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Journal articles on the topic 'Drug design'

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Published: 4 June 2021
Last updated: 25 January 2025

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1

James, Keith. "Drug design." Nature 359, no. 6394 (October 1992): 458. http://dx.doi.org/10.1038/359458a0.

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2

Takayanagl, Issei. "Drug receptors and drug design." Japanese Journal of Pharmacology 67 (1995): 45. http://dx.doi.org/10.1016/s0021-5198(19)46150-7.

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3

Garepally, Prasad, Swathi Goli, and Vijay Kumar Bontha. "Design, Development and Characterizations of Acyclovir Osmotic Tablets." Pharmaceutics and Pharmacology Research 1, no. 1 (October 8, 2018): 01–14. http://dx.doi.org/10.31579/2693-7247/005.

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Oral drug delivery is the most widely utilized route of administration, among all the routes of administration. That has been explored for the systemic delivery drug through different pharmaceutical dosage forms. It can be said that at least 90%of all drugs used to produce systemic effect is by oral route. Conventional oral drug delivery systems are known to provide an immediate release of drug, in which one cannot control the release of the drug and effective concentration at the target site.
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4

Sharma, Anu, Lalubhai Jangid, Nusrat Shaikh, and Jitendra Bhangale. "Computer-Aided Drug Design Boon in Drug Discovery." Asian Journal of Organic & Medicinal Chemistry 7, no. 1 (2022): 55–64. http://dx.doi.org/10.14233/ajomc.2022.ajomc-p361.

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An innovative sequential step of detecting new medicines or drugs dependent on the information of a target is called drug design. The drug is a small molecule that alters the capacity of a bimolecular, example, protein, receptor or catalyst that leads to restorative incentive for patients. Designing of drug by computational method helped steady use of computational science to find, improve and study drugs as well as biologically related active molecules. The displaying examines like the structure-based plan; ligand-based drugs structure; database looking and restricting partiality dependent on the information of a biological target. In this article, we present the zones where CADD (computer aided drug design) devices uphold the medication disclosure measure.
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5

Buchwald, Peter. "Computer-aided retrometabolic drug design: soft drugs." Expert Opinion on Drug Discovery 2, no. 7 (July 2007): 923–33. http://dx.doi.org/10.1517/17460441.2.7.923.

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6

Walsh, John S., and Gerald T. Miwa. "Bioactivation of Drugs: Risk and Drug Design." Annual Review of Pharmacology and Toxicology 51, no. 1 (February 10, 2011): 145–67. http://dx.doi.org/10.1146/annurev-pharmtox-010510-100514.

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7

Barakat, Khaled H., Michael Houghton, D. Lorne Tyrrel, and Jack A. Tuszynski. "Rational Drug Design." International Journal of Computational Models and Algorithms in Medicine 4, no. 1 (January 2014): 59–85. http://dx.doi.org/10.4018/ijcmam.2014010104.

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For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.
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8

Cohen, F. E. "Structural Drug Design." Science 261, no. 5122 (August 6, 1993): 773. http://dx.doi.org/10.1126/science.261.5122.773.

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9

Lorber, David M. "Computational drug design." Chemistry & Biology 6, no. 8 (August 1999): R227—R228. http://dx.doi.org/10.1016/s1074-5521(99)80093-3.

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Häyry, Pekka. "Rational drug design." Transplant Immunology 9, no. 2-4 (May 2002): 201. http://dx.doi.org/10.1016/s0966-3274(02)00018-7.

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11

J. Wilson, Lon, Dawson W. Cagle, Thomas P. Thrash, Steven J. Kennel, Saed Mirzadeh, J. Michael Alford, and Gary J. Ehrhardt. "Metallofullerene drug design." Coordination Chemistry Reviews 190-192 (September 1999): 199–207. http://dx.doi.org/10.1016/s0010-8545(99)00080-6.

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12

Häyry, P., D. du Toit, M. Sarwal, E. Aavik, A. Hoffrén, and J. Vamvakopoulos. "Rational drug design:." Transplantation Proceedings 34, no. 6 (September 2002): 2000–2002. http://dx.doi.org/10.1016/s0041-1345(02)02829-4.

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13

Hart, D., A. Langridge, D. Barlow, and B. Sutton. "Antiparasitic drug design." Parasitology Today 5, no. 4 (April 1989): 117–20. http://dx.doi.org/10.1016/0169-4758(89)90054-9.

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14

Mandal, Soma, Mee'nal Moudgil, and Sanat K. Mandal. "Rational drug design." European Journal of Pharmacology 625, no. 1-3 (December 2009): 90–100. http://dx.doi.org/10.1016/j.ejphar.2009.06.065.

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15

Takayanagi, Issei. "Drug receptor mechanisms and drug design." Japanese Journal of Pharmacology 73 (1997): 4. http://dx.doi.org/10.1016/s0021-5198(19)33785-0.

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16

Cooper, Kelvin. "Drug-receptor interactions and drug design." Trends in Pharmacological Sciences 9, no. 2 (February 1988): 51. http://dx.doi.org/10.1016/0165-6147(88)90115-0.

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17

陈, 米佳. "Research on Interactive Drug Packaging Design for the Elderly." Design 08, no. 03 (2023): 1735–42. http://dx.doi.org/10.12677/design.2023.83209.

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18

Gupta, Satya Prakash. "Roles of Fluorine in Drug Design and Drug Action." Letters in Drug Design & Discovery 16, no. 10 (September 19, 2019): 1089–109. http://dx.doi.org/10.2174/1570180816666190130154726.

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The article discusses the basic properties of fluorine atom that have made it so useful in drug development. It presents several examples of therapeutically useful drugs acting against many life-threatening diseases along with the mechanism as to how fluorine influences the drug activity. It has been pointed out that fluorine, due to its ability to increase the lipophilicity of the molecule, greatly affects the hydrophobic interaction between the drug molecule and the receptor. Because of its small size, it hardly produces any steric effect, rather due to electronic properties enters into electrostatic and hydrogen-bond interactions. Thus, it greatly affects the drug-receptor interaction and leads to increase the activity of the drugs.
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19

Doytchinova, Irini. "Drug Design—Past, Present, Future." Molecules 27, no. 5 (February 23, 2022): 1496. http://dx.doi.org/10.3390/molecules27051496.

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Drug design is a complex pharmaceutical science with a long history. Many achievements have been made in the field of drug design since the end of 19th century, when Emil Fisher suggested that the drug–receptor interaction resembles the key and lock interplay. Gradually, drug design has been transformed into a coherent and well-organized science with a solid theoretical background and practical applications. Now, drug design is the most advanced approach for drug discovery. It utilizes the innovations in science and technology and includes them in its wide-ranging arsenal of methods and tools in order to achieve the main goal: discovery of effective, specific, non-toxic, safe and well-tolerated drugs. Drug design is one of the most intensively developing modern sciences and its progress is accelerated by the implication of artificial intelligence. The present review aims to capture some of the most important milestones in the development of drug design, to outline some of the most used current methods and to sketch the future perspective according to the author’s point of view. Without pretending to cover fully the wide range of drug design topics, the review introduces the reader to the content of Molecules’ Special Issue “Drug Design—Science and Practice”.
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20

Prasad, G., K. Devika, P. Varshith, B. Shravani, E. Pavithra, and Ch Swathi. "Design and Optimizations of Aceclofenac Bioadhesive Extended Release Microspheres." Pharmaceutics and Pharmacology Research 4, no. 4 (December 3, 2021): 01–15. http://dx.doi.org/10.31579/2693-7247/055.

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The oral route for drug delivery is the most popular, desirable, and most preferred method for administrating therapeutically agents for systemic effects because it is a natural, convenient, and cost effective to manufacturing process. Oral route is the most commonly used route for drug administration. Although different route of administration are used for the delivery of drugs, oral route remain the preferred mode. Even for sustained release systems the oral route of administration has been investigated the most because of flexibility in designing dosage forms. Present controlled release drug delivery systems are for a maximum of 12 hours clinical effectiveness. Such systems are primarily used for the drugs with short elimination half life.
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21

Borisov, D. V., and A. V. Veselovsky. "Ligand-receptor binding kinetics in drug design." Biomeditsinskaya Khimiya 66, no. 1 (January 2020): 42–53. http://dx.doi.org/10.18097/pbmc20206601042.

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Traditionally, the thermodynamic values of affinity are considered as the main criterion for the development of new drugs. Usually, these values for drugs are measured in vitro at steady concentrations of the receptor and ligand, which are differed from in vivo environment. Recent studies have shown that the kinetics of the process of drug binding to its receptor make significant contribution in the drug effectiveness. This has increased attention in characterizing and predicting the rate constants of association and dissociation of the receptor ligand at the stage of preclinical studies of drug candidates. A drug with a long residence time can determine ligand-receptor selectivity (kinetic selectivity), maintain pharmacological activity of the drug at its low concentration in vivo. The paper discusses the theoretical basis of protein-ligand binding, molecular determinants that control the kinetics of the drug-receptor binding. Understanding the molecular features underlying the kinetics of receptor-ligand binding will contribute to the rational design of drugs with desired properties.
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22

Barrawaz, Aateka Y. "COMPUTER AIDED DRUG DESIGN: A MINI-REVIEW." Journal of Medical Pharmaceutical And Allied Sciences 9, no. 5 (October 15, 2020): 2584–91. http://dx.doi.org/10.22270/jmpas.v9i5.971.

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New drug discovery and development process is considered much complex process which is time consuming and resources accommodating too. So computer aided drug design are being broadly used to enhance the effectiveness of the drug discovery and development process which ultimately saves time and resources. Various approaches to Computer aided drug design are evaluated to shows potential techniques in accordance with their needs. Two approaches are considered to designing of drug first one is structure-based and second one is Ligand based drug designs. In this review, we are discussing about highly effective and powerful techniques for drug discovery and development as well as various methods of Computer aided drug design like molecular docking at virtual screening for lead identification, QSAR, molecular homology, de-novo design, molecular modeling and optimization. It also elaborate about different software used in Computer aided drug design, different application of Computer aided drug design etc. Major objectives of Computer aided drug design are to commence collaborative foundation of research activities and to discover new chemical entities for novel therapeutics drugs
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23

Zishan Ibrahim, Mohammad. "Review on Design of liposome’s as drug delivery system." Pharmacy and Drug Development 1, no. 2 (December 8, 2022): 01–04. http://dx.doi.org/10.58489/2836-2322/010.

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Liposomes wasinitially described by the British haematologist Dr. Alec D. Bangham and collaborators at the University of Cambridge in the 1960s and the first report was publicized in 1964.Liposomes are a form of vesicles that consist of many, few or just one phospholipid bilayer. The polar character of the liposomal core allow polar drug molecules to be capsulize. Amphiphilic (both hydrophilic and hydrophobic) and lipophilic molecules are solubilised within the phospholipid bilayer according to their affinity towards phospholipids.
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24

Narkhede, Jagruti. "Artificial Intelligence in Drug Discovery and Drug Design." International Journal of Pharmaceutical Research and Applications 09, no. 05 (May 2024): 640–55. https://doi.org/10.35629/4494-0905640655.

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Overthepasttenyears,artificialintelligencehasrevolut ionisedthefieldofdrugresearch. The process for discovering new drugs could be completelytransformed by artificial intelligence, which could provide increased speed, accuracy, and efficiency. The process for discovering new drugs could be completely transformed by artificial intelligence, which could provide increased speed, accuracy, and efficiency. Numerous uses of artificial intelligence, including virtual screening and drug design, have been employed in drug development. AI methods are brokendown into learning paradigms and modelarchitectures. The surveyed publications are arranged chronologicallyto illustratethe evolution ofAI indrug discoveryover time in terms of technical advancement. We anticipate that this survey offers an in-depth examination of artificial intelligence in drug discovery. This development is being motivated, among other things, by the increasing use of machine learning, and specifically deep learning, in many scientific domains and by advancements in computer hardware and software. Medicinal chemistry has benefited from the beginning scepticism that has begun to fade over the use of AI in pharmaceutical development. A number of methodological advancements, including hybrid de novo design, message-passing models, spatial symmetry-preserving networks, and other cutting-edge machine learning paradigms, are probably going to become standard practicesandaidinansweringsomeofthetrickiestprobl ems.Thecreationofmodelsandopen data sharing will be essential to the advancement of drug discovery using AI. There is a growingpotentialforthediscoveryofseveralnovelmed icationsasaresultoftheadvancement of artificial intelligence in the pharmaceutical sector. Human illness rates are rising dramatically, but there areveryfew medications available totreatorcurethem. However, the pharmaceuticalindustry'sandartificialintelligence'sc ombinedeffortswillpreventthissortof situation in the future by accelerating the discovery of medications with better clinical outcomes. AI-based drug development techniques are being used by several pharmaceutical companies to treat a variety of illnesses, including diabetes, Parkinson's disease, Alzheimer's disease, OCD, and more Technology may significantlyassist in resolving a number of issues and limitations with the conventional drug development process.
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25

Gibson, D. "Drug–DNA interactions and novel drug design." Pharmacogenomics Journal 2, no. 5 (May 2002): 275–76. http://dx.doi.org/10.1038/sj.tpj.6500133.

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26

Ranade, Vasant V. "Drug Metabolism in Drug Design and Development." American Journal of Therapeutics 16, no. 5 (September 2009): 467. http://dx.doi.org/10.1097/mjt.0b013e3181728805.

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27

Prokai, Laszlo, and Katalin Prokai-Tatrai. "Metabolism-based drug design and drug targeting." Pharmaceutical Science & Technology Today 2, no. 11 (November 1999): 457–62. http://dx.doi.org/10.1016/s1461-5347(99)00208-4.

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28

Ugariogu, Sylvester Nnaemeka. "Natural Product Chemistry and Computer Aided Drug Design an Approach to Drug Discovery: A Review Article." International Journal of Pharmacognosy & Chinese Medicine 4, no. 3 (2020): 1–8. http://dx.doi.org/10.23880/ipcm-16000207.

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Natural products have been an inherent part of sustaining acculturation because of their medicinal properties. Past discoveries of bioactive natural products have relied on serendipity and accidental experience, and these compounds serve as inspiration for the generation of analogs with desired physicochemical properties. Bioactive natural products with therapeutic potential are abundantly available in nature and some of them are beyond exploration by conventional methods. However there has been a great breakthrough in the study of computer aided drug design (CADD) as many unfruitful lab researches have been averted and money, time and energies saved through CADD. Computer-aided drug design is a stimulating, arousing and manifold discipline where various aspects of applied and basic research integrate and induce each other. The empirical basis of CADD involves quantum mechanics and molecular modeling studies like structure based drug design; ligand-based drug design; database searching and binding affinity based on the knowledge of a biological target. In this present review we present the areas where natural product chemistry and CADD tools support drug discovery processes.
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29

Rother, Kristian, Mathias Dunkel, Elke Michalsky, Silke Trissl, Andrean Goede, Ulf Leser, and Robert Preissner. "A structural keystone for drug design." Journal of Integrative Bioinformatics 3, no. 1 (June 1, 2006): 21–31. http://dx.doi.org/10.1515/jib-2006-19.

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Abstract 3D-structures of proteins and potential ligands are the cornerstones of rational drug design. The first brick to build upon is selecting a protein target and finding out whether biologically active compounds are known. Both tasks require more information than the structures themselves provide. For this purpose we have built a web resource bridging protein and ligand databases. It consists of three parts: i) A data warehouse on annotation of protein structures that integrates many well-known databases such as Swiss-Prot, SCOP, ENZYME and others. ii) A conformational library of structures of approved drugs. iii) A conformational library of ligands from the PDB, linking the realms of proteins and small molecules. The data collection contains structures of 30,000 proteins, 5,000 different ligands from 70,000 ligand-protein complexes, and 2,500 known drugs. Sets of protein structures can be refined by criteria like protein fold, family, metabolic pathway, resolution and textual annotation. The structures of organic compounds (drugs and ligands) can be searched considering chemical formula, trivial and trade names as well as medical classification codes for drugs (ATC). Retrieving structures by 2D-similarity has been implemented for all small molecules using Tanimoto coefficients. For the drug structures, 110,000 structural conformers have been calculated to account for structural flexibility. Two substances can be compared online by 3D-superimposition, where the pair of conformers that fits best is detected. Together, these web-accessible resources can be used to identify promising drug candidates. They have been used in-house to find alternatives to substances with a known binding activity but adverse side effects.
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Xu, Zishuo. "Research on targeted drug design based on computer technology." E3S Web of Conferences 553 (2024): 04013. http://dx.doi.org/10.1051/e3sconf/202455304013.

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This paper provides an insight into the importance and application of computer-aided drug design in today’s drug discovery and development. With the development of medicinal chemistry, molecular biology and proteomics, the synthesis and extraction pathways of many common drugs have been computer-assisted, which helps to optimize the reaction conditions, reduce the generation of waste and hazardous substances, and promote green synthesis and sustainable development. Scientists have conducted in-depth research on the pathogenesis of various diseases, especially in the field of oncology, where significant progress has been made. The intervention of computer technology in drug design and target search has accelerated the process of drug research and development and improved work efficiency. Meanwhile, the current progress of targeted drug research, traditional drug synthesis and target searching methods and computer-assisted target searching and drug design are introduced. Many targeted drugs have been applied in the clinic and shown good therapeutic effects, such as the application of EGFR inhibitors in non-small cell lung cancer patients. Traditional drug synthesis routes are complex, while computer design of targeted drugs can be used to obtain the desired drugs more easily. The article also details the general process and software used for computer-aided drug design, including methods for simulating target finding, protein prediction, and more. Although computer-aided design has made significant progress in the development of targeted anticancer drugs, some challenges remain, such as problems with prediction accuracy, design speed, and multidisciplinary integration. However, the accuracy and efficiency of targeted drug design can be improved by integrating the latest computational models and algorithms. In the future, combining big data and machine learning technologies, computer-aided drug synthesis is expected to become an important tool for drug development, improving therapeutic efficacy and reducing side effects.
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31

Pareek, Varun, Lakshya Tuteja, Lokendra Sharma, Susheel Kumar, and Noopur Verma. "Revolutionizing Drug Design with Artificial Intelligence: A Comprehensive Review of Techniques, Applications, and Case Studies." Journal of Pharmaceutical Research 22, no. 3 (December 31, 2023): 103–12. http://dx.doi.org/10.18579/jopcr/v22.3.23.54.

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Introduction: Artificial intelligence (AI) has the potential to revolutionize drug design and discovery by significantly reducing the time and costs involved in developing new drugs. This literature review aims to explore the use of AI in drug design, focusing on virtual screening, de novo drug design, and prediction of ADME properties. Objective: The objective of this review is to provide an overview of the AI techniques used in drug design and their applications in virtual screening, de novo drug design, and prediction of ADME properties. The review also aims to summarize the advantages and limitations of these approaches and present case studies and examples showcasing their use in drug design. Methodology: A comprehensive search of academic databases was conducted, and 11 relevant articles were selected for inclusion in this review. The selected articles were analyzed to identify the AI techniques used in drug design, their applications, advantages, and limitations. Case studies and examples were also examined to demonstrate the efficacy of AI in drug design. Results: AI techniques such as machine learning, deep learning, and reinforcement learning have been successfully used in virtual screening, de novo drug design, and prediction of ADME properties. Virtual screening involves the use of AI algorithms to identify promising compounds for further testing, while de novo drug design involves the generation of novel compounds using AI techniques. Prediction of ADME properties involves the use of AI to predict the absorption, distribution, metabolism, and excretion of drug candidates. The case studies and examples presented in this review demonstrate the potential of AI to accelerate drug design and discovery. Conclusion: AI has the potential to revolutionize drug design and discovery by significantly reducing the time and costs involved in developing new drugs. Virtual screening, de novo drug design, and prediction of ADME properties are among the most promising applications of AI in drug design. However, further research is needed to fully explore the potential of AI in drug design and overcome some of the limitations of current approaches. Keywords: Artificial Intelligence; Drug Design; Virtual Screening; De Novo Drug Design; ADME Prediction
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De, Baishakhi, Koushik Bhandari, Francisco J. B. Mendonça, Marcus T. Scotti, and Luciana Scotti. "Computational Studies in Drug Design Against Cancer." Anti-Cancer Agents in Medicinal Chemistry 19, no. 5 (June 27, 2019): 587–91. http://dx.doi.org/10.2174/1871520618666180911125700.

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Background: The application of in silico tools in the development of anti cancer drugs. Objective: The summing of different computer aided drug design approaches that have been applied in the development of anti cancer drugs. Methods: Structure based, ligand based, hybrid protein-ligand pharmacophore methods, Homology modeling, molecular docking aids in different steps of drug discovery pipeline with considerable saving in time and expenditure. In silico tools also find applications in the domain of cancer drug development. Results: Structure-based pharmacophore modeling aided in the identification of PUMA inhibitors, structure based approach with high throughput screening for the development of Bcl-2 inhibitors, to derive the most relevant protein-protein interactions, anti mitotic agents; I-Kappa-B Kinase β (IKK- β) inhibitor, screening of new class of aromatase inhibitors that can be important targets in cancer therapy. Conclusion: Application of computational methods in the design of anti cancer drugs was found to be effective.
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Gozes, Illana, and Sharon Furman. "VIP and Drug Design." Current Pharmaceutical Design 9, no. 6 (February 1, 2003): 483–94. http://dx.doi.org/10.2174/1381612033391667.

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Schiavone, N., M. Donnini, A. Nicolin, and S. Capaccioli. "Antisense Oligonucleotide Drug Design." Current Pharmaceutical Design 10, no. 7 (March 1, 2004): 769–84. http://dx.doi.org/10.2174/1381612043452956.

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Kamiya, Kotaro, and Daitaro Misawa. "AI-based drug design." Japanese Journal of Pesticide Science 47, no. 2 (August 20, 2022): 109–12. http://dx.doi.org/10.1584/jpestics.w22-33.

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SAITO, Isao. "DNA-Targeting Drug Design." Journal of Pesticide Science 25, no. 3 (2000): 270–74. http://dx.doi.org/10.1584/jpestics.25.270.

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Marshall, G. R. "Computer-Aided Drug Design." Annual Review of Pharmacology and Toxicology 27, no. 1 (April 1987): 193–213. http://dx.doi.org/10.1146/annurev.pa.27.040187.001205.

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Jain, A. "Computer aided drug design." Journal of Physics: Conference Series 884 (August 2017): 012072. http://dx.doi.org/10.1088/1742-6596/884/1/012072.

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Watson, Dr K. A. "COMPUTERS IN DRUG DESIGN." Biochemical Society Transactions 27, no. 3 (June 1, 1999): A90. http://dx.doi.org/10.1042/bst027a090c.

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40

Hodgson, John. "Data-Directed Drug Design." Nature Biotechnology 9, no. 1 (January 1991): 19–21. http://dx.doi.org/10.1038/nbt0191-19.

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Chin, G. "BIOCHEMISTRY: Bacterial Drug Design." Science 316, no. 5832 (June 22, 2007): 1670c. http://dx.doi.org/10.1126/science.316.5832.1670c.

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Dowty, Martin E., George Hu, Fengmei Hua, F. Barclay Shilliday, and Heather V. Dowty. "Drug Design Structural Alert." International Journal of Toxicology 30, no. 5 (August 25, 2011): 546–50. http://dx.doi.org/10.1177/1091581811413833.

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In the process of drug design, it is important to consider potential structural alerts that may lead to toxicosis. This work illustrates how using trifluoroethane as a part of a novel chemical entity led to cytochrome P450 – mediated N-dealkylation and the formation of trifluoroacetaldehyde, a known testicular toxicant, in exploratory safety studies in rats. Testicular toxicosis was noted microscopically in a dose-dependent manner as measured by testicular spermatocytic degeneration and necrosis and excessive intratubular cellular debris in the epididymis. This apparent toxic effect correlated well with the dose-dependent formation of trifluoroacetaldehyde, identified from in vitro rat liver microsome metabolism studies. A similar safety study performed with an N-tetrazole substitution in place of the N-trifluoroethane showed no evidence of testicular injury, implicating further the role of trifluoroacetaldehyde in the testicular lesion observed. These results highlight the relevance of early metabolic and safety testing in assessing potential structural alerts in drug design.
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Amzel, L. Mario. "Structure-based drug design." Current Opinion in Biotechnology 9, no. 4 (August 1998): 366–69. http://dx.doi.org/10.1016/s0958-1669(98)80009-8.

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Stepan, Antonia F., Vincent Mascitti, Kevin Beaumont, and Amit S. Kalgutkar. "Metabolism-guided drug design." MedChemComm 4, no. 4 (2013): 631. http://dx.doi.org/10.1039/c2md20317k.

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Johnson, L. N. "Structure based drug design." Acta Crystallographica Section A Foundations of Crystallography 49, s1 (August 21, 1993): c4. http://dx.doi.org/10.1107/s0108767378099882.

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Zhang, Weilin, Jianfeng Pei, and Luhua Lai. "Computational Multitarget Drug Design." Journal of Chemical Information and Modeling 57, no. 3 (February 23, 2017): 403–12. http://dx.doi.org/10.1021/acs.jcim.6b00491.

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47

HENRY, CELIA M. "STRUCTURE-BASED DRUG DESIGN." Chemical & Engineering News 79, no. 23 (June 4, 2001): 69–78. http://dx.doi.org/10.1021/cen-v079n023.p069.

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48

Suguna, K. "Crystallography and drug design." Resonance 19, no. 12 (December 2014): 1093–103. http://dx.doi.org/10.1007/s12045-014-0135-6.

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49

Huxtable, Ryan J. "Drug Design for neuroscience." Neurochemistry International 26, no. 5 (May 1995): 537. http://dx.doi.org/10.1016/0197-0186(95)90019-5.

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50

Moore, Michael. "Anti-Cancer Drug Design." British Journal of Cancer 52, no. 5 (November 1985): i3. http://dx.doi.org/10.1038/bjc.1985.242.

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