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Ghorbanalipoor S, Matsumoto K, Gross N, Heimberg L, Krause M, Veldkamp W, Magens M, Zanken J, Neuschutz KJ, De Luca DA, Kridin K, Vidarsson G, Chakievska L, Visser R, Kunzel S, Recke A, Gupta Y, Boch K, Vorobyev A, Kalies K, Manz RA, Bieber K, Ludwig RJ. High throughput screening identifies repurposable drugs for modulation of innate and acquired immune responses. J Autoimmun 2024; 148:103302. [PMID: 39163739 DOI: 10.1016/j.jaut.2024.103302] [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: 12/06/2023] [Revised: 07/25/2024] [Accepted: 07/30/2024] [Indexed: 08/22/2024]
Abstract
A balanced immune system is essential to maintain adequate host defense and effective self-tolerance. While an immune system that fails to generate appropriate response will permit infections to develop, uncontrolled activation may lead to autoinflammatory or autoimmune diseases. To identify drug candidates capable of modulating immune cell functions, we screened 1200 small molecules from the Prestwick Chemical Library for their property to inhibit innate or adaptive immune responses. Our studies focused specifically on drug interactions with T cells, B cells, and polymorphonuclear leukocytes (PMNs). Candidate drugs that were validated in vitro were examined in preclinical models to determine their immunomodulatory impact in chronic inflammatory diseases, here investigated in chronic inflammatory skin diseases. Using this approach, we identified several candidate drugs that were highly effective in preclinical models of chronic inflammatory disease. For example, we found that administration of pyrvinium pamoate, an FDA-approved over-the-counter anthelmintic drug, suppressed B cell activation in vitro and halted the progression of B cell-dependent experimental pemphigoid by reducing numbers of autoantigen-specific B cell responses. In addition, in studies performed in gene-deleted mouse strains provided additional insight into the mechanisms underlying these effects, for example, the receptor-dependent actions of tamoxifen that inhibit immune-complex-mediated activation of PMNs. Collectively, our methods and findings provide a vast resource that can be used to identify drugs that may be repurposed and used to promote or inhibit cellular immune responses.
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Affiliation(s)
| | - Kazuko Matsumoto
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Natalie Gross
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Linda Heimberg
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Malin Krause
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Wendelien Veldkamp
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Moritz Magens
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Johannes Zanken
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Kerstin J Neuschutz
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - David A De Luca
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany; Department of Dermatology, University of Lübeck, Lübeck, Germany
| | - Khalaf Kridin
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Gestur Vidarsson
- Sanquin Research and Landsteiner Laboratory, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands; Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands
| | - Lenche Chakievska
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Remco Visser
- Sanquin Research and Landsteiner Laboratory, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands; Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands
| | - Sven Kunzel
- Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Andreas Recke
- Department of Dermatology, University of Lübeck, Lübeck, Germany
| | - Yask Gupta
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Katharina Boch
- Department of Dermatology, University of Lübeck, Lübeck, Germany
| | - Artem Vorobyev
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany; Department of Dermatology, University of Lübeck, Lübeck, Germany
| | - Kathrin Kalies
- Institute for Anatomy, University of Lübeck, Lübeck, Germany
| | - Rudolf A Manz
- Institute for Systemic Inflammation Research, University of Lübeck, Lübeck, Germany
| | - Katja Bieber
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany.
| | - Ralf J Ludwig
- Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany; Department of Dermatology, University of Lübeck, Lübeck, Germany
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Gao Z, Li L, Ma S, Wang Q, Hemphill L, Xu R. Examining the Potential of ChatGPT on Biomedical Information Retrieval: Fact-Checking Drug-Disease Associations. Ann Biomed Eng 2024; 52:1919-1927. [PMID: 37855948 DOI: 10.1007/s10439-023-03385-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/09/2023] [Indexed: 10/20/2023]
Abstract
Large language models (LLMs) such as ChatGPT have recently attracted significant attention due to their impressive performance on many real-world tasks. These models have also demonstrated the potential in facilitating various biomedical tasks. However, little is known of their potential in biomedical information retrieval, especially identifying drug-disease associations. This study aims to explore the potential of ChatGPT, a popular LLM, in discerning drug-disease associations. We collected 2694 true drug-disease associations and 5662 false drug-disease pairs. Our approach involved creating various prompts to instruct ChatGPT in identifying these associations. Under varying prompt designs, ChatGPT's capability to identify drug-disease associations with an accuracy of 74.6-83.5% and 96.2-97.6% for the true and false pairs, respectively. This study shows that ChatGPT has the potential in identifying drug-disease associations and may serve as a helpful tool in searching pharmacy-related information. However, the accuracy of its insights warrants comprehensive examination before its implementation in medical practice.
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Affiliation(s)
- Zhenxiang Gao
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Lingyao Li
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Siyuan Ma
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qinyong Wang
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Libby Hemphill
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
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3
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Liu J, Gui Y, Rao J, Sun J, Wang G, Ren Q, Qu N, Niu B, Chen Z, Sheng X, Wang Y, Zheng M, Li X. In silico off-target profiling for enhanced drug safety assessment. Acta Pharm Sin B 2024; 14:2927-2941. [PMID: 39027254 PMCID: PMC11252485 DOI: 10.1016/j.apsb.2024.03.002] [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: 11/28/2023] [Revised: 02/21/2024] [Accepted: 02/29/2024] [Indexed: 07/20/2024] Open
Abstract
Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in adverse drug reaction (ADR) enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.
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Affiliation(s)
- Jin Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
| | - Yike Gui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Jingxin Rao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingjing Sun
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Gang Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qun Ren
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Ning Qu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Buying Niu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhiyi Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, Hangzhou 330106, China
| | - Xia Sheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yitian Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingyue Zheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Nanjing University of Chinese Medicine, Nanjing 210023, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, Hangzhou 330106, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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4
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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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5
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Xie W, Yu J, Huang L, For LS, Zheng Z, Chen X, Wang Y, Liu Z, Peng C, Wong KC. DeepSeq2Drug: An expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning. Comput Biol Med 2024; 175:108487. [PMID: 38653064 DOI: 10.1016/j.compbiomed.2024.108487] [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: 03/26/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and cost reductions of drug discovery. Traditional graph-based drug repurposing methods are limited to fast, large-scale virtual screens, as they constrain the counts for drugs and targets and fail to predict novel viruses or drugs. Moreover, though deep learning has been proposed for drug repurposing, only a few methods have been used, including a group of pre-trained deep learning models for embedding generation and transfer learning. Hence, we propose DeepSeq2Drug to tackle the shortcomings of previous methods. We leverage multi-modal embeddings and an ensemble strategy to complement the numbers of drugs and viruses and to guarantee the novel prediction. This framework (including the expanded version) involves four modal types: six NLP models, four CV models, four graph models, and two sequence models. In detail, we first make a pipeline and calculate the predictive performance of each pair of viral and drug embeddings. Then, we select the best embedding pairs and apply an ensemble strategy to conduct anti-viral drug repurposing. To validate the effect of the proposed ensemble model, a monkeypox virus (MPV) case study is conducted to reflect the potential predictive capability. This framework could be a benchmark method for further pre-trained deep learning optimization and anti-viral drug repurposing tasks. We also build software further to make the proposed model easier to reuse. The code and software are freely available at http://deepseq2drug.cs.cityu.edu.hk.
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Affiliation(s)
- Weidun Xie
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Jixiang Yu
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Lei Huang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Lek Shyuen For
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Zetian Zheng
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Xingjian Chen
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuchen Wang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Zhichao Liu
- Sir William Dunn School of Pathology, University of Oxford, UK
| | - Chengbin Peng
- College of Information Science and Engineering, Ningbo University, Ningbo, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China; Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China; Hong Kong Institute for Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China.
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6
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Savva K, Zachariou M, Bourdakou MM, Dietis N, Spyrou GM. D Re Amocracy: A Method to Capitalise on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing. Int J Mol Sci 2024; 25:5319. [PMID: 38791356 PMCID: PMC11121186 DOI: 10.3390/ijms25105319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/26/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024] Open
Abstract
In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as clinical trial studies that have tested/are testing drugs in different phases. To the best of our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards generating a dynamic reference matrix with enhanced resolution. To fill this knowledge gap, we performed weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, namely the Drug Repurposing Hub. Our method, DReAmocracy, exploits this pile of information and creates frequency tables and, finally, a disease suitability score for each drug from the selected library. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimer's, Parkinson's, Huntington's disease and Multiple Sclerosis). A super-reference table with drug suitability scores has been created for all four neurodegenerative diseases and can be queried for any drug candidate against them. Top-scored drugs for Alzheimer's Disease include agomelatine, mirtazapine and vortioxetine; for Parkinson's Disease, they include apomorphine, pramipexole and lisuride; for Huntington's, they include chlorpromazine, fluphenazine and perphenazine; and for Multiple Sclerosis, they include zonisamide, disopyramide and priralfimide. Overall, DReAmocracy is a methodology that focuses on leveraging the existing drug-related experimental and/or computational knowledge rather than a predictive model for drug repurposing, offering a quantified aggregation of existing drug discovery results to (1) reveal trends in selected tracks of drug discovery research with increased resolution that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) score new candidate drugs for repurposing against a selected disease.
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Affiliation(s)
- Kyriaki Savva
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
| | - Margarita Zachariou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
| | - Marilena M. Bourdakou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
| | - Nikolas Dietis
- Experimental Pharmacology Laboratory, Medical School, University of Cyprus, Nicosia 2115, Cyprus;
| | - George M. Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus; (K.S.); (M.Z.); (M.M.B.)
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7
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Vashishat A, Patel P, Das Gupta G, Das Kurmi B. Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches. Stem Cell Rev Rep 2024; 20:881-899. [PMID: 38429620 DOI: 10.1007/s12015-024-10701-x] [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] [Accepted: 02/19/2024] [Indexed: 03/03/2024]
Abstract
Biomedical research has long relied on animal models to unravel the intricacies of human physiology and pathology. However, concerns surrounding ethics, expenses, and inherent species differences have catalyzed the exploration of alternative avenues. The contemporary alternatives to traditional animal models in biomedical research delve into three main categories of alternative approaches: in vitro models, in vertebrate models, and in silico models. This unique approach to artificial intelligence and machine learning has been a keen interest to be used in different biomedical research. The main goal of this review is to serve as a guide to researchers seeking novel avenues for their investigations and underscores the importance of considering alternative models in the pursuit of scientific knowledge and medical breakthroughs, including showcasing the broad spectrum of modern approaches that are revolutionizing biomedical research and leading the way toward a more ethical, efficient, and innovative future. Models can insight into cellular processes, developmental biology, drug interaction, assessing toxicology, and understanding molecular mechanisms.
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Affiliation(s)
- Abhinav Vashishat
- Department of Pharmaceutics, ISF College of Pharmacy, GT Road, Moga, 142001, Punjab, India
| | - Preeti Patel
- Department of Pharmaceutical Chemistry, ISF College Pharmacy, GT Road, Moga, 142001, Punjab, India.
| | - Ghanshyam Das Gupta
- Department of Pharmaceutics, ISF College of Pharmacy, GT Road, Moga, 142001, Punjab, India
| | - Balak Das Kurmi
- Department of Pharmaceutics, ISF College of Pharmacy, GT Road, Moga, 142001, Punjab, India.
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Rani N, Kaushik A, Kardam S, Kag S, Raj VS, Ambasta RK, Kumar P. Reimagining old drugs with new tricks: Mechanisms, strategies and notable success stories in drug repurposing for neurological diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:23-70. [PMID: 38789181 DOI: 10.1016/bs.pmbts.2024.03.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Recent evolution in drug repurposing has brought new anticipation, especially in the conflict against neurodegenerative diseases (NDDs). The traditional approach to developing novel drugs for these complex disorders is laborious, time-consuming, and often abortive. However, drug reprofiling which is the implementation of illuminating novel therapeutic applications of existing approved drugs, has shown potential as a promising strategy to accelerate the hunt for therapeutics. The advancement of computational approaches and artificial intelligence has expedited drug repurposing. These progressive technologies have enabled scientists to analyse extensive datasets and predict potential drug-disease interactions. By prospecting into the existing pharmacological knowledge, scientists can recognise potential therapeutic candidates for reprofiling, saving precious time and resources. Preclinical models have also played a pivotal role in this field, confirming the effectiveness and mechanisms of action of repurposed drugs. Several studies have occurred in recent years, including the discovery of available drugs that demonstrate significant protective effects in NDDs, relieve debilitating symptoms, or slow down the progression of the disease. These findings highlight the potential of repurposed drugs to change the landscape of NDD treatment. Here, we present an overview of recent developments and major advances in drug repurposing intending to provide an in-depth analysis of traditional drug discovery and the strategies, approaches and technologies that have contributed to drug repositioning. In addition, this chapter attempts to highlight successful case studies of drug repositioning in various therapeutic areas related to NDDs and explore the clinical trials, challenges and limitations faced by researchers in the field. Finally, the importance of drug repositioning in drug discovery and development and its potential to address discontented medical needs is also highlighted.
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Affiliation(s)
- Neetu Rani
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Aastha Kaushik
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Shefali Kardam
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Sonika Kag
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - V Samuel Raj
- Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India
| | - Rashmi K Ambasta
- Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India.
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Ghandikota SK, Jegga AG. Application of artificial intelligence and machine learning in drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:171-211. [PMID: 38789178 DOI: 10.1016/bs.pmbts.2024.03.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.
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Affiliation(s)
- Sudhir K Ghandikota
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
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Lei S, Lei X, Chen M, Pan Y. Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity. Interdiscip Sci 2024; 16:160-175. [PMID: 38103130 DOI: 10.1007/s12539-023-00593-9] [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: 07/19/2023] [Revised: 11/03/2023] [Accepted: 11/06/2023] [Indexed: 12/17/2023]
Abstract
Drug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug-disease heterogeneous networks to extract drug-disease features. However, these methods faced difficulty when we are using structurally simple models to deal with complex heterogeneous networks. Therefore, in this study, the researchers introduced a drug repositioning method named DRDSA. The method utilizes a deep sparse autoencoder and integrates drug-disease similarities. First, the researchers constructed a drug-disease feature network by incorporating information from drug chemical structure, disease semantic data, and existing known drug-disease associations. Then, we learned the low-dimensional representation of the feature network using a deep sparse autoencoder. Finally, we utilized a deep neural network to make predictions on new drug-disease associations based on the feature representation. The experimental results show that our proposed method has achieved optimal results on all four benchmark datasets, especially on the CTD dataset where AUC and AUPR reached 0.9619 and 0.9676, respectively, outperforming other baseline methods. In the case study, the researchers predicted the top ten antiviral drugs for COVID-19. Remarkably, six out of these predictions were subsequently validated by other literature sources.
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Affiliation(s)
- Song Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
| | - Ming Chen
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
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11
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He S, Yun L, Yi H. Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction. BMC Bioinformatics 2024; 25:79. [PMID: 38378479 PMCID: PMC10877759 DOI: 10.1186/s12859-024-05705-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 02/14/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Identification of potential drug-disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data. RESULTS In this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug-disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug-drug, drug-disease, and disease-disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation. CONCLUSIONS Rigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug-disease association prediction, which is beneficial for drug repositioning and drug safety research.
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Affiliation(s)
- Shihui He
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education, Kunming, 650500, China
| | - Lijun Yun
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China.
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education, Kunming, 650500, China.
| | - Haicheng Yi
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
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12
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Manoj M, Sowmyanarayan S, Kowshik AV, Chatterjee J. Identification of Potentially Repurposable Drugs for Lewy Body Dementia Using a Network-Based Approach. J Mol Neurosci 2024; 74:21. [PMID: 38363395 DOI: 10.1007/s12031-024-02199-2] [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: 01/01/2024] [Accepted: 02/06/2024] [Indexed: 02/17/2024]
Abstract
The conventional method of one drug being used for one target has not yielded therapeutic solutions for Lewy body dementia (LBD), which is a leading progressive neurological disorder characterized by significant loss of neurons. The age-related disease is marked by memory loss, hallucinations, sleep disorder, mental health deterioration, palsy, and cognitive impairment, all of which have no known effective cure. The present study deploys a network medicine pipeline to repurpose drugs having considerable effect on the genes and proteins related to the diseases of interest. We utilized the novel SAveRUNNER algorithm to quantify the proximity of all drugs obtained from DrugBank with the disease associated gene dataset obtained from Phenopedia and targets in the human interactome. We found that most of the 154 FDA-approved drugs predicted by SAveRUNNER were used to treat nervous system disorders, but some off-label drugs like quinapril and selegiline were interestingly used to treat hypertension and Parkinson's disease (PD), respectively. Additionally, we performed gene set enrichment analysis using Connectivity Map (CMap) and pathway enrichment analysis using EnrichR to validate the efficacy of the drug candidates obtained from the pipeline approach. The investigation enabled us to identify the significant role of the synaptic vesicle pathway in our disease and accordingly finalize 8 suitable antidepressant drugs from the 154 drugs initially predicted by SAveRUNNER. These potential anti-LBD drugs are either selective or non-selective inhibitors of serotonin, dopamine, and norepinephrine transporters. The validated selective serotonin and norepinephrine inhibitors like milnacipran, protriptyline, and venlafaxine are predicted to manage LBD along with the affecting symptomatic issues.
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Affiliation(s)
- Megha Manoj
- Department of Biotechnology, PES University, Bangalore, 560085, India
| | | | - Arjun V Kowshik
- Department of Biotechnology, PES University, Bangalore, 560085, India
| | - Jhinuk Chatterjee
- Department of Biotechnology, PES University, Bangalore, 560085, India.
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13
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Fatemi N, Karimpour M, Bahrami H, Zali MR, Chaleshi V, Riccio A, Nazemalhosseini-Mojarad E, Totonchi M. Current trends and future prospects of drug repositioning in gastrointestinal oncology. Front Pharmacol 2024; 14:1329244. [PMID: 38239190 PMCID: PMC10794567 DOI: 10.3389/fphar.2023.1329244] [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: 10/28/2023] [Accepted: 12/11/2023] [Indexed: 01/22/2024] Open
Abstract
Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.
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Affiliation(s)
- Nayeralsadat Fatemi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Karimpour
- Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Hoda Bahrami
- Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Reza Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Vahid Chaleshi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Andrea Riccio
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies (DiSTABiF), Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy
- Institute of Genetics and Biophysics (IGB) “Adriano Buzzati-Traverso”, Consiglio Nazionale delle Ricerche (CNR), Naples, Italy
| | - Ehsan Nazemalhosseini-Mojarad
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehdi Totonchi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies (DiSTABiF), Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy
- Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
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14
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Rawat S, Subramaniam K, Subramanian SK, Subbarayan S, Dhanabalan S, Chidambaram SKM, Stalin B, Roy A, Nagaprasad N, Aruna M, Tesfaye JL, Badassa B, Krishnaraj R. Drug Repositioning Using Computer-aided Drug Design (CADD). Curr Pharm Biotechnol 2024; 25:301-312. [PMID: 37605405 DOI: 10.2174/1389201024666230821103601] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 03/03/2023] [Accepted: 03/20/2023] [Indexed: 08/23/2023]
Abstract
Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.
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Affiliation(s)
- Sona Rawat
- School of Life Sciences, Jaipur National University, Jaipur-302017, India
| | - Kanmani Subramaniam
- Department of Civil Engineering, KPR Institute of Engineering and Technology, Coimbatore-641407, Tamil Nadu, India
| | - Selva Kumar Subramanian
- Department of Sciences, Amrita School of Engineering, Coimbatore - 641112, Tamil Nadu, India
| | - Saravanan Subbarayan
- Department of Civil Engineering, National Institute of Technology, Trichy-620015, Tamil Nadu, India
| | - Subramanian Dhanabalan
- Department of Mechanical Engineering, M. Kumarasamy College of Engineering, Karur - 639113, Tamil Nadu, India
| | | | - Balasubramaniam Stalin
- Department of Mechanical Engineering, Anna University, Regional Campus Madurai, Madurai - 625 019, Tamil Nadu, India
| | - Arpita Roy
- Department of Biotechnology, School of Engineering & Technology, Sharda University, Greater Noida 201310, India
| | - Nagaraj Nagaprasad
- Department of Mechanical Engineering, ULTRA College of Engineering and Technology, Madurai - 625104, Tamilnadu, India
| | - Mahalingam Aruna
- College of Engineering and Computing, Al Ghurair University, Academic City, Dubai, UAE
| | - Jule Leta Tesfaye
- Dambi Dollo University, College of Natural and Computational Science, Department of Physics, Ethiopia
- Centre for Excellence-Indigenous Knowledge, Innovative Technology Transfer and Entrepreneurship, Dambi Dollo University, Dambi Dollo, Ethiopia
- Ministry of innovation and technology, Ethiopia
| | - Bayissa Badassa
- Department of Mechanical Engineering, Dambi Dollo University, Dambi Dollo, Ethiopia
| | - Ramaswamy Krishnaraj
- Centre for Excellence-Indigenous Knowledge, Innovative Technology Transfer and Entrepreneurship, Dambi Dollo University, Dambi Dollo, Ethiopia
- Ministry of innovation and technology, Ethiopia
- Department of Mechanical Engineering, Dambi Dollo University, Dambi Dollo, Ethiopia
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15
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Gao Z, Ding P, Xu R. IUPHAR review - Data-driven computational drug repurposing approaches for opioid use disorder. Pharmacol Res 2024; 199:106960. [PMID: 37832859 DOI: 10.1016/j.phrs.2023.106960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 10/15/2023]
Abstract
Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.
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Affiliation(s)
- Zhenxiang Gao
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
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16
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Abdul Raheem AK, Dhannoon BN. Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview. Curr Drug Discov Technol 2024; 21:e010923220652. [PMID: 37680152 DOI: 10.2174/1570163820666230901160043] [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: 01/23/2023] [Revised: 05/29/2023] [Accepted: 07/18/2023] [Indexed: 09/09/2023]
Abstract
Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its normal state. Predicting DTIs plays a vital role in the drug discovery (DD) process as it has the potential to enhance efficiency and reduce costs. However, DTI prediction poses significant challenges and expenses due to the time-consuming and costly nature of experimental assays. As a result, researchers have increased their efforts to identify the association between medications and targets in the hopes of speeding up drug development and shortening the time to market. This paper provides a detailed discussion of the initial stage in drug discovery, namely drug-target interactions. It focuses on exploring the application of machine learning methods within this step. Additionally, we aim to conduct a comprehensive review of relevant papers and databases utilized in this field. Drug target interaction prediction covers a wide range of applications: drug discovery, prediction of adverse effects and drug repositioning. The prediction of drugtarget interactions can be categorized into three main computational methods: docking simulation approaches, ligand-based methods, and machine-learning techniques.
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Affiliation(s)
- Ali K Abdul Raheem
- Software Department, College of Information Technology, University of Babylon, Hillah, Babil, Iraq
- University of Warith Al-Anbiyaa, Kerbala, Iraq
| | - Ban N Dhannoon
- Department of Computer Science, College of Science, Al-Nahrain University, Baghdad, Iraq
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17
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Anderson C, Bucholc M, McClean PL, Zhang SD. The Potential of a Stratified Approach to Drug Repurposing in Alzheimer's Disease. Biomolecules 2023; 14:11. [PMID: 38275752 PMCID: PMC10813465 DOI: 10.3390/biom14010011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/13/2023] [Accepted: 12/15/2023] [Indexed: 01/27/2024] Open
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative condition that is characterized by the build-up of amyloid-beta plaques and neurofibrillary tangles. While multiple theories explaining the aetiology of the disease have been suggested, the underlying cause of the disease is still unknown. Despite this, several modifiable and non-modifiable factors that increase the risk of developing AD have been identified. To date, only eight AD drugs have ever gained regulatory approval, including six symptomatic and two disease-modifying drugs. However, not all are available in all countries and high costs associated with new disease-modifying biologics prevent large proportions of the patient population from accessing them. With the current patient population expected to triple by 2050, it is imperative that new, effective, and affordable drugs become available to patients. Traditional drug development strategies have a 99% failure rate in AD, which is far higher than in other disease areas. Even when a drug does reach the market, additional barriers such as high cost and lack of accessibility prevent patients from benefiting from them. In this review, we discuss how a stratified medicine drug repurposing approach may address some of the limitations and barriers that traditional strategies face in relation to drug development in AD. We believe that novel, stratified drug repurposing studies may expedite the discovery of alternative, effective, and more affordable treatment options for a rapidly expanding patient population in comparison with traditional drug development methods.
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Affiliation(s)
- Chloe Anderson
- Personalised Medicine Centre, School of Medicine, Altnagelvin Hospital Campus, Ulster University, Glenshane Road, Derry/Londonderry BT47 6SB, UK;
| | - Magda Bucholc
- School of Computing, Engineering and Intelligent Systems, Magee Campus, Ulster University, Northland Road, Derry/Londonderry BT48 7JL, UK
| | - Paula L. McClean
- Personalised Medicine Centre, School of Medicine, Altnagelvin Hospital Campus, Ulster University, Glenshane Road, Derry/Londonderry BT47 6SB, UK;
| | - Shu-Dong Zhang
- Personalised Medicine Centre, School of Medicine, Altnagelvin Hospital Campus, Ulster University, Glenshane Road, Derry/Londonderry BT47 6SB, UK;
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18
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Amiri R, Razmara J, Parvizpour S, Izadkhah H. A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks. BMC Bioinformatics 2023; 24:442. [PMID: 37993777 PMCID: PMC10664633 DOI: 10.1186/s12859-023-05572-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023] Open
Abstract
Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.
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Affiliation(s)
- Ramin Amiri
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran
| | - Jafar Razmara
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran.
| | - Sepideh Parvizpour
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Habib Izadkhah
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran
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19
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Kumar S, Roy V. Repurposing Drugs: An Empowering Approach to Drug Discovery and Development. Drug Res (Stuttg) 2023; 73:481-490. [PMID: 37478892 DOI: 10.1055/a-2095-0826] [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: 07/23/2023]
Abstract
Drug discovery and development is a time-consuming and costly procedure that necessitates a substantial effort. Drug repurposing has been suggested as a method for developing medicines that takes less time than developing brand new medications and will be less expensive. Also known as drug repositioning or re-profiling, this strategy has been in use from the time of serendipitous drug discoveries to the modern computer aided drug designing and use of computational chemistry. In the light of the COVID-19 pandemic too, drug repurposing emerged as a ray of hope in the dearth of available medicines. Data availability by electronic recording, libraries, and improvements in computational techniques offer a vital substrate for systemic evaluation of repurposing candidates. In the not-too-distant future, it could be possible to create a global research archive for us to access, thus accelerating the process of drug development and repurposing. This review aims to present the evolution, benefits and drawbacks including current approaches, key players and the legal and regulatory hurdles in the field of drug repurposing. The vast quantities of available data secured in multiple drug databases, assisting in drug repurposing is also discussed.
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Affiliation(s)
- Sahil Kumar
- Pharmacology, ESIC Dental College and Hospital, New Delhi, India
| | - Vandana Roy
- Pharmacology, Maulana Azad Medical College, New Delhi, India
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20
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Niazi SK. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Des Devel Ther 2023; 17:2691-2725. [PMID: 37701048 PMCID: PMC10493153 DOI: 10.2147/dddt.s424991] [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: 06/28/2023] [Accepted: 08/24/2023] [Indexed: 09/14/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.
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21
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Grotzinger AD, Singh K, Miller-Fleming TW, Lam M, Mallard TT, Chen Y, Liu Z, Ge T, Smoller JW. Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing. JAMA Psychiatry 2023; 80:811-821. [PMID: 37314780 PMCID: PMC10267850 DOI: 10.1001/jamapsychiatry.2023.1808] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 04/05/2023] [Indexed: 06/15/2023]
Abstract
Importance Psychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy. Objective To identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes. Design, Setting, and Participants This genomic study applied a multivariate transcriptomic method, transcriptome-wide structural equation modeling (T-SEM), to investigate gene expression patterns associated with 5 genomic factors indexing shared risk across 13 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. The Broad Institute Connectivity Map Drug Repurposing Database and Drug-Gene Interaction Database public databases of drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk. Data were collected from database inception up to February 20, 2023. Main Outcomes and Measures Gene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes. Results In total, T-SEM identified 466 genes whose expression was significantly associated (z ≥ 5.02) with genomic factors and 36 genes with disorder-specific effects. Most associated genes were found for a thought disorders factor, defined by bipolar disorder and schizophrenia. Several existing pharmacological interventions were identified that could be repurposed to target genes whose expression was associated with the thought disorders factor or a transdiagnostic p factor defined by all 13 disorders. Conclusions and Relevance The findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.
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Affiliation(s)
- Andrew D. Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder
| | - Kritika Singh
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Tyne W. Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Max Lam
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, New York
- Research Division, Institute of Mental Health Singapore, Singapore
- Human Genetics, Genome Institute of Singapore, Singapore
| | - Travis T. Mallard
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Yu Chen
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Zhaowen Liu
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Tian Ge
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Jordan W. Smoller
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
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22
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Alves V, Martins PH, Miranda B, de Andrade IB, Pereira L, Maeda CT, de Sousa Araújo GR, Frases S. Assessing the In Vitro Potential of Glatiramer Acetate (Copaxone ®) as a Chemotherapeutic Candidate for the Treatment of Cryptococcus neoformans Infection. J Fungi (Basel) 2023; 9:783. [PMID: 37623554 PMCID: PMC10455304 DOI: 10.3390/jof9080783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 07/14/2023] [Accepted: 07/19/2023] [Indexed: 08/26/2023] Open
Abstract
Cryptococcosis is a systemic mycosis affecting immunosuppressed individuals, caused by various Cryptococcus species. The current treatment utilizes a combination of antifungal drugs, but issues such as nephrotoxicity, restricted or limited availability in certain countries, and resistance limit their effectiveness. Repurposing approved drugs presents a viable strategy for developing new antifungal options. This study investigates the potential of glatiramer acetate (Copaxone®) as a chemotherapy candidate for Cryptococcus neoformans infection. Various techniques are employed to evaluate the effects of glatiramer acetate on the fungus, including microdilution, XTT analysis, electron and light microscopy, and physicochemical measurements. The results demonstrate that glatiramer acetate exhibits antifungal properties, with an IC50 of 0.470 mg/mL and a minimum inhibitory concentration (MIC) of 2.5 mg/mL. Furthermore, it promotes enhanced cell aggregation, facilitates biofilm formation, and increases the secretion of fungal polysaccharides. These findings indicate that glatiramer acetate not only shows an antifungal effect but also modulates the key virulence factor-the polysaccharide capsule. In summary, repurposing glatiramer acetate as a potential chemotherapy option offers new prospects for combating C. neoformans infection. It addresses the limitations associated with current antifungal therapies by providing an alternative treatment approach.
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Affiliation(s)
- Vinicius Alves
- Laboratório de Biofísica de Fungos, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil; (V.A.); (P.H.M.); (B.M.); (I.B.d.A.); (L.P.); (G.R.d.S.A.)
| | - Pedro Henrique Martins
- Laboratório de Biofísica de Fungos, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil; (V.A.); (P.H.M.); (B.M.); (I.B.d.A.); (L.P.); (G.R.d.S.A.)
| | - Bruna Miranda
- Laboratório de Biofísica de Fungos, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil; (V.A.); (P.H.M.); (B.M.); (I.B.d.A.); (L.P.); (G.R.d.S.A.)
| | - Iara Bastos de Andrade
- Laboratório de Biofísica de Fungos, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil; (V.A.); (P.H.M.); (B.M.); (I.B.d.A.); (L.P.); (G.R.d.S.A.)
| | - Luiza Pereira
- Laboratório de Biofísica de Fungos, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil; (V.A.); (P.H.M.); (B.M.); (I.B.d.A.); (L.P.); (G.R.d.S.A.)
| | - Christina Takiya Maeda
- Laboratório de Fisiopatologia, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil;
| | - Glauber Ribeiro de Sousa Araújo
- Laboratório de Biofísica de Fungos, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil; (V.A.); (P.H.M.); (B.M.); (I.B.d.A.); (L.P.); (G.R.d.S.A.)
| | - Susana Frases
- Laboratório de Biofísica de Fungos, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil; (V.A.); (P.H.M.); (B.M.); (I.B.d.A.); (L.P.); (G.R.d.S.A.)
- Rede Micologia RJ, FAPERJ, Rio de Janeiro 21941-902, Brazil
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23
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Viesi E, Sardina DS, Perricone U, Giugno R. APDB: a database on air pollutant characterization and similarity prediction. Database (Oxford) 2023; 2023:baad046. [PMID: 37450416 PMCID: PMC10348400 DOI: 10.1093/database/baad046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/12/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023]
Abstract
The World Health Organization estimates that 9 out of 10 people worldwide breathe air containing high levels of pollutants. Long-term and chronic exposure to high concentrations of air pollutants is associated with deleterious effects on vital organs, including increased inflammation in the lungs, oxidative stress in the heart and disruption of the blood-brain barrier. For this reason, in an effort to find an association between exposure to pollutants and the toxicological effects observable on human health, an online resource collecting and characterizing in detail pollutant molecules could be helpful to investigate their properties and mechanisms of action. We developed a database, APDB, collecting air-pollutant-related data from different online resources, in particular, molecules from the US Environmental Protection Agency, their associated targets and bioassays found in the PubChem chemical repository and their computed molecular descriptors and quantum mechanics properties. A web interface allows (i) to browse data by category, (ii) to navigate the database by querying molecules and targets and (iii) to visualize and download molecule and target structures as well as computed descriptors and similarities. The desired data can be freely exported in textual/tabular format and the whole database in SQL format. Database URL http://apdb.di.univr.it.
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Affiliation(s)
- Eva Viesi
- Department of Computer Science, University of Verona, Strada le Grazie 15, Verona 37134, Italy
| | - Davide Stefano Sardina
- Molecular Informatics Unit, Ri.MED Foundation, Via Filippo Marini 14, Palermo 90128, Italy
| | - Ugo Perricone
- Molecular Informatics Unit, Ri.MED Foundation, Via Filippo Marini 14, Palermo 90128, Italy
- National Biodiversity Future Center (NBFC), Piazza Marina 61, Palermo 90133, Italy
| | - Rosalba Giugno
- Department of Computer Science, University of Verona, Strada le Grazie 15, Verona 37134, Italy
- National Biodiversity Future Center (NBFC), Piazza Marina 61, Palermo 90133, Italy
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24
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Dong W, Yang Q, Wang J, Xu L, Li X, Luo G, Gao X. Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network. Brief Bioinform 2023; 24:7145903. [PMID: 37114624 DOI: 10.1093/bib/bbad161] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/19/2023] [Accepted: 04/02/2023] [Indexed: 04/29/2023] Open
Abstract
Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.
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Affiliation(s)
- Weihe Dong
- College of information and Computer Engineering, Northeast Forestry University, Hexing Road, 150040, Harbin, China
| | - Qiang Yang
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, 150080, Harbin, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, 150080, Harbin, China
| | - Jian Wang
- College of information and Computer Engineering, Northeast Forestry University, Hexing Road, 150040, Harbin, China
| | - Long Xu
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, 150080, Harbin, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, 150080, Harbin, China
| | - Xiaokun Li
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, 150080, Harbin, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, 150080, Harbin, China
| | - Gongning Luo
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, 150001, Harbin, China
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
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25
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Ghorbanali Z, Zare-Mirakabad F, Akbari M, Salehi N, Masoudi-Nejad A. DrugRep-KG: Toward Learning a Unified Latent Space for Drug Repurposing Using Knowledge Graphs. J Chem Inf Model 2023; 63:2532-2545. [PMID: 37023229 PMCID: PMC10109243 DOI: 10.1021/acs.jcim.2c01291] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Indexed: 04/08/2023]
Abstract
Drug repurposing or repositioning (DR) refers to finding new therapeutic applications for existing drugs. Current computational DR methods face data representation and negative data sampling challenges. Although retrospective studies attempt to operate various representations, it is a crucial step for an accurate prediction to aggregate these features and bring the associations between drugs and diseases into a unified latent space. In addition, the number of unknown associations between drugs and diseases, which is considered negative data, is much higher than the number of known associations, or positive data, leading to an imbalanced dataset. In this regard, we propose the DrugRep-KG method, which applies a knowledge graph embedding approach for representing drugs and diseases, to address these challenges. Despite the typical DR methods that consider all unknown drug-disease associations as negative data, we select a subset of unknown associations, provided the disease occurs because of an adverse reaction to a drug. DrugRep-KG has been evaluated based on different settings and achieves an AUC-ROC (area under the receiver operating characteristic curve) of 90.83% and an AUC-PR (area under the precision-recall curve) of 90.10%, which are higher than in previous works. Besides, we checked the performance of our framework in finding potential drugs for coronavirus infection and skin-related diseases: contact dermatitis and atopic eczema. DrugRep-KG predicted beclomethasone for contact dermatitis, and fluorometholone, clocortolone, fluocinonide, and beclomethasone for atopic eczema, all of which have previously been proven to be effective in other studies. Fluorometholone for contact dermatitis is a novel suggestion by DrugRep-KG that should be validated experimentally. DrugRep-KG also predicted the associations between COVID-19 and potential treatments suggested by DrugBank, in addition to new drug candidates provided with experimental evidence. The data and code underlying this article are available at https://github.com/CBRC-lab/DrugRep-KG.
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Affiliation(s)
- Zahra Ghorbanali
- Department
of Mathematics and Computer Science, Amirkabir
University of Technology, Tehran 1591634311, Iran
| | - Fatemeh Zare-Mirakabad
- Department
of Mathematics and Computer Science, Amirkabir
University of Technology, Tehran 1591634311, Iran
| | - Mohammad Akbari
- Department
of Mathematics and Computer Science, Amirkabir
University of Technology, Tehran 1591634311, Iran
| | - Najmeh Salehi
- School
of Biological Science, Institute for Research
in Fundamental Sciences (IPM), Tehran 19395-5746, Iran
| | - Ali Masoudi-Nejad
- Laboratory
of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry
and Biophysics, University of Tehran, Tehran 1417935840, Iran
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26
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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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27
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Chandak P, Huang K, Zitnik M. Building a knowledge graph to enable precision medicine. Sci Data 2023; 10:67. [PMID: 36732524 PMCID: PMC9893183 DOI: 10.1038/s41597-023-01960-3] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 01/11/2023] [Indexed: 02/04/2023] Open
Abstract
Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology and the ability to dissect the relationship between molecular and genetic factors and their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized repositories, and evolving ontologies describing various scales of biological organization between genotypes and clinical phenotypes. Here, we present PrimeKG, a multimodal knowledge graph for precision medicine analyses. PrimeKG integrates 20 high-quality resources to describe 17,080 diseases with 4,050,249 relationships representing ten major biological scales, including disease-associated protein perturbations, biological processes and pathways, anatomical and phenotypic scales, and the entire range of approved drugs with their therapeutic action, considerably expanding previous efforts in disease-rooted knowledge graphs. PrimeKG contains an abundance of 'indications', 'contradictions', and 'off-label use' drug-disease edges that lack in other knowledge graphs and can support AI analyses of how drugs affect disease-associated networks. We supplement PrimeKG's graph structure with language descriptions of clinical guidelines to enable multimodal analyses and provide instructions for continual updates of PrimeKG as new data become available.
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Affiliation(s)
- Payal Chandak
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, 02139, USA
| | - Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, 02115, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Harvard Data Science Initiative, Cambridge, MA, 02138, USA.
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28
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Yang K, Yang Y, Fan S, Xia J, Zheng Q, Dong X, Liu J, Liu Q, Lei L, Zhang Y, Li B, Gao Z, Zhang R, Liu B, Wang Z, Zhou X. DRONet: effectiveness-driven drug repositioning framework using network embedding and ranking learning. Brief Bioinform 2023; 24:6958501. [PMID: 36562715 DOI: 10.1093/bib/bbac518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/11/2022] [Accepted: 10/31/2022] [Indexed: 12/24/2022] Open
Abstract
As one of the most vital methods in drug development, drug repositioning emphasizes further analysis and research of approved drugs based on the existing large amount of clinical and experimental data to identify new indications of drugs. However, the existing drug repositioning methods didn't achieve enough prediction performance, and these methods do not consider the effectiveness information of drugs, which make it difficult to obtain reliable and valuable results. In this study, we proposed a drug repositioning framework termed DRONet, which make full use of effectiveness comparative relationships (ECR) among drugs as prior information by combining network embedding and ranking learning. We utilized network embedding methods to learn the deep features of drugs from a heterogeneous drug-disease network, and constructed a high-quality drug-indication data set including effectiveness-based drug contrast relationships. The embedding features and ECR of drugs are combined effectively through a designed ranking learning model to prioritize candidate drugs. Comprehensive experiments show that DRONet has higher prediction accuracy (improving 87.4% on Hit@1 and 37.9% on mean reciprocal rank) than state of the art. The case analysis also demonstrates high reliability of predicted results, which has potential to guide clinical drug development.
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Affiliation(s)
- Kuo Yang
- Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | | | - Shuyue Fan
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Jianan Xia
- Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Qiguang Zheng
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Xin Dong
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China
| | - Qiong Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China
| | - Lei Lei
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, China
| | - Yingying Zhang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, China
| | - Bing Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China
| | - Zhuye Gao
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, National Clinical Research Center for Chinese Medicine Cardiology, China
| | - Runshun Zhang
- Guanganmen Hospital, China Academy of Chinese Medical Sciences, China
| | - Baoyan Liu
- Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, China
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China
| | - Xuezhong Zhou
- Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
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Drug repurposing strategy part 1: from approved drugs to agri-bactericides leads. J Antibiot (Tokyo) 2023; 76:27-51. [PMID: 36241714 PMCID: PMC9569004 DOI: 10.1038/s41429-022-00574-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/22/2022] [Accepted: 09/27/2022] [Indexed: 01/06/2023]
Abstract
Phytopathogenic bacteria are a major cause of crop mortality and yield reduction, especially in field cultivation. The lack of effective chemistry agri-bactericides is responsible for challenging field prevention and treatment, prompting the development of long-lasting solutions to prevent, reduce, or manage some of the most devastating plant diseases facing modern agriculture today and in the future. Therefore, there is an urgent need to find lead drugs preventing and treating phytopathogenic bacterial infection. Drug repurposing, a strategy used to identify novel uses for existing approved drugs outside of their original indication, takes less time and investment than Traditional R&D Strategies in the process of drug development. Based on this method, we conduct a screen of 700 chemically diverse and potentially safe drugs against Xanthomonas oryzae PV. oryzae ACCC 11602 (Xoo), Xanthomonas axonopodis PV. citri (Xac), and Pectobacterium atrosepticum ACCC 19901 (Pa). Furthermore, the structure-activity relationship and structural similarity analysis of active drugs classify potent agri-bactericides into 8 lead series: salicylanilides, cationic nitrogen-containing drugs, azole antifungals, N-containing group, hydroxyquinolines, piperazine, kinase inhibitor and miscellaneous groups. MIC values were evaluated as antibacterial activities in this study. Identifying highly active lead compounds from the screening of approved drugs and comparison with the currently applied plant pathogenic bactericide to validate the bactericidal activity of the best candidates and assess if selected molecules or scaffolds lead to develop new antibacterial agents in the future. In conclusion, this study provides a possibility for the development of potent and highly selective agri-bactericides leads.
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30
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Lang X, Liu J, Zhang G, Feng X, Dan W. Knowledge Mapping of Drug Repositioning's Theme and Development. Drug Des Devel Ther 2023; 17:1157-1174. [PMID: 37096060 PMCID: PMC10122475 DOI: 10.2147/dddt.s405906] [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: 01/25/2023] [Accepted: 04/11/2023] [Indexed: 04/26/2023] Open
Abstract
Background In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends. Methodology The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field. Results The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning. Conclusion The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.
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Affiliation(s)
- Xiaona Lang
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Jinlei Liu
- Cardiology Department, Guang ‘anmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, People’s Republic of China
| | - Guangzhong Zhang
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
| | - Xin Feng
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Wenchao Dan
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
- Correspondence: Wenchao Dan, Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China, Tel +86 13652001152, Email
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31
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Munj SA, Taz TA, Arslanturk S, Heath EI. Biomarker-driven drug repurposing on biologically similar cancers with DNA-repair deficiencies. Front Genet 2022; 13:1015531. [PMID: 36583025 PMCID: PMC9792769 DOI: 10.3389/fgene.2022.1015531] [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: 08/09/2022] [Accepted: 11/15/2022] [Indexed: 12/15/2022] Open
Abstract
Similar molecular and genetic aberrations among diseases can lead to the discovery of jointly important treatment options across biologically similar diseases. Oncologists closely looked at several hormone-dependent cancers and identified remarkable pathological and molecular similarities in their DNA repair pathway abnormalities. Although deficiencies in Homologous Recombination (HR) pathway plays a significant role towards cancer progression, there could be other DNA-repair pathway deficiencies that requires careful investigation. In this paper, through a biomarker-driven drug repurposing model, we identified several potential drug candidates for breast and prostate cancer patients with DNA-repair deficiencies based on common specific biomarkers and irrespective of the organ the tumors originated from. Normalized discounted cumulative gain (NDCG) and sensitivity analysis were used to assess the performance of the drug repurposing model. Our results showed that Mitoxantrone and Genistein were among drugs with high therapeutic effects that significantly reverted the gene expression changes caused by the disease (FDR adjusted p-values for prostate cancer =1.225e-4 and 8.195e-8, respectively) for patients with deficiencies in their homologous recombination (HR) pathways. The proposed multi-cancer treatment framework, suitable for patients whose cancers had common specific biomarkers, has the potential to identify promising drug candidates by enriching the study population through the integration of multiple cancers and targeting patients who respond poorly to organ-specific treatments.
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Affiliation(s)
- Seeya Awadhut Munj
- Department of Computer Science, Wayne State University, Detroit, MI, United States
| | - Tasnimul Alam Taz
- Department of Computer Science, Wayne State University, Detroit, MI, United States
| | - Suzan Arslanturk
- Department of Computer Science, Wayne State University, Detroit, MI, United States,*Correspondence: Suzan Arslanturk,
| | - Elisabeth I. Heath
- Department of Oncology, Wayne State University, Detroit, MI, United States,Molecular Therapeutics Program, Barbara Ann Karmanos Cancer Institute, Detroit, MI, United States
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Zhang ML, Zhao BW, Su XR, He YZ, Yang Y, Hu L. RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction. BMC Bioinformatics 2022; 23:516. [PMID: 36456957 PMCID: PMC9713188 DOI: 10.1186/s12859-022-05069-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Drug repositioning is a very important task that provides critical information for exploring the potential efficacy of drugs. Yet developing computational models that can effectively predict drug-disease associations (DDAs) is still a challenging task. Previous studies suggest that the accuracy of DDA prediction can be improved by integrating different types of biological features. But how to conduct an effective integration remains a challenging problem for accurately discovering new indications for approved drugs. METHODS In this paper, we propose a novel meta-path based graph representation learning model, namely RLFDDA, to predict potential DDAs on heterogeneous biological networks. RLFDDA first calculates drug-drug similarities and disease-disease similarities as the intrinsic biological features of drugs and diseases. A heterogeneous network is then constructed by integrating DDAs, disease-protein associations and drug-protein associations. With such a network, RLFDDA adopts a meta-path random walk model to learn the latent representations of drugs and diseases, which are concatenated to construct joint representations of drug-disease associations. As the last step, we employ the random forest classifier to predict potential DDAs with their joint representations. RESULTS To demonstrate the effectiveness of RLFDDA, we have conducted a series of experiments on two benchmark datasets by following a ten-fold cross-validation scheme. The results show that RLFDDA yields the best performance in terms of AUC and F1-score when compared with several state-of-the-art DDAs prediction models. We have also conducted a case study on two common diseases, i.e., paclitaxel and lung tumors, and found that 7 out of top-10 diseases and 8 out of top-10 drugs have already been validated for paclitaxel and lung tumors respectively with literature evidence. Hence, the promising performance of RLFDDA may provide a new perspective for novel DDAs discovery over heterogeneous networks.
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Affiliation(s)
- Meng-Long 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
| | - Bo-Wei Zhao
- 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
| | - Xiao-Rui Su
- 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
| | - Yi-Zhou He
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Yue Yang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - 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
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Jang HY, Oh JM, Kim IW. Drug repurposing using meta-analysis of gene expression in Alzheimer's disease. Front Neurosci 2022; 16:989174. [PMID: 36440278 PMCID: PMC9684643 DOI: 10.3389/fnins.2022.989174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 10/19/2022] [Indexed: 01/25/2023] Open
Abstract
INTRODUCTION Alzheimer's disease and other forms of dementia are disease that bring an increased global burden. However, the medicine developed to date remains limited. The purpose of this study is to predict drug repositioning candidates using a computational method that integrates gene expression profiles on Alzheimer's disease and compound-induced changes in gene expression levels. METHODS Gene expression data on Alzheimer's disease were obtained from the Gene Expression Omnibus (GEO) and we conducted a meta-analysis of their gene expression levels. The reverse scores of compound-induced gene expressions were computed based on the reversal relationship between disease and drug gene expression profiles. RESULTS Reversal genes and the candidate compounds were identified by the leave-one-out cross-validation procedure. Additionally, the half-maximal inhibitory concentration (IC50) values and the blood-brain barrier (BBB) permeability of candidate compounds were obtained from ChEMBL and PubChem, respectively. CONCLUSION New therapeutic target genes and drug candidates against Alzheimer's disease were identified by means of drug repositioning.
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Affiliation(s)
- Ha Young Jang
- Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea
| | - Jung Mi Oh
- Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea,College of Pharmacy, Seoul National University, Seoul, South Korea
| | - In-Wha Kim
- Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea,*Correspondence: In-Wha Kim,
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Iwata M, Kosai K, Ono Y, Oki S, Mimori K, Yamanishi Y. Regulome-based characterization of drug activity across the human diseasome. NPJ Syst Biol Appl 2022; 8:44. [DOI: 10.1038/s41540-022-00255-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 10/21/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractDrugs are expected to recover the cell system away from the impaired state to normalcy through disease treatment. However, the understanding of gene regulatory machinery underlying drug activity or disease pathogenesis is far from complete. Here, we perform large-scale regulome analysis for various diseases in terms of gene regulatory machinery. Transcriptome signatures were converted into regulome signatures of transcription factors by integrating publicly available ChIP-seq data. Regulome-based correlations between diseases and their approved drugs were much clearer than the transcriptome-based correlations. For example, an inverse correlation was observed for cancers, whereas a positive correlation was observed for immune system diseases. After demonstrating the usefulness of the regulome-based drug discovery method in terms of accuracy and applicability, we predicted new drugs for nonsmall cell lung cancer and validated the anticancer activity in vitro. The proposed method is useful for understanding disease–disease relationships and drug discovery.
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Gnilopyat S, DePietro PJ, Parry TK, McLaughlin WA. The Pharmacorank Search Tool for the Retrieval of Prioritized Protein Drug Targets and Drug Repositioning Candidates According to Selected Diseases. Biomolecules 2022; 12:1559. [PMID: 36358909 PMCID: PMC9687941 DOI: 10.3390/biom12111559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/19/2022] [Accepted: 10/22/2022] [Indexed: 08/13/2023] Open
Abstract
We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease's treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer's disease. Via the tool's URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access.
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Affiliation(s)
| | | | | | - William A. McLaughlin
- Department of Medical Education, Geisinger Commonwealth School of Medicine, 525 Pine Street, Scranton, PA 18509, USA
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36
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Das B, Kutsal M, Das R. A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2022; 229:104640. [PMID: 36042844 PMCID: PMC9400382 DOI: 10.1016/j.chemolab.2022.104640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/17/2022] [Accepted: 08/19/2022] [Indexed: 05/04/2023]
Abstract
Although the coronavirus epidemic spread rapidly with the Omicron variant, it lost its lethality rate with the effect of vaccine and immunity. The hospitalization and intense demand decreased. However, there is no definite information about when this disease will end or how dangerous the different variants could be. In addition, it is not possible to end the risk of variants that will continue to circulate among animals in nature. After this stage, drug-virus interactions should be examined in order to be able to prepare against possible new types of viruses and variants and to rapidly-produce drugs or vaccines against possible viruses. Despite experimental methods that are expensive, laborious, and time-consuming, geometric deep learning(GDL) is an alternative method that can be used to make this process faster and cheaper. In this study, we propose a new model based on geometric deep learning for the prediction of drug-virus interaction against COVID-19. First, we use the antiviral drug data in the SMILES molecular structure representation to generate too many features and better describe the structure of chemical species. Then the data is converted into a molecular representation and then into a graphical structure that the GDL model can understand. The node feature vectors are transferred to a different space with the Message Passing Neural Network (MPNN) for the training process to take place. We develop a geometric neural network architecture where the graph embedding values are passed through the fully connected layer and the prediction is actualized. The results indicate that the proposed method outperforms existing methods with 97% accuracy in predicting drug-virus interactions.
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Affiliation(s)
- Bihter Das
- Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey
| | - Mucahit Kutsal
- Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey
| | - Resul Das
- Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey
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Sun G, Dong D, Dong Z, Zhang Q, Fang H, Wang C, Zhang S, Wu S, Dong Y, Wan Y. Drug repositioning: A bibliometric analysis. Front Pharmacol 2022; 13:974849. [PMID: 36225586 PMCID: PMC9549161 DOI: 10.3389/fphar.2022.974849] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/12/2022] [Indexed: 11/14/2022] Open
Abstract
Drug repurposing has become an effective approach to drug discovery, as it offers a new way to explore drugs. Based on the Science Citation Index Expanded (SCI-E) and Social Sciences Citation Index (SSCI) databases of the Web of Science core collection, this study presents a bibliometric analysis of drug repurposing publications from 2010 to 2020. Data were cleaned, mined, and visualized using Derwent Data Analyzer (DDA) software. An overview of the history and development trend of the number of publications, major journals, major countries, major institutions, author keywords, major contributors, and major research fields is provided. There were 2,978 publications included in the study. The findings show that the United States leads in this area of research, followed by China, the United Kingdom, and India. The Chinese Academy of Science published the most research studies, and NIH ranked first on the h-index. The Icahn School of Medicine at Mt Sinai leads in the average number of citations per study. Sci Rep, Drug Discov. Today, and Brief. Bioinform. are the three most productive journals evaluated from three separate perspectives, and pharmacology and pharmacy are unquestionably the most commonly used subject categories. Cheng, FX; Mucke, HAM; and Butte, AJ are the top 20 most prolific and influential authors. Keyword analysis shows that in recent years, most research has focused on drug discovery/drug development, COVID-19/SARS-CoV-2/coronavirus, molecular docking, virtual screening, cancer, and other research areas. The hotspots have changed in recent years, with COVID-19/SARS-CoV-2/coronavirus being the most popular topic for current drug repurposing research.
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Affiliation(s)
- Guojun Sun
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Dashun Dong
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Zuojun Dong
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Qian Zhang
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Hui Fang
- Institute of Information Resource, Zhejiang University of Technology, Hangzhou, China
| | - Chaojun Wang
- Hangzhou Aeronautical Sanatorium for Special Service of Chinese Air Force, Hangzhou, China
| | - Shaoya Zhang
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Shuaijun Wu
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Yichen Dong
- Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, China
| | - Yuehua Wan
- Institute of Information Resource, Zhejiang University of Technology, Hangzhou, China
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Hernández-Parra H, Cortés H, Avalos-Fuentes JA, Del Prado-Audelo M, Florán B, Leyva-Gómez G, Sharifi-Rad J, Cho WC. Repositioning of drugs for Parkinson's disease and pharmaceutical nanotechnology tools for their optimization. J Nanobiotechnology 2022; 20:413. [PMID: 36109747 PMCID: PMC9479294 DOI: 10.1186/s12951-022-01612-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/31/2022] [Indexed: 11/10/2022] Open
Abstract
Parkinson's disease (PD) significantly affects patients' quality of life and represents a high economic burden for health systems. Given the lack of safe and effective treatments for PD, drug repositioning seeks to offer new medication alternatives, reducing research time and costs compared to the traditional drug development strategy. This review aimed to collect evidence of drugs proposed as candidates to be reused in PD and identify those with the potential to be reformulated into nanocarriers to optimize future repositioning trials. We conducted a detailed search in PubMed, Web of Science, and Scopus from January 2015 at the end of 2021, with the descriptors "Parkinson's disease" and "drug repositioning" or "drug repurposing". We identified 28 drugs as potential candidates, and six of them were found in repositioning clinical trials for PD. However, a limitation of many of these drugs to achieve therapeutic success is their inability to cross the blood-brain barrier (BBB), as is the case with nilotinib, which has shown promising outcomes in clinical trials. We suggest reformulating these drugs in biodegradable nanoparticles (NPs) based on lipids and polymers to perform future trials. As a complementary strategy, we propose functionalizing the NPs surface by adding materials to the surface layer. Among other advantages, functionalization can promote efficient crossing through the BBB and improve the affinity of NPs towards certain brain regions. The main parameters to consider for the design of NPs targeting the central nervous system are highlighted, such as size, PDI, morphology, drug load, and Z potential. Finally, current advances in the use of NPs for Parkinson's disease are cited.
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Affiliation(s)
- Héctor Hernández-Parra
- Departamento de Farmacología, Centro de Investigación Y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Ciudad de Mexico, Mexico
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de Mexico, Mexico
| | - Hernán Cortés
- Laboratorio de Medicina Genómica, Departamento de Genómica, Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Ciudad de Mexico, Mexico
| | - José Arturo Avalos-Fuentes
- Departamento de Fisiología, Biofísica & Neurociencias, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Ciudad de Mexico, Mexico
| | - María Del Prado-Audelo
- Escuela de Ingeniería Y Ciencias, Tecnologico de Monterrey, Campus Ciudad de México, C. Puente 222, 14380 Ciudad de México, Mexico
| | - Benjamín Florán
- Departamento de Fisiología, Biofísica & Neurociencias, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Ciudad de Mexico, Mexico
| | - Gerardo Leyva-Gómez
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de Mexico, Mexico
| | | | - William C. Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong
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Pu Y, Li J, Tang J, Guo F. DeepFusionDTA: Drug-Target Binding Affinity Prediction With Information Fusion and Hybrid Deep-Learning Ensemble Model. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2760-2769. [PMID: 34379594 DOI: 10.1109/tcbb.2021.3103966] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Identification of drug-target interaction (DTI) is the most important issue in the broad field of drug discovery. Using purely biological experiments to verify drug-target binding profiles takes lots of time and effort, so computational technologies for this task obviously have great benefits in reducing the drug search space. Most of computational methods to predict DTI are proposed to solve a binary classification problem, which ignore the influence of binding strength. Therefore, drug-target binding affinity prediction is still a challenging issue. Currently, lots of studies only extract sequence information that lacks feature-rich representation, but we consider more spatial features in order to merge various data in drug and target spaces. In this study, we propose a two-stage deep neural network ensemble model for detecting drug-target binding affinity, called DeepFusionDTA, via various information analysis modules. First stage is to utilize sequence and structure information to generate fusion feature map of candidate protein and drug pair through various analysis modules based deep learning. Second stage is to apply bagging-based ensemble learning strategy for regression prediction, and we obtain outstanding results by combining the advantages of various algorithms in efficient feature abstraction and regression calculation. Importantly, we evaluate our novel method, DeepFusionDTA, which delivers 1.5 percent CI increase on KIBA dataset and 1.0 percent increase on Davis dataset, by comparing with existing prediction tools, DeepDTA. Furthermore, the ideas we have offered can be applied to in-silico screening of the interaction space, to provide novel DTIs which can be experimentally pursued. The codes and data are available from https://github.com/guofei-tju/DeepFusionDTA.
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40
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Gao Z, Ding P, Xu R. KG-Predict: A knowledge graph computational framework for drug repurposing. J Biomed Inform 2022; 132:104133. [PMID: 35840060 PMCID: PMC9595135 DOI: 10.1016/j.jbi.2022.104133] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 06/18/2022] [Accepted: 07/03/2022] [Indexed: 11/26/2022]
Abstract
The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data has offered unprecedented opportunities for drug discovery including drug repurposing. Various knowledge graph-based methods have been developed to integrate and analyze complex and heterogeneous data sources to find new therapeutic applications for existing drugs. However, existing methods have limitations in modeling and capturing context-sensitive inter-relationships among tens of thousands of biomedical entities. In this paper, we developed KG-Predict: a knowledge graph computational framework for drug repurposing. We first integrated multiple types of entities and relations from various genotypic and phenotypic databases to construct a knowledge graph termed GP-KG. GP-KG was composed of 1,246,726 associations between 61,146 entities. KG-Predict then aggregated the heterogeneous topological and semantic information from GP-KG to learn low-dimensional representations of entities and relations, and further utilized these representations to infer new drug-disease interactions. In cross-validation experiments, KG-Predict achieved high performances [AUROC (the area under receiver operating characteristic) = 0.981, AUPR (the area under precision-recall) = 0.409 and MRR (the mean reciprocal rank) = 0.261], outperforming other state-of-art graph embedding methods. We applied KG-Predict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD) and showed that KG-Predict prioritized both FDA-approved and active clinical trial anti-AD drugs among the top (AUROC = 0.868 and AUPR = 0.364).
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Affiliation(s)
- Zhenxiang Gao
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, 44106 OH, USA.
| | - Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, 44106 OH, USA.
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, 44106 OH, USA.
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Repositioning of Quinazolinedione-Based Compounds on Soluble Epoxide Hydrolase (sEH) through 3D Structure-Based Pharmacophore Model-Driven Investigation. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27123866. [PMID: 35744994 PMCID: PMC9228872 DOI: 10.3390/molecules27123866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/08/2022] [Accepted: 06/14/2022] [Indexed: 12/01/2022]
Abstract
The development of new bioactive compounds represents one of the main purposes of the drug discovery process. Various tools can be employed to identify new drug candidates against pharmacologically relevant biological targets, and the search for new approaches and methodologies often represents a critical issue. In this context, in silico drug repositioning procedures are required even more in order to re-evaluate compounds that already showed poor biological results against a specific biological target. 3D structure-based pharmacophoric models, usually built for specific targets to accelerate the identification of new promising compounds, can be employed for drug repositioning campaigns as well. In this work, an in-house library of 190 synthesized compounds was re-evaluated using a 3D structure-based pharmacophoric model developed on soluble epoxide hydrolase (sEH). Among the analyzed compounds, a small set of quinazolinedione-based molecules, originally selected from a virtual combinatorial library and showing poor results when preliminarily investigated against heat shock protein 90 (Hsp90), was successfully repositioned against sEH, accounting the related built 3D structure-based pharmacophoric model. The promising results here obtained highlight the reliability of this computational workflow for accelerating the drug discovery/repositioning processes.
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A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
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43
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Zhang Y, Lei X, Pan Y, Wu FX. Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks. Front Pharmacol 2022; 13:872785. [PMID: 35620297 PMCID: PMC9127467 DOI: 10.3389/fphar.2022.872785] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 04/11/2022] [Indexed: 11/29/2022] Open
Abstract
The understanding of therapeutic properties is important in drug repositioning and drug discovery. However, chemical or clinical trials are expensive and inefficient to characterize the therapeutic properties of drugs. Recently, artificial intelligence (AI)-assisted algorithms have received extensive attention for discovering the potential therapeutic properties of drugs and speeding up drug development. In this study, we propose a new method based on GraphSAGE and clustering constraints (DRGCC) to investigate the potential therapeutic properties of drugs for drug repositioning. First, the drug structure features and disease symptom features are extracted. Second, the drug–drug interaction network and disease similarity network are constructed according to the drug–gene and disease–gene relationships. Matrix factorization is adopted to extract the clustering features of networks. Then, all the features are fed to the GraphSAGE to predict new associations between existing drugs and diseases. Benchmark comparisons on two different datasets show that our method has reliable predictive performance and outperforms other six competing. We have also conducted case studies on existing drugs and diseases and aimed to predict drugs that may be effective for the novel coronavirus disease 2019 (COVID-19). Among the predicted anti-COVID-19 drug candidates, some drugs are being clinically studied by pharmacologists, and their binding sites to COVID-19-related protein receptors have been found via the molecular docking technology.
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Affiliation(s)
- Yuchen Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
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DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network. Curr Issues Mol Biol 2022; 44:2287-2299. [PMID: 35678684 PMCID: PMC9164023 DOI: 10.3390/cimb44050155] [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: 03/24/2022] [Revised: 05/08/2022] [Accepted: 05/14/2022] [Indexed: 11/17/2022] Open
Abstract
Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.
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45
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Liu S, Wang Y, Deng Y, He L, Shao B, Yin J, Zheng N, Liu TY, Wang T. Improved drug-target interaction prediction with intermolecular graph transformer. Brief Bioinform 2022; 23:6581433. [PMID: 35514186 DOI: 10.1093/bib/bbac162] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/28/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
The identification of active binding drugs for target proteins (referred to as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieve better performance than molecular docking, existing models often neglect topological or spatial of intermolecular information, hindering prediction performance. We recognize this problem and propose a novel approach called the Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art (SoTA) approaches by 9.1% and 20.5% over the second best option for binding activity and binding pose prediction, respectively, and exhibits superior generalization ability to unseen receptor proteins than SoTA approaches. Furthermore, IGT exhibits promising drug screening ability against severe acute respiratory syndrome coronavirus 2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. Source code and datasets are available at https://github.com/microsoft/IGT-Intermolecular-Graph-Transformer.
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Affiliation(s)
- Siyuan Liu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.,Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China.,Microsoft Research Asia, Beijing, 100080, China
| | - Yusong Wang
- Microsoft Research Asia, Beijing, 100080, China.,Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yifan Deng
- Microsoft Research Asia, Beijing, 100080, China
| | - Liang He
- Microsoft Research Asia, Beijing, 100080, China.,School of Computer Science, Fudan University, Shanghai, 200433, China
| | - Bin Shao
- Microsoft Research Asia, Beijing, 100080, China
| | - Jian Yin
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.,Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China
| | - Nanning Zheng
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Tie-Yan Liu
- Microsoft Research Asia, Beijing, 100080, China
| | - Tong Wang
- Microsoft Research Asia, Beijing, 100080, China
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Pham TH, Qiu Y, Liu J, Zimmer S, O’Neill E, Xie L, Zhang P. Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing. PATTERNS 2022; 3:100441. [PMID: 35465231 PMCID: PMC9023899 DOI: 10.1016/j.patter.2022.100441] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/13/2021] [Accepted: 01/12/2022] [Indexed: 12/18/2022]
Abstract
Chemical-induced gene expression profiles provide critical information of chemicals in a biological system, thus offering new opportunities for drug discovery. Despite their success, large-scale analysis leveraging gene expressions is limited by time and cost. Although several methods for predicting gene expressions were proposed, they only focused on imputation and classification settings, which have limited applications to real-world scenarios of drug discovery. Therefore, a chemical-induced gene expression ranking (CIGER) framework is proposed to target a more realistic but more challenging setting in which overall rankings in gene expression profiles induced by de novo chemicals are predicted. The experimental results show that CIGER significantly outperforms existing methods in both ranking and classification metrics. Furthermore, a drug screening pipeline based on CIGER is proposed to identify potential treatments of drug-resistant pancreatic cancer. Our predictions have been validated by experiments, thereby showing the effectiveness of CIGER for phenotypic compound screening of precision medicine. A new deep-learning method (CIGER) for chemical-induced gene expression ranking CIGER can predict gene expression for de novo chemicals from chemical structures We discovered drugs for the treatment of drug-resistant pancreatic cancer
In recent years, a phenotype-based drug discovery approach using chemical-induced gene expressions has shown to be effective in drug discovery and precision medicine. However, it is not feasible to experimentally determine chemical-induced gene expressions for all available chemicals of interest, thereby hindering the application of gene expression-based compound screening on a large scale. Thus, it is crucial to design a computational approach that can generate gene expression information for any chemicals. We proposed a new, deep-learning framework named chemical-induced gene expression ranking (CIGER) to predict a landmark gene expression profile (i.e., gene ranking) induced by de novo chemicals based on their chemical structures. Leveraging CIGER, we predicted and experimentally validated that several existing drugs can increase the therapeutic response on drug-resistant pancreatic cancer. Our results demonstrated the effectiveness of CIGER for precision drug discovery in practice.
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Affiliation(s)
- Thai-Hoang Pham
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Yue Qiu
- Ph.D. Program in Biology, The Graduate Center, The City University of New York, New York, NY 10016, USA
| | - Jiahui Liu
- Department of Oncology, University of Oxford, Oxford OX3 7DQ, UK
| | | | - Eric O’Neill
- Department of Oncology, University of Oxford, Oxford OX3 7DQ, UK
- EpiCombi.AI Therapeutics, Oxford OX7 3SB, UK
| | - Lei Xie
- Ph.D. Program in Biology, The Graduate Center, The City University of New York, New York, NY 10016, USA
- Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, USA
- Ph.D. Program in Computer Science and Biochemistry, The Graduate Center, The City University of New York, New York, NY 10016, USA
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Ping Zhang
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
- Corresponding author
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Qin L, Wang J, Wu Z, Li W, Liu G, Tang Y. Drug Repurposing for Newly Emerged Diseases via Network-Based Inference on A Gene-Disease-Drug Network. Mol Inform 2022; 41:e2200001. [PMID: 35338586 DOI: 10.1002/minf.202200001] [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: 01/05/2022] [Accepted: 03/25/2022] [Indexed: 11/06/2022]
Abstract
Identification of disease-drug associations is an effective strategy for drug repurposing, especially in searching old drugs for newly emerged diseases like COVID-19. In this study, we put forward a network-based method named NEDNBI to predict disease-drug associations based on a gene-disease-drug tripartite network, which could be applied in drug repurposing. The novelty of our method lies in the fact that no negative data are required, and new disease could be added into the disease-drug network with gene as the bridge. The comprehensive evaluation results showed that the proposed method had good performance, with AUC value 0.948 ± 0.009 for 10-fold cross validation. In a case study, 8 of the 20 predicted old drugs have been tested clinically for the treatment of COVID-19, which illustrated the usefulness of our method in drug repurposing. The source code and data of the method are available at https://github.com/Qli97/NEDNBI.
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Affiliation(s)
- Li Qin
- East China University of Science and Technology School of Pharmacy, CHINA
| | - Jiye Wang
- East China University of Science and Technology School of Pharmacy, CHINA
| | - Zengrui Wu
- East China University of Science and Technology, CHINA
| | | | - Guixia Liu
- East China University of Science and Technology, CHINA
| | - Yun Tang
- East China University of Science and Technology, CHINA
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Gurbuz O, Alanis-Lobato G, Picart-Armada S, Sun M, Haslinger C, Lawless N, Fernandez-Albert F. Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method. Front Genet 2022; 13:814093. [PMID: 35360842 PMCID: PMC8963915 DOI: 10.3389/fgene.2022.814093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 01/28/2022] [Indexed: 11/19/2022] Open
Abstract
Indication expansion aims to find new indications for existing targets in order to accelerate the process of launching a new drug for a disease on the market. The rapid increase in data types and data sources for computational drug discovery has fostered the use of semantic knowledge graphs (KGs) for indication expansion through target centric approaches, or in other words, target repositioning. Previously, we developed a novel method to construct a KG for indication expansion studies, with the aim of finding and justifying alternative indications for a target gene of interest. In contrast to other KGs, ours combines human-curated full-text literature and gene expression data from biomedical databases to encode relationships between genes, diseases, and tissues. Here, we assessed the suitability of our KG for explainable target-disease link prediction using a glass-box approach. To evaluate the predictive power of our KG, we applied shortest path with tissue information- and embedding-based prediction methods to a graph constructed with information published before or during 2010. We also obtained random baselines by applying the shortest path predictive methods to KGs with randomly shuffled node labels. Then, we evaluated the accuracy of the top predictions using gene-disease links reported after 2010. In addition, we investigated the contribution of the KG’s tissue expression entity to the prediction performance. Our experiments showed that shortest path-based methods significantly outperform the random baselines and embedding-based methods outperform the shortest path predictions. Importantly, removing the tissue expression entity from the KG severely impacts the quality of the predictions, especially those produced by the embedding approaches. Finally, since the interpretability of the predictions is crucial in indication expansion, we highlight the advantages of our glass-box model through the examination of example candidate target-disease predictions.
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Affiliation(s)
- Ozge Gurbuz
- Discovery Research Coordination Germany, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
- *Correspondence: Ozge Gurbuz, ; Francesc Fernandez-Albert,
| | - Gregorio Alanis-Lobato
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Sergio Picart-Armada
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Miao Sun
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Christian Haslinger
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Nathan Lawless
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Francesc Fernandez-Albert
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
- *Correspondence: Ozge Gurbuz, ; Francesc Fernandez-Albert,
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49
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Zafirlukast inhibits the growth of lung adenocarcinoma via inhibiting TMEM16A channel activity. J Biol Chem 2022; 298:101731. [PMID: 35176281 PMCID: PMC8931426 DOI: 10.1016/j.jbc.2022.101731] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/08/2022] [Accepted: 02/11/2022] [Indexed: 01/05/2023] Open
Abstract
Lung cancer has the highest mortality among cancers worldwide due to its high incidence and lack of the effective cures. We have previously demonstrated that the membrane ion channel TMEM16A is a potential drug target for the treatment of lung adenocarcinoma and have identified a pocket of inhibitor binding that provides the basis for screening promising new inhibitors. However, conventional drug discovery strategies are lengthy and costly, and the unpredictable side effects lead to a high failure rate in drug development. Therefore, finding new therapeutic directions for already marketed drugs may be a feasible strategy to obtain safe and effective therapeutic drugs. Here, we screened a library of over 1400 Food and Drug Administration-approved drugs through virtual screening and activity testing. We identified a drug candidate, Zafirlukast (ZAF), clinically approved for the treatment of asthma, that could inhibit the TMEM16A channel in a concentration-dependent manner. Molecular dynamics simulations and site-directed mutagenesis experiments showed that ZAF can bind to S387/N533/R535 in the nonselective inhibitor binding pocket, thereby blocking the channel pore. Furthermore, we demonstrate ZAF can target TMEM16A channel to inhibit the proliferation and migration of lung adenocarcinoma LA795 cells. In vivo experiments showed that ZAF can significantly inhibit lung adenocarcinoma tumor growth in mice. Taken together, we identified ZAF as a novel TMEM16A channel inhibitor with excellent anticancer activity, and as such, it represents a promising candidate for future preclinical and clinical studies.
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50
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Nguyen TM, Nguyen T, Le TM, Tran T. GEFA: Early Fusion Approach in Drug-Target Affinity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:718-728. [PMID: 34197324 DOI: 10.1109/tcbb.2021.3094217] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA)problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues. This would lead to inaccurate learning of target representation which may change due to the drug binding effects. In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets. We propose GEFA (Graph Early Fusion Affinity), a novel graph-in-graph neural network with attention mechanism to address the changes in target representation because of the binding effects. Specifically, a drug is modeled as a graph of atoms, which then serves as a node in a larger graph of residues-drug complex. The resulting model is an expressive deep nested graph neural network. We also use pre-trained protein representation powered by the recent effort of learning contextualized protein representation. The experiments are conducted under different settings to evaluate scenarios such as novel drugs or targets. The results demonstrate the effectiveness of the pre-trained protein embedding and the advantages our GEFA in modeling the nested graph for drug-target interaction.
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