug-target pairs, then a learned masking is performed to retain the most promising regions, of both sequences, and amplify them with a learned factor in such a way to make the learning focus more on them. We evaluate the performance of our method on two benchmark datasets, KIBA and Davis. The results show that our mutual attention approach is very effective. Compared to other well-known approaches, it achieved excellent results regarding the considered performance metrics.
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42
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Hong Y, Chen D, Jin Y, Zu M, Zhang Y. PINet 1.0: A pathway network-based evaluation of drug combinations for the management of specific diseases. Front Mol Biosci 2022; 9:971768. [PMID: 36330216 PMCID: PMC9623281 DOI: 10.3389/fmolb.2022.971768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 10/03/2022] [Indexed: 12/03/2022] Open
Abstract
Drug combinations can increase the therapeutic effect by reducing the level of toxicity and the occurrence of drug resistance. Therefore, several drug combinations are often used in the management of complex diseases. However, due to the exponential growth in drug development, it would be impractical to evaluate all combinations through experiments. In view of this, we developed Pathway Interaction Network (PINet) biological model to estimate the optimal drug combinations for various diseases. The random walk with restart (RWR) algorithm was used to capture the "disease state" and "drug state," while PINet was used to evaluate the optimal drug combinations and the high-order drug combination. The model achieved a mean area under the curve of a receiver operating characteristic curve of 0.885. In addition, for some diseases, PINet predicted the optimal drug combination. For example, in the case of acute myeloid leukemia, PINet correctly predicted midostaurin and gemtuzumab as effective drug combinations, as demonstrated by the results of a Phase-I clinical trial. Moreover, PINet also correctly predicted the potential drug combinations for diseases that lacked a training dataset that could not be predicted using standard machine learning models.
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Affiliation(s)
| | | | | | - Mian Zu
- *Correspondence: Mian Zu, ; Yin Zhang,
| | - Yin Zhang
- *Correspondence: Mian Zu, ; Yin Zhang,
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Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning. Biomolecules 2022; 12:biom12101497. [PMID: 36291706 PMCID: PMC9599692 DOI: 10.3390/biom12101497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/10/2022] [Accepted: 10/13/2022] [Indexed: 11/18/2022] Open
Abstract
Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug–disease associations. This review reveals existing network-based approaches for predicting drug–disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug–drug similarities based on chemical structures and ATC codes, ontology-based disease–disease similarities, and drug–disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug–drug similarity measurement are crucial for improving disease-side prediction.
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44
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Yan X, Liu Y. Graph-sequence attention and transformer for predicting drug-target affinity. RSC Adv 2022; 12:29525-29534. [PMID: 36320763 PMCID: PMC9562047 DOI: 10.1039/d2ra05566j] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 10/04/2022] [Indexed: 11/30/2022] Open
Abstract
Drug-target binding affinity (DTA) prediction has drawn increasing interest due to its substantial position in the drug discovery process. The development of new drugs is costly, time-consuming, and often accompanied by safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. Therefore, it is of great significance to develop effective computational methods to predict DTAs. The attention mechanisms allow the computational method to focus on the most relevant parts of the input and have been proven to be useful for various tasks. In this study, we proposed a novel model based on self-attention, called GSATDTA, to predict the binding affinity between drugs and targets. For the representation of drugs, we use Bi-directional Gated Recurrent Units (BiGRU) to extract the SMILES representation from SMILES sequences, and graph neural networks to extract the graph representation of the molecular graphs. Then we utilize an attention mechanism to fuse the two representations of the drug. For the target/protein, we utilized an efficient transformer to learn the representation of the protein, which can capture the long-distance relationships in the sequence of amino acids. We conduct extensive experiments to compare our model with state-of-the-art models. Experimental results show that our model outperforms the current state-of-the-art methods on two independent datasets.
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Affiliation(s)
- Xiangfeng Yan
- School of Computer Science and Technology, Heilongjiang University Harbin China
| | - Yong Liu
- School of Computer Science and Technology, Heilongjiang University Harbin China
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45
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Orientation algorithm for PPI networks based on network propagation approach. J Biosci 2022. [DOI: 10.1007/s12038-022-00284-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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46
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Vigil-Vásquez C, Schüller A. De Novo Prediction of Drug Targets and Candidates by Chemical Similarity-Guided Network-Based Inference. Int J Mol Sci 2022; 23:ijms23179666. [PMID: 36077062 PMCID: PMC9455815 DOI: 10.3390/ijms23179666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/12/2022] [Accepted: 08/21/2022] [Indexed: 12/01/2022] Open
Abstract
Identifying drug–target interactions is a crucial step in discovering novel drugs and for drug repositioning. Network-based methods have shown great potential thanks to the straightforward integration of information from different sources and the possibility of extracting novel information from the graph topology. However, despite recent advances, there is still an urgent need for efficient and robust prediction methods. Here, we present SimSpread, a novel method that combines network-based inference with chemical similarity. This method employs a tripartite drug–drug–target network constructed from protein–ligand interaction annotations and drug–drug chemical similarity on which a resource-spreading algorithm predicts potential biological targets for both known or failed drugs and novel compounds. We describe small molecules as vectors of similarity indices to other compounds, thereby providing a flexible means to explore diverse molecular representations. We show that our proposed method achieves high prediction performance through multiple cross-validation and time-split validation procedures over a series of datasets. In addition, we demonstrate that our method performed a balanced exploration of both chemical ligand space (scaffold hopping) and biological target space (target hopping). Our results suggest robust and balanced performance, and our method may be useful for predicting drug targets, virtual screening, and drug repositioning.
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Affiliation(s)
- Carlos Vigil-Vásquez
- Department of Molecular Genetics and Microbiology, School of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | - Andreas Schüller
- Department of Molecular Genetics and Microbiology, School of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
- Correspondence:
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El-Behery H, Attia AF, El-Fishawy N, Torkey H. An ensemble-based drug-target interaction prediction approach using multiple feature information with data balancing. J Biol Eng 2022; 16:21. [PMID: 35941686 PMCID: PMC9361677 DOI: 10.1186/s13036-022-00296-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/02/2022] [Indexed: 11/16/2022] Open
Abstract
Background Recently, drug repositioning has received considerable attention for its advantage to pharmaceutical industries in drug development. Artificial intelligence techniques have greatly enhanced drug reproduction by discovering therapeutic drug profiles, side effects, and new target proteins. However, as the number of drugs increases, their targets and enormous interactions produce imbalanced data that might not be preferable as an input to a prediction model immediately. Methods This paper proposes a novel scheme for predicting drug–target interactions (DTIs) based on drug chemical structures and protein sequences. The drug Morgan fingerprint, drug constitutional descriptors, protein amino acid composition, and protein dipeptide composition were employed to extract the drugs and protein’s characteristics. Then, the proposed approach for extracting negative samples using a support vector machine one-class classifier was developed to tackle the imbalanced data problem feature sets from the drug–target dataset. Negative and positive samplings were constructed and fed into different prediction algorithms to identify DTIs. A 10-fold CV validation test procedure was applied to assess the predictability of the proposed method, in addition to the study of the effectiveness of the chemical and physical features in the evaluation and discovery of the drug–target interactions. Results Our experimental model outperformed existing techniques concerning the curve for receiver operating characteristic (AUC), accuracy, precision, recall F-score, mean square error, and MCC. The results obtained by the AdaBoost classifier enhanced prediction accuracy by 2.74%, precision by 1.98%, AUC by 1.14%, F-score by 3.53%, and MCC by 4.54% over existing methods.
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Affiliation(s)
- Heba El-Behery
- Department of Computer Science and Engineering, Faculty of Engineering, Kafrelsheikh University, Kafr_El_Sheikh, Egypt.
| | - Abdel-Fattah Attia
- Department of Computer Science and Engineering, Faculty of Engineering, Kafrelsheikh University, Kafr_El_Sheikh, Egypt
| | - Nawal El-Fishawy
- Computer Science & Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Hanaa Torkey
- Computer Science & Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
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Lian M, Wang X, Du W. Integrated multi-similarity fusion and heterogeneous graph inference for drug-target interaction prediction. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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49
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Rahman MM, Islam MR, Rahman F, Rahaman MS, Khan MS, Abrar S, Ray TK, Uddin MB, Kali MSK, Dua K, Kamal MA, Chellappan DK. Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance. Bioengineering (Basel) 2022; 9:bioengineering9080335. [PMID: 35892749 PMCID: PMC9332125 DOI: 10.3390/bioengineering9080335] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/09/2022] [Accepted: 07/18/2022] [Indexed: 01/07/2023] Open
Abstract
Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.
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Affiliation(s)
- Md. Mominur Rahman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Md. Rezaul Islam
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Firoza Rahman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Md. Saidur Rahaman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Md. Shajib Khan
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Sayedul Abrar
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Tanmay Kumar Ray
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Mohammad Borhan Uddin
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Most. Sumaiya Khatun Kali
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Kamal Dua
- Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia
- Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun 248007, India
| | - Mohammad Amjad Kamal
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Enzymoics, 7 Peterlee Place, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
| | - Dinesh Kumar Chellappan
- Department of Life Sciences, School of Pharmacy, International Medical University, Bukit Jalil, Kuala Lumpur 57000, Malaysia
- Correspondence:
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Zheng J, Xiao X, Qiu WR. DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method. Front Genet 2022; 13:859188. [PMID: 35754843 PMCID: PMC9213727 DOI: 10.3389/fgene.2022.859188] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/25/2022] [Indexed: 11/20/2022] Open
Abstract
Drug–target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.
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Affiliation(s)
- Jie Zheng
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Wang-Ren Qiu
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
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