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Singh S, Kaur N, Gehlot A. Application of artificial intelligence in drug design: A review. Comput Biol Med 2024; 179:108810. [PMID: 38991316 DOI: 10.1016/j.compbiomed.2024.108810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/31/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.
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Affiliation(s)
- Simrandeep Singh
- Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India.
| | - Navjot Kaur
- Department of Pharmacognosy, Amar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, Ropar, India
| | - Anita Gehlot
- Uttaranchal Institute of technology, Uttaranchal University, Dehradun, India
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2
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Sufyan M, Shokat Z, Ashfaq UA. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective. Comput Biol Med 2023; 165:107356. [PMID: 37688994 DOI: 10.1016/j.compbiomed.2023.107356] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/21/2023] [Accepted: 08/12/2023] [Indexed: 09/11/2023]
Abstract
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.
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Affiliation(s)
- Muhammad Sufyan
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Zeeshan Shokat
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
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3
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Tagde P, Tagde S, Bhattacharya T, Tagde P, Chopra H, Akter R, Kaushik D, Rahman MH. Blockchain and artificial intelligence technology in e-Health. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:52810-52831. [PMID: 34476701 PMCID: PMC8412875 DOI: 10.1007/s11356-021-16223-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/24/2021] [Indexed: 05/21/2023]
Abstract
Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.
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Affiliation(s)
- Priti Tagde
- Bhabha Pharmacy Research Institute, Bhabha University Bhopal, Bhopal M.P, India.
- PRISAL Foundation (Pharmaceutical Royal International Society), New delhi, India.
| | - Sandeep Tagde
- PRISAL Foundation (Pharmaceutical Royal International Society), New delhi, India
| | - Tanima Bhattacharya
- School of Chemistry & Chemical Engineering, Hubei University, Wuhan, China
- Department of Science & Engineering, Novel Global Community Education Foundation, Hebersham, Australia
| | - Pooja Tagde
- Practice of Medicine Department, Govt. Homeopathy College, Bhopal, M.P, India
| | - Hitesh Chopra
- Chitkara College of Pharmacy, Rajpura, Punjab, 140401, India
| | - Rokeya Akter
- Department of Pharmacy, Jagannath University, Sadarghat, Dhaka, 1100, Bangladesh
| | - Deepak Kaushik
- Department of Pharmaceutical Sciences, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Md Habibur Rahman
- Department of Pharmacy, Southeast University, Banani, Dhaka, 1213, Bangladesh.
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Kashyap K, Siddiqi MI. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents. Mol Divers 2021; 25:1517-1539. [PMID: 34282519 DOI: 10.1007/s11030-021-10274-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.
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Affiliation(s)
- Kushagra Kashyap
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India.,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India
| | - Mohammad Imran Siddiqi
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India. .,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India.
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Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today 2021; 26:80-93. [PMID: 33099022 PMCID: PMC7577280 DOI: 10.1016/j.drudis.2020.10.010] [Citation(s) in RCA: 314] [Impact Index Per Article: 104.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 09/03/2020] [Accepted: 10/13/2020] [Indexed: 02/07/2023]
Abstract
Artificial intelligence-integrated drug discovery and development has accelerated the growth of the pharmaceutical sector, leading to a revolutionary change in the pharma industry. Here, we discuss areas of integration, tools, and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them.
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Affiliation(s)
- Debleena Paul
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Gaurav Sanap
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Snehal Shenoy
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Dnyaneshwar Kalyane
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Kiran Kalia
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Rakesh K Tekade
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India.
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Jain Pancholi N, Sonker N, Bajpai J, Bajpai AK. Predictions of Drug-Protein Interactions and Study of Magnetically Assisted Release Dynamics of 5-Fluorouracil from Soya Protein-Coated Iron Oxide Core-Shell Nanoparticles. ACS APPLIED BIO MATERIALS 2020; 3:3170-3186. [PMID: 35025360 DOI: 10.1021/acsabm.0c00178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In silico studies were performed using 5-fluorouracil (5-FU) to explore the efficacy of template docking and facilitate designing of drug nanocarrier systems. The binding of human uridine phosphorylase (huPP1) with 5-FU was found to show the following interactions: (1) hydrogen bonds were alleviated by a network of GLN217 and ARG219, (2) hydrophobic interactions were shown by PHE213, THR141, LEU272, and ILE281 (3) positive electrostatic interactions were shown by PHE213, THR141, LEU272, SER142, GLU248, and GLY143. As an experimental supplementation and validation to the adopted computational approach, 5- FU-loaded soya protein-coated iron oxide (SPCIO) core-shell nanoparticles were prepared following microemulsion and co-precipitation techniques and subsequently characterized by FTIR, particle size and zeta potential studies, TEM, XRD, and DSC techniques. Whereas the FTIR spectra confirm the presence of the soya protein and drug 5-FU in the nanoparticles, the zeta potential was found to be suppressed due to the loading of 5-FU. The XRD study confirmed the crystalline nature of the drug-loaded nanoparticles. TEM analysis suggested that the nanoparticles have sizes up to 200 nm and the morphology and size remain almost the same even after loading of the drug 5-FU onto nanoparticles. The soya protein-coated iron oxide nanoparticles demonstrated zero cytotoxicity against fibroblast cells. The controlled release of 5-FU was studied in vitro, and the effects of pH, chemical composition of nanoparticles, extent of drug loading, and simulated biofluids on the controlled release of 5-FU were studied. The swelling of nanoparticles and release of 5-FU were found to increase with increasing strength of the externally applied magnetic field.
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Affiliation(s)
- Nilanjana Jain Pancholi
- Bose Memorial Research Lab Department of Chemistry Government Autonomous Science College, Jabalpur, M.P. 482001, India
| | - Neha Sonker
- Bose Memorial Research Lab Department of Chemistry Government Autonomous Science College, Jabalpur, M.P. 482001, India
| | - Jaya Bajpai
- Bose Memorial Research Lab Department of Chemistry Government Autonomous Science College, Jabalpur, M.P. 482001, India
| | - Anil Kumar Bajpai
- Bose Memorial Research Lab Department of Chemistry Government Autonomous Science College, Jabalpur, M.P. 482001, India
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Lin X, Li X, Lin X. A Review on Applications of Computational Methods in Drug Screening and Design. Molecules 2020; 25:E1375. [PMID: 32197324 PMCID: PMC7144386 DOI: 10.3390/molecules25061375] [Citation(s) in RCA: 222] [Impact Index Per Article: 55.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/16/2020] [Accepted: 03/16/2020] [Indexed: 12/27/2022] Open
Abstract
Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.
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Affiliation(s)
- Xiaoqian Lin
- Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China;
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xiu Li
- School of Chemistry and Material Science, Shanxi Normal University, Linfen 041004, China;
| | - Xubo Lin
- Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China;
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
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Qian T, Zhu S, Hoshida Y. Use of big data in drug development for precision medicine: an update. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2019; 4:189-200. [PMID: 31286058 PMCID: PMC6613936 DOI: 10.1080/23808993.2019.1617632] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 05/08/2019] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios. AREAS COVERED Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery. EXPERT OPINION In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.
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Affiliation(s)
- Tongqi Qian
- Department of Genetics and Genomic Sciences and Icahn
Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
| | - Shijia Zhu
- Liver Tumor Translational Research Program, Simmons
Comprehensive Cancer Center, Division of Digestive and Liver Diseases, Department of
Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX
75390, USA
| | - Yujin Hoshida
- Liver Tumor Translational Research Program, Simmons
Comprehensive Cancer Center, Division of Digestive and Liver Diseases, Department of
Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX
75390, USA
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Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today 2017; 22:1680-1685. [PMID: 28881183 DOI: 10.1016/j.drudis.2017.08.010] [Citation(s) in RCA: 269] [Impact Index Per Article: 38.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 07/13/2017] [Accepted: 08/30/2017] [Indexed: 01/29/2023]
Abstract
Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era.
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Affiliation(s)
- Lu Zhang
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, 100124, China
| | - Jianjun Tan
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, 100124, China.
| | - Dan Han
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, 100124, China
| | - Hao Zhu
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, 100124, China; Department of Chemistry, Rutgers University, Camden, NJ 08102, USA; The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA.
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Poongavanam V, Namasivayam V, Vanangamudi M, Al Shamaileh H, Veedu RN, Kihlberg J, Murugan NA. Integrative approaches in
HIV
‐1 non‐nucleoside reverse transcriptase inhibitor design. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2017. [DOI: 10.1002/wcms.1328] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
| | | | - Murugesan Vanangamudi
- Department of Medicinal and Pharmaceutical ChemistrySree Vidyanikethan College of Pharmacy Tirupathi India
| | | | - Rakesh N Veedu
- Centre for Comparative GenomicsMurdoch University Perth Australia
- Perron Institute for Neurological and Translational Science Perth Australia
| | - Jan Kihlberg
- Department of Chemistry‐BMCUppsala University Uppsala Sweden
| | - N Arul Murugan
- Division of Theoretical Chemistry and Biology, School of BiotechnologyKTH‐Royal Institute of Technology Stockholm Sweden
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Mandalapu D, Singh DK, Gupta S, Balaramnavar VM, Shafiq M, Banerjee D, Sharma VL. Discovery of monocarbonyl curcumin hybrids as a novel class of human DNA ligase I inhibitors: in silico design, synthesis and biology. RSC Adv 2016. [DOI: 10.1039/c5ra25853g] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
A pharmacophore model identified a novel class of hLigI inhibitors to treat cancer. 36 compounds were synthesized and the identified inhibitor, compound 23 shown antiligase activity at IC50 24.9 μM by abolishing the interaction between hLigI and DNA.
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Affiliation(s)
- Dhanaraju Mandalapu
- Medicinal & Process Chemistry Division
- CSIR-Central Drug Research Institute (CSIR-CDRI)
- Lucknow
- India
| | - Deependra Kumar Singh
- Molecular & Structural Biology Division
- CSIR-Central Drug Research Institute (CSIR-CDRI)
- Lucknow
- India
| | - Sonal Gupta
- Medicinal & Process Chemistry Division
- CSIR-Central Drug Research Institute (CSIR-CDRI)
- Lucknow
- India
- Academy of Scientific and Innovative Research (AcSIR)
| | - Vishal M. Balaramnavar
- Molecular & Structural Biology Division
- CSIR-Central Drug Research Institute (CSIR-CDRI)
- Lucknow
- India
| | - Mohammad Shafiq
- Molecular & Structural Biology Division
- CSIR-Central Drug Research Institute (CSIR-CDRI)
- Lucknow
- India
| | - Dibyendu Banerjee
- Molecular & Structural Biology Division
- CSIR-Central Drug Research Institute (CSIR-CDRI)
- Lucknow
- India
- Academy of Scientific and Innovative Research (AcSIR)
| | - Vishnu Lal Sharma
- Medicinal & Process Chemistry Division
- CSIR-Central Drug Research Institute (CSIR-CDRI)
- Lucknow
- India
- Academy of Scientific and Innovative Research (AcSIR)
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12
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Vite-Caritino H, Méndez-Lucio O, Reyes H, Cabrera A, Chávez D, Medina-Franco JL. Advances in the development of pyridinone derivatives as non-nucleoside reverse transcriptase inhibitors. RSC Adv 2016. [DOI: 10.1039/c5ra25722k] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Medicinal chemistry, computational design and biological screening have advanced pyridin-2(1H)-one derivatives as a promising class of non-nucleoside reverse transcriptase inhibitors for the treatment of HIV/AIDS.
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Affiliation(s)
- Hugo Vite-Caritino
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- Mexico City 04510
- Mexico
| | - Oscar Méndez-Lucio
- Unilever Centre for Molecular Science Informatics
- Department of Chemistry
- University of Cambridge
- Cambridge CB2 1EW
- UK
| | - Héctor Reyes
- Centro de Graduados e Investigación en Química del Instituto Tecnológico de Tijuana
- Tijuana
- Mexico
| | - Alberto Cabrera
- Centro de Graduados e Investigación en Química del Instituto Tecnológico de Tijuana
- Tijuana
- Mexico
| | - Daniel Chávez
- Centro de Graduados e Investigación en Química del Instituto Tecnológico de Tijuana
- Tijuana
- Mexico
| | - José L. Medina-Franco
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- Mexico City 04510
- Mexico
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