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Yadav S, Koka SS, Jain P, Darwhekar GN, Vinchurkar K. Essential database resources for modern drug discovery. ADVANCES IN PHARMACOLOGY (SAN DIEGO, CALIF.) 2025; 103:81-100. [PMID: 40175056 DOI: 10.1016/bs.apha.2025.01.002] [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: 04/04/2025]
Abstract
In the fast-expanding field of drug discovery, researchers and pharmaceutical professionals require immediate access to critical database resources. This book chapter explains essential databases used in various stages of drug development, such as target selection, chemical screening, and clinical trial management. Databases including PubChem, ChEMBL, and Drug Bank, highlight their contributions to providing detailed chemical knowledge, biological activity data, and drug interaction profiles. Using powerful computer programs like AI and machine learning to combine data from these sources improves decision-making, speeds up time-to-market, and raises the chances of finding effective medicines. This book chapter signifies the importance of key databases, their uses, and how they integrate into the current drug discovery process.
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Affiliation(s)
- Saloni Yadav
- Acropolis Institute of Pharmaceutical Education & Research, Indore, Madhya Pradesh, India
| | - Sweta S Koka
- Acropolis Institute of Pharmaceutical Education & Research, Indore, Madhya Pradesh, India
| | - Priya Jain
- Oxford International College - Pharmacy, Indore, Madhya Pradesh, India
| | - G N Darwhekar
- Acropolis Institute of Pharmaceutical Education & Research, Indore, Madhya Pradesh, India
| | - Kuldeep Vinchurkar
- Department of Pharmaceutics, Sandip Institute of Pharmaceutical Sciences (SIPS), Affiliated To Savitribai Phule Pune University (SPPU, Pune), Nashik, Maharashtra, India.
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2
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Nada H, Meanwell NA, Gabr MT. Virtual screening: hope, hype, and the fine line in between. Expert Opin Drug Discov 2025; 20:145-162. [PMID: 39862145 PMCID: PMC11844436 DOI: 10.1080/17460441.2025.2458666] [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: 12/11/2024] [Revised: 01/17/2025] [Accepted: 01/22/2025] [Indexed: 01/27/2025]
Abstract
INTRODUCTION Technological advancements in virtual screening (VS) have rapidly accelerated its application in drug discovery, as reflected by the exponential growth in VS-related publications. However, a significant gap remains between the volume of computational predictions and their experimental validation. This discrepancy has led to a rise in the number of unverified 'claimed' hits which impedes the drug discovery efforts. AREAS COVERED This perspective examines the current VS landscape, highlighting essential practices and identifying critical challenges, limitations, and common pitfalls. Using case studies and practices, this perspective aims to highlight strategies that can effectively mitigate or overcome these challenges. Furthermore, the perspective explores common approaches for addressing pharmacodynamic and pharmacokinetic issues in optimizing VS hits. EXPERT OPINION VS has become a tried-and-true technique of drug discovery due to the rapid advances in computational methods and machine learning (ML) over the past two decades. Although each VS workflow varies depending on the chosen approach and methodology, integrated strategies that combine biological and in silico data have consistently yielded higher success rates. Moreover, the widespread adoption of ML has enhanced the integration of VS into the drug discovery pipeline. However, the absence of standardized evaluation criteria hinders the objective assessment of VS studies' success and the identification of optimal adoption methods.
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Affiliation(s)
- Hossam Nada
- Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY 10065, USA
| | - Nicholas A. Meanwell
- Baruch S. Blumberg Institute, Doylestown, PA, USA; School of Pharmacy, University of Michigan, Ann Arbor, MI, USA
- Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA
| | - Moustafa T. Gabr
- Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY 10065, USA
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Yang Q, Fan L, Hao E, Hou X, Deng J, Du Z, Xia Z. Construction of an explanatory model for predicting hepatotoxicity: a case study of the potentially hepatotoxic components of Gardenia jasminoides. Drug Chem Toxicol 2025; 48:107-119. [PMID: 38938098 DOI: 10.1080/01480545.2024.2364905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 05/17/2024] [Accepted: 06/01/2024] [Indexed: 06/29/2024]
Abstract
It is well-known that the hepatotoxicity of drugs can significantly influence their clinical use. Despite their effective therapeutic efficacy, many drugs are severely limited in clinical applications due to significant hepatotoxicity. In response, researchers have created several machine learning-based hepatotoxicity prediction models for use in drug discovery and development. Researchers aim to predict the potential hepatotoxicity of drugs to enhance their utility. However, current hepatotoxicity prediction models often suffer from being unverified, and they fail to capture the detailed toxicological structures of predicted hepatotoxic compounds. Using the 56 chemical constituents of Gardenia jasminoides as examples, we validated the trained hepatotoxicity prediction model through literature reviews, principal component analysis (PCA), and structural comparison methods. Ultimately, we successfully developed a model with strong predictive performance and conducted visual validation. Interestingly, we discovered that the predicted hepatotoxic chemical constituents of Gardenia possess both toxic and therapeutic effects, which are likely dose-dependent. This discovery greatly contributes to our understanding of the dual nature of drug-induced hepatotoxicity.
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Affiliation(s)
- Qi Yang
- School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
| | - Lili Fan
- School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, China
| | - Erwei Hao
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
| | - Xiaotao Hou
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
| | - Jiagang Deng
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
| | - Zhengcai Du
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Scientific Research Center of Traditional Chinese Medicine, Guangxi University of Chinese Medicine, Nanning, China
| | - Zhongshang Xia
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
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Chakraborty C, Bhattacharya M, Lee SS, Wen ZH, Lo YH. The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102295. [PMID: 39257717 PMCID: PMC11386122 DOI: 10.1016/j.omtn.2024.102295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha 756020, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do 24252, Republic of Korea
| | - Zhi-Hong Wen
- Department of Marine Biotechnology and Resources, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - Yi-Hao Lo
- Department of Family Medicine, Zuoying Armed Forces General Hospital, Kaohsiung 813204, Taiwan
- Shu-Zen Junior College of Medicine and Management, Kaohsiung 821004, Taiwan
- Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 804201, Taiwan
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Bhandari D, Adepu KK, Anishkin A, Kay CD, Young EE, Baumbauer KM, Ghosh A, Chintapalli SV. Unraveling Protein-Metabolite Interactions in Precision Nutrition: A Case Study of Blueberry-Derived Metabolites Using Advanced Computational Methods. Metabolites 2024; 14:430. [PMID: 39195526 DOI: 10.3390/metabo14080430] [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: 06/27/2024] [Revised: 07/30/2024] [Accepted: 08/02/2024] [Indexed: 08/29/2024] Open
Abstract
Metabolomics, the study of small-molecule metabolites within biological systems, has become a potent instrument for understanding cellular processes. Despite its profound insights into health, disease, and drug development, identifying the protein partners for metabolites, especially dietary phytochemicals, remains challenging. In the present study, we introduced an innovative in silico, structure-based target prediction approach to efficiently predict protein targets for metabolites. We analyzed 27 blood serum metabolites from nutrition intervention studies' blueberry-rich diets, known for their health benefits, yet with elusive mechanisms of action. Our findings reveal that blueberry-derived metabolites predominantly interact with Carbonic Anhydrase (CA) family proteins, which are crucial in acid-base regulation, respiration, fluid balance, bone metabolism, neurotransmission, and specific aspects of cellular metabolism. Molecular docking showed that these metabolites bind to a common pocket on CA proteins, with binding energies ranging from -5.0 kcal/mol to -9.0 kcal/mol. Further molecular dynamics (MD) simulations confirmed the stable binding of metabolites near the Zn binding site, consistent with known compound interactions. These results highlight the potential health benefits of blueberry metabolites through interaction with CA proteins.
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Affiliation(s)
| | - Kiran Kumar Adepu
- Arkansas Children's Nutrition Center, Little Rock, AR 72202, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Andriy Anishkin
- Department of Biology, University of Maryland, College Park, MD 20742, USA
| | - Colin D Kay
- Arkansas Children's Nutrition Center, Little Rock, AR 72202, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Erin E Young
- KU Medical Center, Department of Anesthesiology, Pain and Perioperative Medicine, University of Kansas School of Medicine, Kansas City, KS 66160, USA
| | - Kyle M Baumbauer
- KU Medical Center, Department of Anesthesiology, Pain and Perioperative Medicine, University of Kansas School of Medicine, Kansas City, KS 66160, USA
- KU Medical Center, Department of Cell Biology and Physiology, University of Kansas School of Medicine, Kansas City, KS 66160, USA
| | - Anuradha Ghosh
- Department of Environmental Health, Pittsburg State University, Pittsburg, KS 66762, USA
| | - Sree V Chintapalli
- Arkansas Children's Nutrition Center, Little Rock, AR 72202, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
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Wang Y, Tong C, Liu Q, Han R, Liu C. Intergrowth Zeolites, Synthesis, Characterization, and Catalysis. Chem Rev 2023; 123:11664-11721. [PMID: 37707958 DOI: 10.1021/acs.chemrev.3c00373] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
Microporous zeolites that can act as heterogeneous catalysts have continued to attract a great deal of academic and industrial interest, but current progress in their synthesis and application is restricted to single-phase zeolites, severely underestimating the potential of intergrowth frameworks. Compared with single-phase zeolites, intergrowth zeolites possess unique properties, such as different diffusion pathways and molecular confinement, or special crystalline pore environments for binding metal active sites. This review first focuses on the structural features and synthetic details of all the intergrowth zeolites, especially providing some insightful discussion of several potential frameworks. Subsequently, characterization methods for intergrowth zeolites are introduced, and highlighting fundamental features of these crystals. Then, the applications of intergrowth zeolites in several of the most active areas of catalysis are presented, including selective catalytic reduction of NOx by ammonia (NH3-SCR), methanol to olefins (MTO), petrochemicals and refining, fine chemicals production, and biomass conversion on Beta, and the relationship between structure and catalytic activity was profiled from the perspective of intergrowth grain boundary structure. Finally, the synthesis, characterization, and catalysis of intergrowth zeolites are summarized in a comprehensive discussion, and a brief outlook on the current challenges and future directions of intergrowth zeolites is indicated.
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Affiliation(s)
- Yanhua Wang
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Chengzheng Tong
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Qingling Liu
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Rui Han
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Caixia Liu
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
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Chen W, Liu X, Zhang S, Chen S. Artificial intelligence for drug discovery: Resources, methods, and applications. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 31:691-702. [PMID: 36923950 PMCID: PMC10009646 DOI: 10.1016/j.omtn.2023.02.019] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery.
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Affiliation(s)
- Wei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Xuesong Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Sanyin Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Shilin Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
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Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
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Biological databases and their application. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00021-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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10
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Abstract
Today there exists no public, freely downloadable, comprehensive database of all known chemical reactions and associated information. Such a database not only would serve chemical sciences and technologies around the world but also would enable the power of modern AI and machine learning methods to be unleashed on a host of fundamental problems. In time, this could lead to important scientific discoveries and economic developments for the benefit of humanity. While ideally such a repository ought to be created and maintained by an international consortium, in the near future, it may be easier to begin the process through governmental agencies such as the National Science Foundation or the National Institutes of Health. Working together, we could use a multipronged approach that could combine negotiations with commercial stakeholders, crowd-sourcing efforts, automated extraction methods, and legislative actions.
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Affiliation(s)
- Pierre Baldi
- Department of Computer Science, University of California, Irvine, Irvine, California 92697, United States
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11
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Web-Based Quantitative Structure-Activity Relationship Resources Facilitate Effective Drug Discovery. Top Curr Chem (Cham) 2021; 379:37. [PMID: 34554348 DOI: 10.1007/s41061-021-00349-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/17/2021] [Indexed: 12/28/2022]
Abstract
Traditional drug discovery effectively contributes to the treatment of many diseases but is limited by high costs and long cycles. Quantitative structure-activity relationship (QSAR) methods were introduced to evaluate the activity of compounds virtually, which saves the significant cost of determining the activities of the compounds experimentally. Over the past two decades, many web tools for QSAR modeling with various features have been developed to facilitate the usage of QSAR methods. These web tools significantly reduce the difficulty of using QSAR and indirectly promote drug discovery. However, there are few comprehensive summaries of these QSAR tools, and researchers may have difficulty determining which tool to use. Hence, we systematically surveyed the mainstream web tools for QSAR modeling. This work may guide researchers in choosing appropriate web tools for developing QSAR models, and may also help develop more bioinformatics tools based on these existing resources. For nonprofessionals, we also hope to make more people aware of QSAR methods and expand their use.
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Pinto GP, Hendrikse NM, Stourac J, Damborsky J, Bednar D. Virtual screening of potential anticancer drugs based on microbial products. Semin Cancer Biol 2021; 86:1207-1217. [PMID: 34298109 DOI: 10.1016/j.semcancer.2021.07.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 07/14/2021] [Accepted: 07/18/2021] [Indexed: 01/20/2023]
Abstract
The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNPOnco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.
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Affiliation(s)
- Gaspar P Pinto
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, Brno, 625 00, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, Brno, 656 91, Czech Republic
| | - Natalie M Hendrikse
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, Brno, 625 00, Czech Republic
| | - Jan Stourac
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, Brno, 625 00, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, Brno, 656 91, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, Brno, 625 00, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, Brno, 656 91, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, Brno, 625 00, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, Brno, 656 91, Czech Republic.
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Yao R, Ianevski A, Kainov D. Safe-in-Man Broad Spectrum Antiviral Agents. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1322:313-337. [PMID: 34258746 DOI: 10.1007/978-981-16-0267-2_12] [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: 02/06/2023]
Abstract
Emerging and re-emerging viral diseases occur with regularity within the human population. The conventional 'one drug, one virus' paradigm for antivirals does not adequately allow for proper preparedness in the face of unknown future epidemics. In addition, drug developers lack the financial incentives to work on antiviral drug discovery, with most pharmaceutical companies choosing to focus on more profitable disease areas. Safe-in-man broad spectrum antiviral agents (BSAAs) can help meet the need for antiviral development by already having passed phase I clinical trials, requiring less time and money to develop, and having the capacity to work against many viruses, allowing for a speedy response when unforeseen epidemics arise. In this chapter, we discuss the benefits of repurposing existing drugs as BSAAs, describe the major steps in safe-in-man BSAA drug development from discovery through clinical trials, and list several database resources that are useful tools for antiviral drug repositioning.
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Affiliation(s)
- Rouan Yao
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Aleksandr Ianevski
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Denis Kainov
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- Institute of Technology, University of Tartu, Tartu, Estonia.
- Institute for Molecule Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland.
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Kumar R, Harilal S, Gupta SV, Jose J, Thomas Parambi DG, Uddin MS, Shah MA, Mathew B. Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work. Eur J Med Chem 2019; 182:111602. [PMID: 31421629 PMCID: PMC7127402 DOI: 10.1016/j.ejmech.2019.111602] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 08/07/2019] [Accepted: 08/07/2019] [Indexed: 02/07/2023]
Abstract
Drug discovery and development are long and financially taxing processes. On an average it takes 12-15 years and costs 1.2 billion USD for successful drug discovery and approval for clinical use. Many lead molecules are not developed further and their potential is not tapped to the fullest due to lack of resources or time constraints. In order for a drug to be approved by FDA for clinical use, it must have excellent therapeutic potential in the desired area of target with minimal toxicities as supported by both pre-clinical and clinical studies. The targeted clinical evaluations fail to explore other potential therapeutic applications of the candidate drug. Drug repurposing or repositioning is a fast and relatively cheap alternative to the lengthy and expensive de novo drug discovery and development. Drug repositioning utilizes the already available clinical trials data for toxicity and adverse effects, at the same time explores the drug's therapeutic potential for a different disease. This review addresses recent developments and future scope of drug repositioning strategy.
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Affiliation(s)
- Rajesh Kumar
- Department of Pharmacy, Kerala University of Health Sciences, Thrissur, Kerala, India
| | - Seetha Harilal
- Department of Pharmacy, Kerala University of Health Sciences, Thrissur, Kerala, India
| | - Sheeba Varghese Gupta
- Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL, 33612, USA
| | - Jobin Jose
- Department of Pharmaceutics, NGSM Institute of Pharmaceutical Science, NITTE Deemed to be University, Manglore, 575018, India
| | - Della Grace Thomas Parambi
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka, Al Jouf, 2014, Saudi Arabia
| | - Md Sahab Uddin
- Department of Pharmacy, Southeast University, Dhaka, Bangladesh; Pharmakon Neuroscience Research Network, Dhaka, Bangladesh
| | - Muhammad Ajmal Shah
- Department of Pharmacogonosy, Faculty of Pharmaceutical Sciences, Government College University, Faisalabad, Pakistan
| | - Bijo Mathew
- Division of Drug Design and Medicinal Chemistry Research Lab, Department of Pharmaceutical Chemistry, Ahalia School of Pharmacy, Palakkad, 678557, Kerala, India.
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15
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Karaman B, Sippl W. Computational Drug Repurposing: Current Trends. Curr Med Chem 2019; 26:5389-5409. [DOI: 10.2174/0929867325666180530100332] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 05/06/2018] [Accepted: 05/14/2018] [Indexed: 01/31/2023]
Abstract
:
Biomedical discovery has been reshaped upon the exploding digitization of data
which can be retrieved from a number of sources, ranging from clinical pharmacology to
cheminformatics-driven databases. Now, supercomputing platforms and publicly available
resources such as biological, physicochemical, and clinical data, can all be integrated to construct
a detailed map of signaling pathways and drug mechanisms of action in relation to drug
candidates. Recent advancements in computer-aided data mining have facilitated analyses of
‘big data’ approaches and the discovery of new indications for pre-existing drugs has been
accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or
incorporating molecular structure information of drugs and protein targets with other kinds of
data derived from systems biology provide great potential to accelerate drug discovery and
improve the success of drug repurposing attempts. In this review, we highlight commonly
used computational drug repurposing strategies, including bioinformatics and cheminformatics
tools, to integrate large-scale data emerging from the systems biology, and consider both
the challenges and opportunities of using this approach. Moreover, we provide successful examples
and case studies that combined various in silico drug-repurposing strategies to predict
potential novel uses for known therapeutics.
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Affiliation(s)
- Berin Karaman
- Biruni University - Department of Pharmaceutical Chemistry, Istanbul, Turkey
| | - Wolfgang Sippl
- Martin-Luther University of Halle-Wittenberg - Institute of Pharmacy, Halle (Saale), Germany
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16
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Freeze JG, Kelly HR, Batista VS. Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists. Chem Rev 2019; 119:6595-6612. [PMID: 31059236 DOI: 10.1021/acs.chemrev.8b00759] [Citation(s) in RCA: 100] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In silico catalyst design is a grand challenge of chemistry. Traditional computational approaches have been limited by the need to compute properties for an intractably large number of possible catalysts. Recently, inverse design methods have emerged, starting from a desired property and optimizing a corresponding chemical structure. Techniques used for exploring chemical space include gradient-based optimization, alchemical transformations, and machine learning. Though the application of these methods to catalysis is in its early stages, further development will allow for robust computational catalyst design. This review provides an overview of the evolution of inverse design approaches and their relevance to catalysis. The strengths and limitations of existing techniques are highlighted, and suggestions for future research are provided.
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Affiliation(s)
- Jessica G Freeze
- Department of Chemistry , Yale University , New Haven , Connecticut 06520 , United States.,Energy Sciences Institute , Yale University , West Haven , Connecticut 06516 , United States
| | - H Ray Kelly
- Department of Chemistry , Yale University , New Haven , Connecticut 06520 , United States.,Energy Sciences Institute , Yale University , West Haven , Connecticut 06516 , United States
| | - Victor S Batista
- Energy Sciences Institute , Yale University , West Haven , Connecticut 06516 , United States.,Department of Chemistry , Yale University , P.O. Box 208107 , New Haven , Connecticut 06520 , United States
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17
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Schaub AJ, Moreno GO, Zhao S, Truong HV, Luo R, Tsai SC. Computational structural enzymology methodologies for the study and engineering of fatty acid synthases, polyketide synthases and nonribosomal peptide synthetases. Methods Enzymol 2019; 622:375-409. [PMID: 31155062 PMCID: PMC7197764 DOI: 10.1016/bs.mie.2019.03.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Various computational methodologies can be applied to enzymological studies on enzymes in the fatty acid, polyketide, and non-ribosomal peptide biosynthetic pathways. These multi-domain complexes are called fatty acid synthases, polyketide synthases, and non-ribosomal peptide synthetases. These mega-synthases biosynthesize chemically diverse and complex bioactive molecules, with the intermediates being chauffeured between catalytic partners via a carrier protein. Recent efforts have been made to engineer these systems to expand their product diversity. A major stumbling block is our poor understanding of the transient protein-protein and protein-substrate interactions between the carrier protein and its many catalytic partner domains and product intermediates. The innate reactivity of pathway intermediates in two major classes of polyketide synthases has frustrated our mechanistic understanding of these interactions during the biosynthesis of these natural products, ultimately impeding the engineering of these systems for the generation of engineered natural products. Computational techniques described in this chapter can aid data interpretation or used to generate testable models of these experimentally intractable transient interactions, thereby providing insight into key interactions that are difficult to capture otherwise, with the potential to expand the diversity in these systems.
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Affiliation(s)
- Andrew J Schaub
- Department of Chemistry, University of California, Irvine, CA, United States
| | - Gabriel O Moreno
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, United States
| | - Shiji Zhao
- Mathematical, Computational and Systems Biology Program, Center for Complex Biological Systems, University of California, Irvine, CA, United States
| | - Hau V Truong
- Department of Chemistry, University of California, Irvine, CA, United States
| | - Ray Luo
- Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, University of California, Irvine, CA, United States.
| | - Shiou-Chuan Tsai
- Department of Molecular Biology and Biochemistry, Chemistry, Pharmaceutical Sciences, University of California, Irvine, CA, United States.
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18
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Vachery J, Ranu S. RISC: Rapid Inverted-Index Based Search of Chemical Fingerprints. J Chem Inf Model 2019; 59:2702-2713. [PMID: 30908028 DOI: 10.1021/acs.jcim.9b00069] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The ability to search for a query molecule on massive molecular repositories is a fundamental task in chemoinformatics and drug-discovery. Chemical fingerprints are commonly used to characterize the structure and properties of molecules. Some fingerprints, particularly unfolded fingerprints, are often of extreme high dimension and sparse where only few features have a positive value. In this work, we propose a new searching algorithm, RISC, which exploits sparsity in high-dimensional fingerprints to derive effective pruning mechanisms and dramatically speed-up searching efficiency. RISC is robust enough to work on both binary and nonbinary chemical fingerprints. Extensive experiments on Range Queries and Top-k Queries across several molecular repositories demonstrate that at fingerprints of dimension 2048 and above, which is often the case with unfolded fingerprints, RISC is consistently faster than the state-of-the-art techniques. The source code of our implementation is available at http://www.cse.iitd.ac.in/~sayan/software.html .
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Affiliation(s)
- Jithin Vachery
- Department of Computer Science , IIT-Madras , Chennai , 600036 , India
| | - Sayan Ranu
- Department of Computer Science , IIT-Delhi , New Delhi , 110016 , India
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19
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Gurubasavaraj PM, Charantimath JS. Recent Advances in Azole Based Scaffolds as Anticandidal Agents. LETT DRUG DES DISCOV 2019. [DOI: 10.2174/1570180815666180917125916] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Aim:The present review aims to explore the development of novel antifungal agents, such as pharmacology, pharmacokinetics, spectrum of activity, safety, toxicity and other aspects that involve drug-drug interactions of the azole antifungal agents.Introduction:Fungal infections in critically ill and immune-compromised patients are increasing at alarming rates, caused mainly by Candida albicans an opportunistic fungus. Despite antifungal annihilators like amphotericin B, azoles and caspofungin, these infections are enormously increasing. The unconventional increase in such patients is a challenging task for the management of antifungal infections especially Candidiasis. Moreover, problem of toxicity associated with antifungal drugs on hosts and rise of drug-resistance in primary and opportunistic fungal pathogens has obstructed the success of antifungal therapy.Conclusion:Hence, to conflict these problems new antifungal agents with advanced efficacy, new formulations of drug delivery and novel compounds which can interact with fungal virulence are developed and used to treat antifungal infections.
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20
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Xue H, Li J, Xie H, Wang Y. Review of Drug Repositioning Approaches and Resources. Int J Biol Sci 2018; 14:1232-1244. [PMID: 30123072 PMCID: PMC6097480 DOI: 10.7150/ijbs.24612] [Citation(s) in RCA: 357] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Accepted: 06/12/2018] [Indexed: 12/23/2022] Open
Abstract
Drug discovery is a time-consuming, high-investment, and high-risk process in traditional drug development. Drug repositioning has become a popular strategy in recent years. Different from traditional drug development strategies, the strategy is efficient, economical and riskless. There are usually three kinds of approaches: computational approaches, biological experimental approaches, and mixed approaches, all of which are widely used in drug repositioning. In this paper, we reviewed computational approaches and highlighted their characteristics to provide references for researchers to develop more powerful approaches. At the same time, the important findings obtained using these approaches are listed. Furthermore, we summarized 76 important resources about drug repositioning. Finally, challenges and opportunities in drug repositioning are discussed from multiple perspectives, including technology, commercial models, patents and investment.
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Affiliation(s)
| | - Jie Li
- ✉ Corresponding author: Jie Li,
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21
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Passeri GI, Trisciuzzi D, Alberga D, Siragusa L, Leonetti F, Mangiatordi GF, Nicolotti O. Strategies of Virtual Screening in Medicinal Chemistry. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijqspr.2018010108] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.
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Affiliation(s)
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Lydia Siragusa
- Molecular Discovery Ltd., Pinner, Middlesex, London, United Kingdom
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Giuseppe F. Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
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22
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Arbuckle C, Greenberg M, Bergh A, German R, Sirago N, Linstead E. T-Time: A data repository of T cell and calcium release-activated calcium channel activation imagery. BMC Res Notes 2017; 10:408. [PMID: 28807036 PMCID: PMC5557281 DOI: 10.1186/s13104-017-2739-x] [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: 12/19/2016] [Accepted: 08/08/2017] [Indexed: 11/10/2022] Open
Abstract
Background A fundamental understanding of live-cell dynamics is necessary in order to advance scientific techniques and personalized medicine. For this understanding to be possible, image processing techniques, probes, tracking algorithms and many other methodologies must be improved. Currently there are no large open-source datasets containing live-cell imaging to act as a standard for the community. As a result, researchers cannot evaluate their methodologies on an independent benchmark or leverage such a dataset to formulate scientific questions. Findings Here we present T-Time, the largest free and publicly available data set of T cell phase contrast imagery designed with the intention of furthering live-cell dynamics research. T-Time consists of over 40 GB of imagery data, and includes annotations derived from these images using a custom T cell identification and tracking algorithm. The data set contains 71 time-lapse sequences containing T cell movement and calcium release activated calcium channel activation, along with 50 time-lapse sequences of T cell activation and T reg interactions. The database includes a user-friendly web interface, summary information on the time-lapse images, and a mechanism for users to download tailored image datasets for their own research. T-Time is freely available on the web at http://ttime.mlatlab.org. Conclusions T-Time is a novel data set of T cell images and associated metadata. It allows users to study T cell interaction and activation.
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Affiliation(s)
- Cody Arbuckle
- Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA, 92866, USA.,Anivive Life Sciences Incorporated, Long Beach, CA, 90808, USA
| | - Milton Greenberg
- Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA, 92866, USA.,Department of Physiology and Biophysics, University of California, Irvine, CA, 92697, USA
| | - Adrienne Bergh
- Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA, 92866, USA
| | - Rene German
- Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA, 92866, USA
| | - Nick Sirago
- Anivive Life Sciences Incorporated, Long Beach, CA, 90808, USA
| | - Erik Linstead
- Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA, 92866, USA.
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23
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O'Hagan S, Kell DB. MetMaxStruct: A Tversky-Similarity-Based Strategy for Analysing the (Sub)Structural Similarities of Drugs and Endogenous Metabolites. Front Pharmacol 2016; 7:266. [PMID: 27597830 PMCID: PMC4992690 DOI: 10.3389/fphar.2016.00266] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 08/08/2016] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Previous studies compared the molecular similarity of marketed drugs and endogenous human metabolites (endogenites), using a series of fingerprint-type encodings, variously ranked and clustered using the Tanimoto (Jaccard) similarity coefficient (TS). Because this gives equal weight to all parts of the encoding (thence to different substructures in the molecule) it may not be optimal, since in many cases not all parts of the molecule will bind to their macromolecular targets. Unsupervised methods cannot alone uncover this. We here explore the kinds of differences that may be observed when the TS is replaced-in a manner more equivalent to semi-supervised learning-by variants of the asymmetric Tversky (TV) similarity, that includes α and β parameters. RESULTS Dramatic differences are observed in (i) the drug-endogenite similarity heatmaps, (ii) the cumulative "greatest similarity" curves, and (iii) the fraction of drugs with a Tversky similarity to a metabolite exceeding a given value when the Tversky α and β parameters are varied from their Tanimoto values. The same is true when the sum of the α and β parameters is varied. A clear trend toward increased endogenite-likeness of marketed drugs is observed when α or β adopt values nearer the extremes of their range, and when their sum is smaller. The kinds of molecules exhibiting the greatest similarity to two interrogating drug molecules (chlorpromazine and clozapine) also vary in both nature and the values of their similarity as α and β are varied. The same is true for the converse, when drugs are interrogated with an endogenite. The fraction of drugs with a Tversky similarity to a molecule in a library exceeding a given value depends on the contents of that library, and α and β may be "tuned" accordingly, in a semi-supervised manner. At some values of α and β drug discovery library candidates or natural products can "look" much more like (i.e., have a numerical similarity much closer to) drugs than do even endogenites. CONCLUSIONS Overall, the Tversky similarity metrics provide a more useful range of examples of molecular similarity than does the simpler Tanimoto similarity, and help to draw attention to molecular similarities that would not be recognized if Tanimoto alone were used. Hence, the Tversky similarity metrics are likely to be of significant value in many general problems in cheminformatics.
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Affiliation(s)
- Steve O'Hagan
- School of Chemistry, The University of ManchesterManchester, UK
- The Manchester Institute of Biotechnology, The University of ManchesterManchester, UK
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals, The University of ManchesterManchester, UK
| | - Douglas B. Kell
- School of Chemistry, The University of ManchesterManchester, UK
- The Manchester Institute of Biotechnology, The University of ManchesterManchester, UK
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals, The University of ManchesterManchester, UK
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24
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Abstract
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Despite a large and
rapidly growing body of small molecule bioactivity
screens available in the public domain, systematic leverage of the
data to assess target druggability and compound selectivity has been
confounded by a lack of suitable cross-target analysis software. We
have developed bioassayR, a computational tool that enables simultaneous
analysis of thousands of bioassay experiments performed over a diverse
set of compounds and biological targets. Unique features include support
for large-scale cross-target analyses of both public and custom bioassays,
generation of high throughput screening fingerprints (HTSFPs), and
an optional preloaded database that provides access to a substantial
portion of publicly available bioactivity data. bioassayR is implemented
as an open-source R/Bioconductor package available from https://bioconductor.org/packages/bioassayR/.
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Affiliation(s)
- Tyler William H Backman
- Institute for Integrative Genome Biology, University of California, Riverside , Riverside, California 92521, United States
| | - Thomas Girke
- Institute for Integrative Genome Biology, University of California, Riverside , Riverside, California 92521, United States
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25
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Abuhammad A, Taha M. Innovative computer-aided methods for the discovery of new kinase ligands. Future Med Chem 2016; 8:509-526. [PMID: 27105126 DOI: 10.4155/fmc-2015-0003] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 02/02/2016] [Indexed: 07/10/2024] Open
Abstract
Recent evidence points to significant roles played by protein kinases in cell signaling and cellular proliferation. Faulty protein kinases are involved in cancer, diabetes and chronic inflammation. Efforts are continuously carried out to discover new inhibitors for selected protein kinases. In this review, we discuss two new computer-aided methodologies we developed to mine virtual databases for new bioactive compounds. One method is ligand-based exploration of the pharmacophoric space of inhibitors of any particular biotarget followed by quantitative structure-activity relationship-based selection of the best pharmacophore(s). The second approach is structure-based assuming that potent ligands come into contact with binding site spots distinct from those contacted by weakly potent ligands. Both approaches yield pharmacophores useful as 3D search queries for the discovery of new bioactive (kinase) inhibitors.
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Affiliation(s)
- Areej Abuhammad
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, 11942, Amman, Jordan
| | - Mutasem Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, 11942, Amman, Jordan
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26
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Accurate and efficient target prediction using a potency-sensitive influence-relevance voter. J Cheminform 2015; 7:63. [PMID: 26719774 PMCID: PMC4696267 DOI: 10.1186/s13321-015-0110-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 12/02/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A number of algorithms have been proposed to predict the biological targets of diverse molecules. Some are structure-based, but the most common are ligand-based and use chemical fingerprints and the notion of chemical similarity. These methods tend to be computationally faster than others, making them particularly attractive tools as the amount of available data grows. RESULTS Using a ChEMBL-derived database covering 490,760 molecule-protein interactions and 3236 protein targets, we conduct a large-scale assessment of the performance of several target-prediction algorithms at predicting drug-target activity. We assess algorithm performance using three validation procedures: standard tenfold cross-validation, tenfold cross-validation in a simulated screen that includes random inactive molecules, and validation on an external test set composed of molecules not present in our database. CONCLUSIONS We present two improvements over current practice. First, using a modified version of the influence-relevance voter (IRV), we show that using molecule potency data can improve target prediction. Second, we demonstrate that random inactive molecules added during training can boost the accuracy of several algorithms in realistic target-prediction experiments. Our potency-sensitive version of the IRV (PS-IRV) obtains the best results on large test sets in most of the experiments. Models and software are publicly accessible through the chemoinformatics portal at http://chemdb.ics.uci.edu/.
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27
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Deller MC, Rupp B. Models of protein-ligand crystal structures: trust, but verify. J Comput Aided Mol Des 2015; 29:817-36. [PMID: 25665575 PMCID: PMC4531100 DOI: 10.1007/s10822-015-9833-8] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Accepted: 01/29/2015] [Indexed: 11/26/2022]
Abstract
X-ray crystallography provides the most accurate models of protein-ligand structures. These models serve as the foundation of many computational methods including structure prediction, molecular modelling, and structure-based drug design. The success of these computational methods ultimately depends on the quality of the underlying protein-ligand models. X-ray crystallography offers the unparalleled advantage of a clear mathematical formalism relating the experimental data to the protein-ligand model. In the case of X-ray crystallography, the primary experimental evidence is the electron density of the molecules forming the crystal. The first step in the generation of an accurate and precise crystallographic model is the interpretation of the electron density of the crystal, typically carried out by construction of an atomic model. The atomic model must then be validated for fit to the experimental electron density and also for agreement with prior expectations of stereochemistry. Stringent validation of protein-ligand models has become possible as a result of the mandatory deposition of primary diffraction data, and many computational tools are now available to aid in the validation process. Validation of protein-ligand complexes has revealed some instances of overenthusiastic interpretation of ligand density. Fundamental concepts and metrics of protein-ligand quality validation are discussed and we highlight software tools to assist in this process. It is essential that end users select high quality protein-ligand models for their computational and biological studies, and we provide an overview of how this can be achieved.
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Affiliation(s)
- Marc C Deller
- The Joint Center for Structural Genomics, San Diego, CA, USA
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Bernhard Rupp
- , k.-k. Hofkristallamt 991 Audrey Place, Vista, CA, 92084, USA.
- Department of Genetic Epidemiology, Medical University of Innsbruck, Schöpfstr. 41, 6020, Innsbruck, Austria.
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28
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Cerqueira NMFSA, Gesto D, Oliveira EF, Santos-Martins D, Brás NF, Sousa SF, Fernandes PA, Ramos MJ. Receptor-based virtual screening protocol for drug discovery. Arch Biochem Biophys 2015; 582:56-67. [PMID: 26045247 DOI: 10.1016/j.abb.2015.05.011] [Citation(s) in RCA: 86] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Revised: 05/26/2015] [Accepted: 05/27/2015] [Indexed: 12/12/2022]
Abstract
Computational aided drug design (CADD) is presently a key component in the process of drug discovery and development as it offers great promise to drastically reduce cost and time requirements. In the pharmaceutical arena, virtual screening is normally regarded as the top CADD tool to screen large libraries of chemical structures and reduce them to a key set of likely drug candidates regarding a specific protein target. This chapter provides a comprehensive overview of the receptor-based virtual screening process and of its importance in the present drug discovery and development paradigm. Following a focused contextualization on the subject, the main stages of a virtual screening campaign, including its strengths and limitations, are the subject of particular attention in this review. In all of these stages special consideration will be given to practical issues that are normally the Achilles heel of the virtual screening process.
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Affiliation(s)
- Nuno M F S A Cerqueira
- UCIBIO, REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Diana Gesto
- UCIBIO, REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Eduardo F Oliveira
- UCIBIO, REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Diogo Santos-Martins
- UCIBIO, REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Natércia F Brás
- UCIBIO, REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Sérgio F Sousa
- UCIBIO, REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Pedro A Fernandes
- UCIBIO, REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Maria J Ramos
- UCIBIO, REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal.
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Wong SSW, Samaranayake LP, Seneviratne CJ. In pursuit of the ideal antifungal agent for Candida infections: high-throughput screening of small molecules. Drug Discov Today 2014; 19:1721-1730. [PMID: 24952336 DOI: 10.1016/j.drudis.2014.06.009] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Revised: 05/23/2014] [Accepted: 06/12/2014] [Indexed: 01/22/2023]
Abstract
Candida infections have created a great burden on the public healthcare sector. The situation is worsened by recent epidemiological changes. Furthermore, the current arsenal of antifungal agents is limited and associated with undesirable drawbacks. Therefore, new antifungal agents that surpass the existing ones are urgently needed. High-throughput screening of small molecule libraries enables rapid hit identification and, possibly, increases hit rate. Moreover, the identified hits could be associated with unrecognized or multiple drug targets, which would provide novel insights into the biological processes of the pathogen. Hence, it is proposed that high-throughput screening of small molecules is particularly important in the pursuit of the ideal antifungal agents for Candida infections.
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Affiliation(s)
- Sarah S W Wong
- Faculty of Dentistry, University of Hong Kong, Hong Kong
| | | | - Chaminda J Seneviratne
- Faculty of Dentistry, University of Hong Kong, Hong Kong; Oral Sciences, Faculty of Dentistry, National University of Singapore, Singapore.
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30
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Kotera M, Goto S, Kanehisa M. Predictive genomic and metabolomic analysis for the standardization of enzyme data. ACTA ACUST UNITED AC 2014. [DOI: 10.1016/j.pisc.2014.02.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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31
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Galgonek J, Vondrášek J. On InChI and evaluating the quality of cross-reference links. J Cheminform 2014; 6:15. [PMID: 24742140 PMCID: PMC4005828 DOI: 10.1186/1758-2946-6-15] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2013] [Accepted: 03/25/2014] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND There are many databases of small molecules focused on different aspects of research and its applications. Some tasks may require integration of information from various databases. However, determining which entries from different databases represent the same compound is not straightforward. Integration can be based, for example, on automatically generated cross-reference links between entries. Another approach is to use the manually curated links stored directly in databases. This study employs well-established InChI identifiers to measure the consistency and completeness of the manually curated links by comparing them with the automatically generated ones. RESULTS We used two different tools to generate InChI identifiers and observed some ambiguities in their outputs. In part, these ambiguities were caused by indistinctness in interpretation of the structural data used. InChI identifiers were used successfully to find duplicate entries in databases. We found that the InChI inconsistencies in the manually curated links are very high (28.85% in the worst case). Even using a weaker definition of consistency, the measured values were very high in general. The completeness of the manually curated links was also very poor (only 93.8% in the best case) compared with that of the automatically generated links. CONCLUSIONS We observed several problems with the InChI tools and the files used as their inputs. There are large gaps in the consistency and completeness of manually curated links if they are measured using InChI identifiers. However, inconsistency can be caused both by errors in manually curated links and the inherent limitations of the InChI method.
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Affiliation(s)
- Jakub Galgonek
- Institute of Organic Chemistry and Biochemistry, Academy of Sciences of the Czech Republic, Flemingovo nam. 2, 166 10 Prague 6, Czech Republic
| | - Jiří Vondrášek
- Institute of Organic Chemistry and Biochemistry, Academy of Sciences of the Czech Republic, Flemingovo nam. 2, 166 10 Prague 6, Czech Republic
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32
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Ginex T, Spyrakis F, Cozzini P. FADB: a food additive molecular database forin silicoscreening in food toxicology. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2014; 31:792-8. [DOI: 10.1080/19440049.2014.888784] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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33
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Recent Advances in the Open Access Cheminformatics Toolkits, Software Tools, Workflow Environments, and Databases. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2014. [DOI: 10.1007/7653_2014_35] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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34
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Abstract
Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.
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Affiliation(s)
- Gregory Sliwoski
- Jr., Center for Structural Biology, 465 21st Ave South, BIOSCI/MRBIII, Room 5144A, Nashville, TN 37232-8725.
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35
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Mycolic acids: structures, biosynthesis, and beyond. ACTA ACUST UNITED AC 2013; 21:67-85. [PMID: 24374164 DOI: 10.1016/j.chembiol.2013.11.011] [Citation(s) in RCA: 403] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2013] [Revised: 11/04/2013] [Accepted: 11/27/2013] [Indexed: 11/24/2022]
Abstract
Mycolic acids are major and specific lipid components of the mycobacterial cell envelope and are essential for the survival of members of the genus Mycobacterium that contains the causative agents of both tuberculosis and leprosy. In the alarming context of the emergence of multidrug-resistant, extremely drug-resistant, and totally drug-resistant tuberculosis, understanding the biosynthesis of these critical determinants of the mycobacterial physiology is an important goal to achieve, because it may open an avenue for the development of novel antimycobacterial agents. This review focuses on the chemistry, structures, and known inhibitors of mycolic acids and describes progress in deciphering the mycolic acid biosynthetic pathway. The functional and key biological roles of these molecules are also discussed, providing a historical perspective in this dynamic area.
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36
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Tabei Y, Yamanishi Y. Scalable prediction of compound-protein interactions using minwise hashing. BMC SYSTEMS BIOLOGY 2013; 7 Suppl 6:S3. [PMID: 24564870 PMCID: PMC4029277 DOI: 10.1186/1752-0509-7-s6-s3] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The identification of compound-protein interactions plays key roles in the drug development toward discovery of new drug leads and new therapeutic protein targets. There is therefore a strong incentive to develop new efficient methods for predicting compound-protein interactions on a genome-wide scale. In this paper we develop a novel chemogenomic method to make a scalable prediction of compound-protein interactions from heterogeneous biological data using minwise hashing. The proposed method mainly consists of two steps: 1) construction of new compact fingerprints for compound-protein pairs by an improved minwise hashing algorithm, and 2) application of a sparsity-induced classifier to the compact fingerprints. We test the proposed method on its ability to make a large-scale prediction of compound-protein interactions from compound substructure fingerprints and protein domain fingerprints, and show superior performance of the proposed method compared with the previous chemogenomic methods in terms of prediction accuracy, computational efficiency, and interpretability of the predictive model. All the previously developed methods are not computationally feasible for the full dataset consisting of about 200 millions of compound-protein pairs. The proposed method is expected to be useful for virtual screening of a huge number of compounds against many protein targets.
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37
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Sadowski P, Baldi P. Small-molecule 3D structure prediction using open crystallography data. J Chem Inf Model 2013; 53:3127-30. [PMID: 24261562 DOI: 10.1021/ci4005282] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Predicting the 3D structures of small molecules is a common problem in chemoinformatics. Even the best methods are inaccurate for complex molecules, and there is a large gap in accuracy between proprietary and free algorithms. Previous work presented COSMOS, a novel data-driven algorithm that uses knowledge of known structures from the Cambridge Structural Database and demonstrates performance that was competitive with proprietary algorithms. However, dependence on the Cambridge Structural Database prevented its widespread use. Here, we present an updated version of the COSMOS structure predictor, complete with a free structure library derived from open data sources. We demonstrate that COSMOS performs better than other freely available methods, with a mean RMSD of 1.16 and 1.68 Å for organic and metal-organic structures, respectively, and a mean prediction time of 60 ms per molecule. This is a 17% and 20% reduction, respectively, in RMSD compared to the free predictor provided by Open Babel, and it is 10 times faster. The ChemDB Web portal provides a COSMOS prediction Web server, as well as downloadable copies of the COSMOS executable and library of molecular substructures.
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Affiliation(s)
- Peter Sadowski
- Institute for Genomics and Bioinformatics , University of California, Irvine , Irvine, California 92697, United States
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38
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Abstract
The Transporter Classification Database (TCDB; http://www.tcdb.org) serves as a common reference point for transport protein research. The database contains more than 10 000 non-redundant proteins that represent all currently recognized families of transmembrane molecular transport systems. Proteins in TCDB are organized in a five level hierarchical system, where the first two levels are the class and subclass, the second two are the family and subfamily, and the last one is the transport system. Superfamilies that contain multiple families are included as hyperlinks to the five tier TC hierarchy. TCDB includes proteins from all types of living organisms and is the only transporter classification system that is both universal and recognized by the International Union of Biochemistry and Molecular Biology. It has been expanded by manual curation, contains extensive text descriptions providing structural, functional, mechanistic and evolutionary information, is supported by unique software and is interconnected to many other relevant databases. TCDB is of increasing usefulness to the international scientific community and can serve as a model for the expansion of database technologies. This manuscript describes an update of the database descriptions previously featured in NAR database issues.
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Affiliation(s)
- Milton H Saier
- Department of Molecular Biology, University of California at San Diego, La Jolla, CA 92093-0116, USA
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39
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Ellabaan M, Ong Y, Handoko S, Kwoh C, Man H. Discovering Unique, Low-Energy Transition States Using Evolutionary Molecular Memetic Computing. IEEE COMPUT INTELL M 2013. [DOI: 10.1109/mci.2013.2264252] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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40
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Hähnke V, Rupp M, Hartmann AK, Schneider G. Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical Similarity. Mol Inform 2013; 32:625-46. [PMID: 27481770 DOI: 10.1002/minf.201300021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2013] [Accepted: 04/19/2013] [Indexed: 11/06/2022]
Abstract
Previously, we proposed a ligand-based virtual screening technique (PhAST) based on global alignment of linearized interaction patterns. Here, we applied techniques developed for similarity assessment in local sequence alignments to our method resulting in p-values for chemical similarity. We compared two sampling strategies, a simple sampling strategy and a Markov Chain Monte Carlo (MCMC) method, and investigated the similarity of sampled distributions to Gaussian, Gumbel, modified Gumbel, and Gamma distributions. The Gumbel distribution with a Gaussian correction term was identified as the most similar to the observed empirical distributions. These techniques were applied in retrospective screenings on a drug-like dataset. Obtained p-values were adjusted to the size of the screening library with four different methods. Evaluation of E-value thresholds corroborated the Bonferroni correction as a preferred means to identify significant chemical similarity with PhAST. An online version of PhAST with significance estimation is available at http://modlab-cadd.ethz.ch/.
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Affiliation(s)
- Volker Hähnke
- Eidgenössische Technische Hochschule (ETH), Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Wolfgang-Pauli-Str. 10, 8093 Zürich, Switzerland phone: +1 (202)436-5989.
| | - Matthias Rupp
- Eidgenössische Technische Hochschule (ETH), Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Wolfgang-Pauli-Str. 10, 8093 Zürich, Switzerland phone: +1 (202)436-5989
| | - Alexander K Hartmann
- Universität Oldenburg, Computational Theoretical Physics, Institut für Physik, Carl-von-Ossietzky Strasse 9-11, 26111 Oldenburg, Germany
| | - Gisbert Schneider
- Eidgenössische Technische Hochschule (ETH), Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Wolfgang-Pauli-Str. 10, 8093 Zürich, Switzerland phone: +1 (202)436-5989
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41
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Kristensen TG, Nielsen J, Pedersen CNS. Methods for Similarity-based Virtual Screening. Comput Struct Biotechnol J 2013; 5:e201302009. [PMID: 24688702 PMCID: PMC3962175 DOI: 10.5936/csbj.201302009] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2012] [Revised: 01/30/2013] [Accepted: 02/08/2013] [Indexed: 11/22/2022] Open
Abstract
Developing new medical drugs is expensive. Among the first steps is a screening process, in which molecules in existing chemical libraries are tested for activity against a given target. This requires a lot of resources and manpower. Therefore it has become common to perform a virtual screening, where computers are used for predicting the activity of very large libraries of molecules, to identify the most promising leads for further laboratory experiments. Since computer simulations generally require fewer resources than physical experimentation this can lower the cost of medical and biological research significantly. In this paper we review practically fast algorithms for screening databases of molecules in order to find molecules that are sufficiently similar to a query molecule.
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Affiliation(s)
- Thomas G Kristensen
- Bioinformatics Research Center, Aarhus University, C. F. Møllers Allé 8, DK- 8000 Aarhus C, Denmark ; Now employed by Trifork Gmbh
| | - Jesper Nielsen
- Bioinformatics Research Center, Aarhus University, C. F. Møllers Allé 8, DK- 8000 Aarhus C, Denmark ; Now employed by Google Inc
| | - Christian N S Pedersen
- Bioinformatics Research Center, Aarhus University, C. F. Møllers Allé 8, DK- 8000 Aarhus C, Denmark
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42
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Hall AS, Shan Y, Lushington G, Visvanathan M. An overview of computational life science databases & exchange formats of relevance to chemical biology research. Comb Chem High Throughput Screen 2013; 16:189-98. [PMID: 22934944 PMCID: PMC4782780 DOI: 10.2174/1386207311316030004] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2012] [Revised: 06/19/2012] [Accepted: 07/14/2012] [Indexed: 11/22/2022]
Abstract
Databases and exchange formats describing biological entities such as chemicals and proteins, along with their relationships, are a critical component of research in life sciences disciplines, including chemical biology wherein small information about small molecule properties converges with cellular and molecular biology. Databases for storing biological entities are growing not only in size, but also in type, with many similarities between them and often subtle differences. The data formats available to describe and exchange these entities are numerous as well. In general, each format is optimized for a particular purpose or database, and hence some understanding of these formats is required when choosing one for research purposes. This paper reviews a selection of different databases and data formats with the goal of summarizing their purposes, features, and limitations. Databases are reviewed under the categories of 1) protein interactions, 2) metabolic pathways, 3) chemical interactions, and 4) drug discovery. Representation formats will be discussed according to those describing chemical structures, and those describing genomic/proteomic entities.
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Affiliation(s)
- Aaron Smalter Hall
- Bioinformatics Core Facility, University of Kansas, Lawrence, Kansas, 66047, United States
| | - Yunfeng Shan
- Bioinformatics Core Facility, University of Kansas, Lawrence, Kansas, 66047, United States
| | - Gerald Lushington
- Bioinformatics Core Facility, University of Kansas, Lawrence, Kansas, 66047, United States
| | - Mahesh Visvanathan
- Bioinformatics Core Facility, University of Kansas, Lawrence, Kansas, 66047, United States
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43
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Yang CH, Cheng YH, Chuang LY, Chang HW. Drug-SNPing: an integrated drug-based, protein interaction-based tagSNP-based pharmacogenomics platform for SNP genotyping. Bioinformatics 2013; 29:758-64. [DOI: 10.1093/bioinformatics/btt037] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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44
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Danishuddin M, Kaushal L, Hassan Baig M, Khan AU. AMDD: antimicrobial drug database. GENOMICS PROTEOMICS & BIOINFORMATICS 2013; 10:360-3. [PMID: 23317704 PMCID: PMC5054706 DOI: 10.1016/j.gpb.2012.04.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2012] [Revised: 03/21/2012] [Accepted: 04/05/2012] [Indexed: 11/25/2022]
Abstract
Drug resistance is one of the major concerns for antimicrobial chemotherapy against any particular target. Knowledge of the primary structure of antimicrobial agents and their activities is essential for rational drug design. Thus, we developed a comprehensive database, anti microbial drug database (AMDD), of known synthetic antibacterial and antifungal compounds that were extracted from the available literature and other chemical databases, e.g., PubChem, PubChem BioAssay and ZINC, etc. The current version of AMDD contains ~2900 antibacterial and ~1200 antifungal compounds. The molecules are annotated with properties such as description, target, format, bioassay, molecular weight, hydrogen bond donor, hydrogen bond acceptor and rotatable bond. The availability of these antimicrobial agents on common platform not only provides useful information but also facilitate the virtual screening process, thus saving time and overcoming difficulties in selecting specific type of inhibitors for the specific targets. AMDD may provide a more effective and efficient way of accessing antimicrobial compounds based on their properties along with the links to their structure and bioassay. All the compounds are freely available at the advanced web-based search interface http://www.amddatabase.info.
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Affiliation(s)
- Mohd Danishuddin
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh 202002, India
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45
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Nikolov N, Pavlov T, Niemelä JR, Mekenyan O. Accessing and using chemical databases. Methods Mol Biol 2013; 930:29-52. [PMID: 23086836 DOI: 10.1007/978-1-62703-059-5_2] [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: 06/01/2023]
Abstract
Computer-based representation of chemicals makes it possible to organize data in chemical databases-collections of chemical structures and associated properties. Databases are widely used wherever efficient processing of chemical information is needed, including search, storage, retrieval, and dissemination. Structure and functionality of chemical databases are considered. The typical kinds of information found in a chemical database are considered-identification, structural, and associated data. Functionality of chemical databases is presented, with examples of search and access types. More details are included about the OASIS database and platform and the Danish (Q)SAR Database online. Various types of chemical database resources are discussed, together with a list of examples.
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Affiliation(s)
- Nikolai Nikolov
- National Food Institute Technical University of Denmark, Soeborg, Denmark
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46
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Reymond JL, Awale M. Exploring chemical space for drug discovery using the chemical universe database. ACS Chem Neurosci 2012; 3:649-57. [PMID: 23019491 DOI: 10.1021/cn3000422] [Citation(s) in RCA: 178] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2012] [Accepted: 04/25/2012] [Indexed: 01/20/2023] Open
Abstract
Herein we review our recent efforts in searching for bioactive ligands by enumeration and virtual screening of the unknown chemical space of small molecules. Enumeration from first principles shows that almost all small molecules (>99.9%) have never been synthesized and are still available to be prepared and tested. We discuss open access sources of molecules, the classification and representation of chemical space using molecular quantum numbers (MQN), its exhaustive enumeration in form of the chemical universe generated databases (GDB), and examples of using these databases for prospective drug discovery. MQN-searchable GDB, PubChem, and DrugBank are freely accessible at www.gdb.unibe.ch.
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Affiliation(s)
- Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Berne, Freiestrasse 3, 3012 Berne, Switzerland
| | - Mahendra Awale
- Department of Chemistry and Biochemistry, University of Berne, Freiestrasse 3, 3012 Berne, Switzerland
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47
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Xie L, Kinnings SL, Xie L, Bourne PE. Predicting the Polypharmacology of Drugs: Identifying New Uses through Chemoinformatics, Structural Informatics, and Molecular Modeling‐Based Approaches. DRUG REPOSITIONING 2012:163-205. [DOI: 10.1002/9781118274408.ch7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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48
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Reymond JL, Ruddigkeit L, Blum L, van Deursen R. The enumeration of chemical space. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2012. [DOI: 10.1002/wcms.1104] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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49
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Nasr R, Vernica R, Li C, Baldi P. Speeding up chemical searches using the inverted index: the convergence of chemoinformatics and text search methods. J Chem Inf Model 2012; 52:891-900. [PMID: 22462644 DOI: 10.1021/ci200552r] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In ligand-based screening, retrosynthesis, and other chemoinformatics applications, one often seeks to search large databases of molecules in order to retrieve molecules that are similar to a given query. With the expanding size of molecular databases, the efficiency and scalability of data structures and algorithms for chemical searches are becoming increasingly important. Remarkably, both the chemoinformatics and information retrieval communities have converged on similar solutions whereby molecules or documents are represented by binary vectors, or fingerprints, indexing their substructures such as labeled paths for molecules and n-grams for text, with the same Jaccard-Tanimoto similarity measure. As a result, similarity search methods from one field can be adapted to the other. Here we adapt recent, state-of-the-art, inverted index methods from information retrieval to speed up similarity searches in chemoinformatics. Our results show a several-fold speed-up improvement over previous methods for both threshold searches and top-K searches. We also provide a mathematical analysis that allows one to predict the level of pruning achieved by the inverted index approach and validate the quality of these predictions through simulation experiments. All results can be replicated using data freely downloadable from http://cdb.ics.uci.edu/ .
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Affiliation(s)
- Ramzi Nasr
- Departments of Computer Science, University of California, Irvine, Irvine, California 92697-3435, United States
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50
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Biniashvili T, Schreiber E, Kliger Y. Improving Classical Substructure-Based Virtual Screening to Handle Extrapolation Challenges. J Chem Inf Model 2012; 52:678-85. [DOI: 10.1021/ci200472s] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Tammy Biniashvili
- Compugen LTD, Tel Aviv 69512, Israel
- The Mina and Everard Goodman
Faculty of Life Sciences, Bar Ilan University, Ramat-Gan 52900, Israel
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