1
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Carpenter KA, Altman RB. Databases of ligand-binding pockets and protein-ligand interactions. Comput Struct Biotechnol J 2024; 23:1320-1338. [PMID: 38585646 PMCID: PMC10997877 DOI: 10.1016/j.csbj.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/16/2024] [Accepted: 03/17/2024] [Indexed: 04/09/2024] Open
Abstract
Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.
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Affiliation(s)
- Kristy A. Carpenter
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
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2
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Kim J, Kim BK, Moh SH, Jang G, Ryu JY. Investigation of the General Molecular Mechanisms of Gallic Acid via Analyses of Its Transcriptome Profile. Int J Mol Sci 2024; 25:2303. [PMID: 38396979 PMCID: PMC10888745 DOI: 10.3390/ijms25042303] [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: 12/05/2023] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Gallic acid (GA), a phenolic compound naturally found in many plants, exhibits potential preventive and therapeutic roles. However, the underlying molecular mechanisms of its diverse biological activities remain unclear. Here, we investigated possible mechanisms of GA function through a transcriptome-based analysis using LINCS L1000, a publicly available data resource. We compared the changes in the gene expression profiles induced by GA with those induced by FDA-approved drugs in three cancer cell lines (A549, PC3, and MCF7). The top 10 drugs exhibiting high similarity with GA in their expression patterns were identified by calculating the connectivity score in the three cell lines. We specified the known target proteins of these drugs, which could be potential targets of GA, and identified 19 potential targets. Next, we retrieved evidence in the literature that GA likely binds directly to DNA polymerase β and ribonucleoside-diphosphate reductase. Although our results align with previous studies suggesting a direct and/or indirect connection between GA and the target proteins, further experimental investigations are required to fully understand the exact molecular mechanisms of GA. Our study provides insights into the therapeutic mechanisms of GA, introducing a new approach to characterizing therapeutic natural compounds using transcriptome-based analyses.
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Affiliation(s)
- Jiyeon Kim
- Laboratory of Theriogenology and Biotechnology, Department of Veterinary Clinical Science, College of Veterinary Medicine and the Research Institute of Veterinary Science, Seoul National University, Seoul 08826, Republic of Korea;
- Plant Cell Research Institute of BIO-FD&C Co., Ltd., Incheon 21990, Republic of Korea;
| | - Bo Kyung Kim
- Department of Biotechnology, Duksung Women’s University, 33 Samyang-Ro 144-Gil, Dobong-gu, Seoul 01369, Republic of Korea;
| | - Sang Hyun Moh
- Plant Cell Research Institute of BIO-FD&C Co., Ltd., Incheon 21990, Republic of Korea;
| | - Goo Jang
- Laboratory of Theriogenology and Biotechnology, Department of Veterinary Clinical Science, College of Veterinary Medicine and the Research Institute of Veterinary Science, Seoul National University, Seoul 08826, Republic of Korea;
| | - Jae Yong Ryu
- Department of Biotechnology, Duksung Women’s University, 33 Samyang-Ro 144-Gil, Dobong-gu, Seoul 01369, Republic of Korea;
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3
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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] [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
- *Correspondence: Yuehua Wan,
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4
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Monteiro NR, Oliveira JL, Arrais JP. DTITR: End-to-end drug–target binding affinity prediction with transformers. Comput Biol Med 2022; 147:105772. [DOI: 10.1016/j.compbiomed.2022.105772] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/07/2022] [Accepted: 06/19/2022] [Indexed: 11/03/2022]
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5
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Zhao Q, Yang M, Cheng Z, Li Y, Wang J. Biomedical Data and Deep Learning Computational Models for Predicting Compound-Protein Relations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2092-2110. [PMID: 33769935 DOI: 10.1109/tcbb.2021.3069040] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The identification of compound-protein relations (CPRs), which includes compound-protein interactions (CPIs) and compound-protein affinities (CPAs), is critical to drug development. A common method for compound-protein relation identification is the use of in vitro screening experiments. However, the number of compounds and proteins is massive, and in vitro screening experiments are labor-intensive, expensive, and time-consuming with high failure rates. Researchers have developed a computational field called virtual screening (VS) to aid experimental drug development. These methods utilize experimentally validated biological interaction information to generate datasets and use the physicochemical and structural properties of compounds and target proteins as input information to train computational prediction models. At present, deep learning has been widely used in computer vision and natural language processing and has experienced epoch-making progress. At the same time, deep learning has also been used in the field of biomedicine widely, and the prediction of CPRs based on deep learning has developed rapidly and has achieved good results. The purpose of this study is to investigate and discuss the latest applications of deep learning techniques in CPR prediction. First, we describe the datasets and feature engineering (i.e., compound and protein representations and descriptors) commonly used in CPR prediction methods. Then, we review and classify recent deep learning approaches in CPR prediction. Next, a comprehensive comparison is performed to demonstrate the prediction performance of representative methods on classical datasets. Finally, we discuss the current state of the field, including the existing challenges and our proposed future directions. We believe that this investigation will provide sufficient references and insight for researchers to understand and develop new deep learning methods to enhance CPR predictions.
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6
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Monteiro NRC, Ribeiro B, Arrais JP. Drug-Target Interaction Prediction: End-to-End Deep Learning Approach. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2364-2374. [PMID: 32142454 DOI: 10.1109/tcbb.2020.2977335] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Although putting efforts on the traditional in vivo or in vitro methods, pharmaceutical financial investment has been reduced over the years. Therefore, establishing effective computational methods is decisive to find new leads in a reasonable amount of time. Successful approaches have been presented to solve this problem but seldom protein sequences and structured data are used together. In this paper, we present a deep learning architecture model, which exploits the particular ability of Convolutional Neural Networks (CNNs) to obtain 1D representations from protein sequences (amino acid sequence) and compounds SMILES (Simplified Molecular Input Line Entry System) strings. These representations can be interpreted as features that express local dependencies or patterns that can then be used in a Fully Connected Neural Network (FCNN), acting as a binary classifier. The results achieved demonstrate that using CNNs to obtain representations of the data, instead of the traditional descriptors, lead to improved performance. The proposed end-to-end deep learning method outperformed traditional machine learning approaches in the correct classification of both positive and negative interactions.
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7
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Lim G, Lim CJ, Lee JH, Lee BH, Ryu JY, Oh KS. Identification of new target proteins of a Urotensin-II receptor antagonist using transcriptome-based drug repositioning approach. Sci Rep 2021; 11:17138. [PMID: 34429474 PMCID: PMC8384862 DOI: 10.1038/s41598-021-96612-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 08/11/2021] [Indexed: 12/20/2022] Open
Abstract
Drug repositioning research using transcriptome data has recently attracted attention. In this study, we attempted to identify new target proteins of the urotensin-II receptor antagonist, KR-37524 (4-(3-bromo-4-(piperidin-4-yloxy)benzyl)-N-(3-(dimethylamino)phenyl)piperazine-1-carboxamide dihydrochloride), using a transcriptome-based drug repositioning approach. To do this, we obtained KR-37524-induced gene expression profile changes in four cell lines (A375, A549, MCF7, and PC3), and compared them with the approved drug-induced gene expression profile changes available in the LINCS L1000 database to identify approved drugs with similar gene expression profile changes. Here, the similarity between the two gene expression profile changes was calculated using the connectivity score. We then selected proteins that are known targets of the top three approved drugs with the highest connectivity score in each cell line (12 drugs in total) as potential targets of KR-37524. Seven potential target proteins were experimentally confirmed using an in vitro binding assay. Through this analysis, we identified that neurologically regulated serotonin transporter proteins are new target proteins of KR-37524. These results indicate that the transcriptome-based drug repositioning approach can be used to identify new target proteins of a given compound, and we provide a standalone software developed in this study that will serve as a useful tool for drug repositioning.
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Affiliation(s)
- Gyutae Lim
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea
| | - Chae Jo Lim
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea
| | - Jeong Hyun Lee
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea
| | - Byung Ho Lee
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea
| | - Jae Yong Ryu
- Department of Biotechnology, Duksung Women's University, 33 Samyang-ro 144-gil, Dobong-gu, Seoul, 01369, Republic of Korea.
| | - Kwang-Seok Oh
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea. .,Department of Medicinal and Pharmaceutical Chemistry, University of Science and Technology, 217 Gajeong-ro, Yuseong,-gu, Daejeon, 34113, Republic of Korea.
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8
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He S, Wen Y, Yang X, Liu Z, Song X, Huang X, Bo X. PIMD: An Integrative Approach for Drug Repositioning Using Multiple Characterization Fusion. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:565-581. [PMID: 33075523 PMCID: PMC8377380 DOI: 10.1016/j.gpb.2018.10.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 09/21/2018] [Accepted: 10/10/2018] [Indexed: 11/28/2022]
Abstract
The accumulation of various types of drug informatics data and computational approaches for drug repositioning can accelerate pharmaceutical research and development. However, the integration of multi-dimensional drug data for precision repositioning remains a pressing challenge. Here, we propose a systematic framework named PIMD to predict drug therapeutic properties by integrating multi-dimensional data for drug repositioning. In PIMD, drug similarity networks (DSNs) based on chemical, pharmacological, and clinical data are fused into an integrated DSN (iDSN) composed of many clusters. Rather than simple fusion, PIMD offers a systematic way to annotate clusters. Unexpected drugs within clusters and drug pairs with a high iDSN similarity score are therefore identified to predict novel therapeutic uses. PIMD provides new insights into the universality, individuality, and complementarity of different drug properties by evaluating the contribution of each property data. To test the performance of PIMD, we use chemical, pharmacological, and clinical properties to generate an iDSN. Analyses of the contributions of each drug property indicate that this iDSN was driven by all data types and performs better than other DSNs. Within the top 20 recommended drug pairs, 7 drugs have been reported to be repurposed. The source code for PIMD is available at https://github.com/Sepstar/PIMD/.
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Affiliation(s)
- Song He
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiaoxi Yang
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Zhen Liu
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xinyu Song
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xin Huang
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China.
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9
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Wang C, Wang W, Lu K, Zhang J, Chen P, Wang B. Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition. Int J Mol Sci 2020; 21:ijms21165694. [PMID: 32784497 PMCID: PMC7570185 DOI: 10.3390/ijms21165694] [Citation(s) in RCA: 6] [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: 07/18/2020] [Revised: 08/05/2020] [Accepted: 08/06/2020] [Indexed: 12/13/2022] Open
Abstract
The task of drug-target interaction (DTI) prediction plays important roles in drug development. The experimental methods in DTIs are time-consuming, expensive and challenging. To solve these problems, machine learning-based methods are introduced, which are restricted by effective feature extraction and negative sampling. In this work, features with electrotopological state (E-state) fingerprints for drugs and amphiphilic pseudo amino acid composition (APAAC) for target proteins are tested. E-state fingerprints are extracted based on both molecular electronic and topological features with the same metric. APAAC is an extension of amino acid composition (AAC), which is calculated based on hydrophilic and hydrophobic characters to construct sequence order information. Using the combination of these feature pairs, the prediction model is established by support vector machines. In order to enhance the effectiveness of features, a distance-based negative sampling is proposed to obtain reliable negative samples. It is shown that the prediction results of area under curve for Receiver Operating Characteristic (AUC) are above 98.5% for all the three datasets in this work. The comparison of state-of-the-art methods demonstrates the effectiveness and efficiency of proposed method, which will be helpful for further drug development.
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Affiliation(s)
- Cheng Wang
- Department of Computer Science & Technology, Tongji University, Shanghai 201804, China;
| | - Wenyan Wang
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan 243002, China; (W.W.); (K.L.)
- Key Laboratory of Power Electronics and Motion Control Anhui Education Department, Ma’anshan 243032, China
| | - Kun Lu
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan 243002, China; (W.W.); (K.L.)
| | - Jun Zhang
- Institutes of Physical Science and Information Technology & School of Internet, Anhui University, Hefei 230601, China;
| | - Peng Chen
- Institutes of Physical Science and Information Technology & School of Internet, Anhui University, Hefei 230601, China;
- Correspondence: (P.C.); (B.W.)
| | - Bing Wang
- Department of Computer Science & Technology, Tongji University, Shanghai 201804, China;
- School of Electrical & Information Engineering, Anhui University of Technology, Ma’anshan 243002, China; (W.W.); (K.L.)
- Key Laboratory of Power Electronics and Motion Control Anhui Education Department, Ma’anshan 243032, China
- Correspondence: (P.C.); (B.W.)
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10
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Thafar MA, Olayan RS, Ashoor H, Albaradei S, Bajic VB, Gao X, Gojobori T, Essack M. DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques. J Cheminform 2020; 12:44. [PMID: 33431036 PMCID: PMC7325230 DOI: 10.1186/s13321-020-00447-2] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/16/2020] [Indexed: 12/14/2022] Open
Abstract
In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug–Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.
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Affiliation(s)
- Maha A Thafar
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Collage of Computers and Information Technology, Taif University, Taif, Kingdom of Saudi Arabia
| | - Rawan S Olayan
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Haitham Ashoor
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Somayah Albaradei
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - Vladimir B Bajic
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
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11
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Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions. Sci Rep 2020; 10:6641. [PMID: 32313024 PMCID: PMC7171114 DOI: 10.1038/s41598-020-62891-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 03/12/2020] [Indexed: 01/29/2023] Open
Abstract
Accumulating evidence has shown that drug-target interactions (DTIs) play a crucial role in the process of genomic drug discovery. Although biological experimental technology has made great progress, the identification of DTIs is still very time-consuming and expensive nowadays. Hence it is urgent to develop in silico model as a supplement to the biological experiments to predict the potential DTIs. In this work, a new model is designed to predict DTIs by incorporating chemical sub-structures and protein evolutionary information. Specifically, we first use Position-Specific Scoring Matrix (PSSM) to convert the protein sequence into the numerical descriptor containing biological evolutionary information, then use Discrete Cosine Transform (DCT) algorithm to extract the hidden features and integrate them with the chemical sub-structures descriptor, and finally utilize Rotation Forest (RF) classifier to accurately predict whether there is interaction between the drug and the target protein. In the 5-fold cross-validation (CV) experiment, the average accuracy of the proposed model on the benchmark datasets of Enzymes, Ion Channels, GPCRs and Nuclear Receptors reached 0.9140, 0.8919, 0.8724 and 0.8111, respectively. In order to fully evaluate the performance of the proposed model, we compare it with different feature extraction model, classifier model, and other state-of-the-art models. Furthermore, we also implemented case studies. As a result, 8 of the top 10 drug-target pairs with the highest prediction score were confirmed by related databases. These excellent results indicate that the proposed model has outstanding ability in predicting DTIs and can provide reliable candidates for biological experiments.
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12
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Srivastav A, Dhorje S, Lavhate P. In silico drug repurposing: An antifungal drug, itraconazole, repurposed as an anticancer agent using molecular docking. MGM JOURNAL OF MEDICAL SCIENCES 2020. [DOI: 10.4103/mgmj.mgmj_31_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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13
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Zhang J, Chai H. Recent In Silico Research in High-Throughput Drug Discovery and Molecular Biochemistry. Curr Top Med Chem 2019; 19:103-104. [PMID: 30950333 DOI: 10.2174/156802661902190328150226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology Xinyang Normal University, China
| | - Haiting Chai
- College of Medical, Veterinary and Life Sciences University of Glasgow, United Kingdom
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14
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Farha MA, Brown ED. Drug repurposing for antimicrobial discovery. Nat Microbiol 2019; 4:565-577. [PMID: 30833727 DOI: 10.1038/s41564-019-0357-1] [Citation(s) in RCA: 181] [Impact Index Per Article: 36.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 01/03/2019] [Indexed: 12/17/2022]
Abstract
Antimicrobial resistance continues to be a public threat on a global scale. The ongoing need to develop new antimicrobial drugs that are effective against multi-drug-resistant pathogens has spurred the research community to invest in various drug discovery strategies, one of which is drug repurposing-the process of finding new uses for existing drugs. While still nascent in the antimicrobial field, the approach is gaining traction in both the public and private sector. While the approach has particular promise in fast-tracking compounds into clinical studies, it nevertheless has substantial obstacles to success. This Review covers the art of repurposing existing drugs for antimicrobial purposes. We discuss enabling screening platforms for antimicrobial discovery and present encouraging findings of novel antimicrobial therapeutic strategies. Also covered are general advantages of repurposing over de novo drug development and challenges of the strategy, including scientific, intellectual property and regulatory issues.
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Affiliation(s)
- Maya A Farha
- Michael G. DeGroote Institute for Infectious Disease Research, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Eric D Brown
- Michael G. DeGroote Institute for Infectious Disease Research, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.
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15
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Ezzat A, Wu M, Li X, Kwoh CK. Computational Prediction of Drug-Target Interactions via Ensemble Learning. Methods Mol Biol 2019; 1903:239-254. [PMID: 30547446 DOI: 10.1007/978-1-4939-8955-3_14] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Therapeutic effects of drugs are mediated via interactions between them and their intended targets. As such, prediction of drug-target interactions is of great importance. Drug-target interaction prediction is especially relevant in the case of drug repositioning where attempts are made to repurpose old drugs for new indications. While experimental wet-lab techniques exist for predicting such interactions, they are tedious and time-consuming. On the other hand, computational methods also exist for predicting interactions, and they do so with reasonable accuracy. In addition, computational methods can help guide their wet-lab counterparts by recommending interactions for further validation. In this chapter, a computational method for predicting drug-target interactions is presented. Specifically, we describe a machine learning method that utilizes ensemble learning to perform predictions. We also mention details pertaining to the preparation of the data required for the prediction effort and demonstrate how to evaluate and improve prediction performance.
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Affiliation(s)
- Ali Ezzat
- Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
| | - Min Wu
- Data Analytics Department, Institute for Infocomm Research, A-Star, Singapore, Singapore
| | - Xiaoli Li
- Data Analytics Department, Institute for Infocomm Research, A-Star, Singapore, Singapore
| | - Chee-Keong Kwoh
- Division of Software and Information Systems, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
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16
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Abstract
The protocol below describes an in silico method for drug repositioning (drug repurposing). The data source is ClinicalTrials.gov , which contains about a quarter of a million clinical studies. Mining such rich and clean clinical summary data could be helpful to many health-related researches. Described here is a method that utilizes serious adverse event data to identify potential new uses of drugs and dietary supplements (repositioning).
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Affiliation(s)
- Eric Wen Su
- Advanced Analytics Hub, Eli Lilly and Company, Indianapolis, IN, USA.
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17
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Zhou P, Huang J, Ding W. Effect of Ling-Gui-Zhu-Gan decoction major components on the plasma protein binding of metoprolol using UPLC analysis coupled with ultrafiltration. RSC Adv 2018; 8:35981-35988. [PMID: 35558481 PMCID: PMC9088703 DOI: 10.1039/c8ra07153e] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 10/10/2018] [Indexed: 11/21/2022] Open
Abstract
Using traditional Chinese medicine formula Ling-Gui-Zhu-Gan decoction (LGZGD) plus selective β1-adrenergic receptor inhibitor metoprolol to treat arrhythmia of coronary heart disease can significantly improve efficiency with no adverse reactions. However, the effect of major components of LGZGD on the plasma protein binding of metoprolol is unclear. Firstly, this study aimed to computationally predict the molecular interactions between metoprolol, the major components of LGZGD, and bovine serum albumin (BSA). Secondly, the plasma protein binding of metoprolol combined with major components of LGZGD was investigated by UPLC analysis coupled with ultrafiltration. The MOE (2008.10) software package was used to investigate the molecular interactions among metoprolol, the major components of LGZGD, and BSA. Using in vitro experiments, BSA was separately spiked with a mixtures of metoprolol and the major components of LGZGD. The results showed that metoprolol interacted with BSA mainly through arene-arene interactions, as did cinnamic acid and liquiritin. However, the energy scores of cinnamic acid and liquiritin were lower than that of metoprolol. There were no interactions between metoprolol and the major components of LGZGD. Further studies in vitro showed that the presence of the major components of LGZGD did not change the plasma protein binding of metoprolol. We adopted molecular docking to predict the drug-herb plasma protein binding interactions of metoprolol and then used ultrafiltration to verify the docking results. There were no drug-herb interactions between metoprolol and LGZGD in BSA, which indicated that this combination therapy might be safe and feasible.
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Affiliation(s)
- Peng Zhou
- Department of Integrated Traditional Chinese and Western Medicine, Anhui University of Chinese Medicine Hefei 230012 People's Republic of China +86-0551-68129468 +86-0551-68129468
- Research Institute of Integrated Traditional Chinese and Western Medicine, Anhui Academy of Chinese Medicine Hefei 230012 People's Republic of China
- Key Laboratory of Chinese Herbal Compound Formula in Anhui Province Hefei 230012 People's Republic of China
| | - Jinling Huang
- Department of Integrated Traditional Chinese and Western Medicine, Anhui University of Chinese Medicine Hefei 230012 People's Republic of China +86-0551-68129468 +86-0551-68129468
- Research Institute of Integrated Traditional Chinese and Western Medicine, Anhui Academy of Chinese Medicine Hefei 230012 People's Republic of China
- Key Laboratory of Chinese Herbal Compound Formula in Anhui Province Hefei 230012 People's Republic of China
| | - Wanxue Ding
- Department of Integrated Traditional Chinese and Western Medicine, Anhui University of Chinese Medicine Hefei 230012 People's Republic of China +86-0551-68129468 +86-0551-68129468
- Research Institute of Integrated Traditional Chinese and Western Medicine, Anhui Academy of Chinese Medicine Hefei 230012 People's Republic of China
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18
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Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, Xie W, Rosen GL, Lengerich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM, Lavender CA, Turaga SC, Alexandari AM, Lu Z, Harris DJ, DeCaprio D, Qi Y, Kundaje A, Peng Y, Wiley LK, Segler MHS, Boca SM, Swamidass SJ, Huang A, Gitter A, Greene CS. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 2018; 15:20170387. [PMID: 29618526 PMCID: PMC5938574 DOI: 10.1098/rsif.2017.0387] [Citation(s) in RCA: 790] [Impact Index Per Article: 131.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 03/07/2018] [Indexed: 11/12/2022] Open
Abstract
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.
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Affiliation(s)
- Travers Ching
- Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Daniel S Himmelstein
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brett K Beaulieu-Jones
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexandr A Kalinin
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | - Gregory P Way
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Enrico Ferrero
- Computational Biology and Stats, Target Sciences, GlaxoSmithKline, Stevenage, UK
| | | | - Michael Zietz
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Wei Xie
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Gail L Rosen
- Ecological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Benjamin J Lengerich
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Johnny Israeli
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Jack Lanchantin
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Stephen Woloszynek
- Ecological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Avanti Shrikumar
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago, IL, USA
| | - Evan M Cofer
- Department of Computer Science, Trinity University, San Antonio, TX, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Christopher A Lavender
- Integrative Bioinformatics, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Srinivas C Turaga
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
| | - Amr M Alexandari
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - David J Harris
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA
| | | | - Yanjun Qi
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Anshul Kundaje
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Yifan Peng
- National Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Laura K Wiley
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Marwin H S Segler
- Institute of Organic Chemistry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Simina M Boca
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University in Saint Louis, St Louis, MO, USA
| | - Austin Huang
- Department of Medicine, Brown University, Providence, RI, USA
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
- Morgridge Institute for Research, Madison, WI, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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19
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Lee G, Plaksin J, Ramasamy R, Gold-von Simson G. Targeted drug discovery and development, from molecular signaling to the global market: an educational program at New York University, 5-year metrics. JOURNAL OF TRANSLATIONAL SCIENCE 2018; 4:1-9. [PMID: 29657854 PMCID: PMC5898375 DOI: 10.15761/jts.1000215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Drug discovery and development (DDD) is a collaborative, dynamic process of great interest to researchers, but an area where there is a lack of formal training. The Drug Development Educational Program (DDEP) at New York University was created in 2012 to stimulate an improved, multidisciplinary DDD workforce by educating early stage scientists as well as a variety of other like-minded students. The first course of the program emphasizes post-compounding aspects of DDD; the second course focuses on molecular signaling pathways. In five years, 196 students (candidates for PhD, MD, Master's degree, and post-doctoral MD/PhD) from different schools (Medicine, Biomedical Sciences, Dentistry, Engineering, Business, and Education) completed the course(s). Pre/post surveys demonstrate knowledge gain across all course topics. 26 students were granted career development awards (73% women, 23% underrepresented minorities). Some graduates of their respective degree-granting/post-doctoral programs embarked on DDD related careers. This program serves as a framework for other academic institutions to develop compatible programs designed to train a more informed DDD workforce.
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Affiliation(s)
- Gloria Lee
- T35 NIDDK Honors Trainee, Clinical and Translational Science Institute, New York University School of Medicine, NY, NY, USA
- State University of New York at Downstate College of Medicine, Brooklyn, NY, USA
| | - Joseph Plaksin
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Ravichandran Ramasamy
- Departments of Medicine, Biochemistry and Molecular Pharmacology, co-PI NIDDK R25, New York University School of Medicine, New York, NY, USA
| | - Gabrielle Gold-von Simson
- Department of Pediatrics, PI NIDDK R25, New York University School of Medicine, New York, NY, USA
- Clinical Translational Science Institute, New York University School of Medicine, New York, NY, USA
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20
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Ezzat A, Wu M, Li XL, Kwoh CK. Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform 2018; 20:1337-1357. [DOI: 10.1093/bib/bby002] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 12/21/2017] [Indexed: 01/18/2023] Open
Abstract
Abstract
Computational prediction of drug–target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.
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21
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Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors. PLoS Comput Biol 2017; 13:e1005678. [PMID: 28787438 PMCID: PMC5560747 DOI: 10.1371/journal.pcbi.1005678] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 08/17/2017] [Accepted: 07/11/2017] [Indexed: 01/09/2023] Open
Abstract
Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications.
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Su EW, Sanger TM. Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov. PeerJ 2017; 5:e3154. [PMID: 28348935 PMCID: PMC5366063 DOI: 10.7717/peerj.3154] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Accepted: 03/07/2017] [Indexed: 01/05/2023] Open
Abstract
Drug repositioning (i.e., drug repurposing) is the process of discovering new uses for marketed drugs. Historically, such discoveries were serendipitous. However, the rapid growth in electronic clinical data and text mining tools makes it feasible to systematically identify drugs with the potential to be repurposed. Described here is a novel method of drug repositioning by mining ClinicalTrials.gov. The text mining tools I2E (Linguamatics) and PolyAnalyst (Megaputer) were utilized. An I2E query extracts “Serious Adverse Events” (SAE) data from randomized trials in ClinicalTrials.gov. Through a statistical algorithm, a PolyAnalyst workflow ranks the drugs where the treatment arm has fewer predefined SAEs than the control arm, indicating that potentially the drug is reducing the level of SAE. Hypotheses could then be generated for the new use of these drugs based on the predefined SAE that is indicative of disease (for example, cancer).
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Affiliation(s)
- Eric Wen Su
- Advanced Analytics Hub, Eli Lilly and Company , Indianapolis , IN , United States of America
| | - Todd M Sanger
- Advanced Analytics Hub, Eli Lilly and Company , Indianapolis , IN , United States of America
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