Journal articles: 'Computer-based drug design'

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ISHIGURO, Masaji. "Computer-Aided Structure Based Drug Design." Journal of the agricultural chemical society of Japan 67, no. 9 (1993): 1295–98. http://dx.doi.org/10.1271/nogeikagaku1924.67.1295.

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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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Prathipati, Philip, Anshuman Dixit, and Anil Saxena. "Computer-Aided Drug Design: Integration of Structure-Based and Ligand-Based Approaches in Drug Design." Current Computer Aided-Drug Design 3, no. 2 (June 1, 2007): 133–48. http://dx.doi.org/10.2174/157340907780809516.

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Zeng, Huahui, and Xiangxiang Wu. "Alzheimer's disease drug development based on Computer-Aided Drug Design." European Journal of Medicinal Chemistry 121 (October 2016): 851–63. http://dx.doi.org/10.1016/j.ejmech.2015.08.039.

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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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Suzuki, E., T. Akutsu, and S. Ohsuga. "Knowledge-based system for computer-aided drug design." Knowledge-Based Systems 6, no. 2 (June 1993): 114–26. http://dx.doi.org/10.1016/0950-7051(93)90026-p.

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Ejalonibu, Murtala A., Segun A. Ogundare, Ahmed A. Elrashedy, Morufat A. Ejalonibu, Monsurat M. Lawal, Ndumiso N. Mhlongo, and Hezekiel M. Kumalo. "Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach." International Journal of Molecular Sciences 22, no. 24 (December 9, 2021): 13259. http://dx.doi.org/10.3390/ijms222413259.

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Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

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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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Douguet, Dominique, Hélène Munier-Lehmann, Gilles Labesse, and Sylvie Pochet. "LEA3D: A Computer-Aided Ligand Design for Structure-Based Drug Design." Journal of Medicinal Chemistry 48, no. 7 (April 2005): 2457–68. http://dx.doi.org/10.1021/jm0492296.

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Yu, Wenye, and Zhenyu Chen. "Computer Aided Drug Design Based on Artificial Intelligence Algorithm." Journal of Physics: Conference Series 2066, no. 1 (November 1, 2021): 012012. http://dx.doi.org/10.1088/1742-6596/2066/1/012012.

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Abstract The problems such as high cost and long development time in drug design and development have an important impact on its development, which makes many scholars devote themselves to looking for the auxiliary model of drug design. With the rapid development of computer technology, computer-aided drug molecular research model is more and more mature. This paper aims to study the computer-aided drug system based on artificial intelligence algorithm, so that researchers can speed up the process and reduce the cost when searching for specific protein molecules. In this paper, the principle of complementary matching in the docking process of target molecules and ligands, which is commonly used in drug design, is described, and the functional expression mode and various docking methods of molecular docking are studied. Finally, the research hotspots of molecular docking technology are analyzed, including scoring function, search strategy and flexible protein docking. Ant colony algorithm is introduced into molecular docking platform as a variant of conformation search algorithm, and a new plants algorithm is developed. Finally, the implementation of plants algorithm is analyzed in detail, and the optimized plants system and gold system based on genetic algorithm are simulated, and the relevant experimental data are counted. The simulation results show that the new drug design method based on ant colony algorithm has advantages in docking success rate, docking speed and docking accuracy. The success rate of plants is higher than that of gold, and the docking time is only 1/6 of that of gold.

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Schneider, Gisbert, and Uli Fechner. "Computer-based de novo design of drug-like molecules." Nature Reviews Drug Discovery 4, no. 8 (August 2005): 649–63. http://dx.doi.org/10.1038/nrd1799.

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Surabhi, Surabhi, and BK Singh. "COMPUTER AIDED DRUG DESIGN: AN OVERVIEW." Journal of Drug Delivery and Therapeutics 8, no. 5 (September 18, 2018): 504–9. http://dx.doi.org/10.22270/jddt.v8i5.1894.

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Discovery and development of a new drug is generally known as a very complex process which takes a lot of time and resources. So now a day’s computer aided drug design approaches are used very widely to increase the efficiency of the drug discovery and development course. Various approaches of CADD are evaluated as promising techniques according to their need, in between all these structure-based drug design and ligand-based drug design approaches are known as very efficient and powerful techniques in drug discovery and development. These both methods can be applied with molecular docking to virtual screening for lead identification and optimization. In the recent times computational tools are widely used in pharmaceutical industries and research areas to improve effectiveness and efficacy of drug discovery and development pipeline. In this article we give an overview of computational approaches, which is inventive process of finding novel leads and aid in the process of drug discovery and development research. Keywords: computer aided drug discovery, structure-based drug design, ligand-based drug design, virtual screening and molecular docking

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Sehgal, Vijay Kumar, Supratik Das, and Anand Vardhan. "Computer Aided Drug Designing." International Journal of Medical and Dental Sciences 6, no. 1 (January 1, 2017): 1433. http://dx.doi.org/10.18311/ijmds/2017/18804.

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Designing of drugs and their development are a time and resource consuming process. There is an increasing effort to introduce the role of computational approach to chemical and biological space in order to organise the design and development of drugs and their optimisation. The role of Computer Aided Drug Designing (CADD) are nowadays expressed in Nanotechnology, Molecular biology, Biochemistry etc. It is a diverse discipline where various forms of applied and basic researches are interlinked with each other. Computer aided or in Silico drug designing is required to detect hits and leads. Optimise/ alter the absorption, distribution, metabolism, excretion and toxicity profile and prevent safety issues. Some commonly used computational approaches include ligand-based drug design, structure-based drug design, and quantitative structure-activity and quantitative structure-property relationships. In today's world, due to an avid interest of regulatory agencies and, even pharmaceutical companies in advancing drug discovery and development process by computational means, it is expected that its power will grow as technology continues to evolve. The main purpose of this review article is to give a brief glimpse about the role Computer Aided Drug Design has played in modern medical science and the scope it carries in the near future, in the service of designing newer drugs along with lesser expenditure of time and money.

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Lin, Yipeng. "Review of Modern Computer-aided Drug Design Methods." International Journal of Biology and Life Sciences 1, no. 1 (December 1, 2022): 47–50. http://dx.doi.org/10.54097/ijbls.v1i1.3230.

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Computer technology has developed rapidly in recent decades, and it is also widely used in the field of drug research and development. Computer-aided drug design (CADD) has appeared in the form of assistance to drug discovery process in this background. Computer-aided drug design can save time which is spent in the experimental process in the real world. Since appearance of computer-based drug design strategies, the concepts of HTS, structure-based and ligand-based drug design (SBDD and LBDD), and virtual screening (VS) have been proposed. These technologies have their own advantages and disadvantages, and have different scope of application. This review provides an introduction of modern drug design strategies which are based on computer technology, classifies different methods and finds out the basic working principle of each one, the applicability and limitations of these methods are discussed and recommendations are provided in the application of each method.

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Shimada, Jun, Sean Ekins, Carl Elkin, Eugene I. Shakhnovich, and Jean-Pierre Wery. "Integrating computer-based de novo drug design and multidimensional filtering for desirable drugs." TARGETS 1, no. 6 (December 2002): 196–205. http://dx.doi.org/10.1016/s1477-3627(02)02274-2.

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Gurung, Arun Bahadur, Mohammad Ajmal Ali, Joongku Lee, Mohammad Abul Farah, and Khalid Mashay Al-Anazi. "An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19." BioMed Research International 2021 (June 24, 2021): 1–18. http://dx.doi.org/10.1155/2021/8853056.

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The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

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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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Dorahy, Georgia, Jake Zheng Chen, and Thomas Balle. "Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs." Molecules 28, no. 3 (January 30, 2023): 1324. http://dx.doi.org/10.3390/molecules28031324.

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Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

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Sutch, Brian T., Rebecca M. Romero, Nouri Neamati, and Ian S. Haworth. "Integrated Teaching of Structure-Based Drug Design and Biopharmaceutics: A Computer-Based Approach." Journal of Chemical Education 89, no. 1 (September 8, 2011): 45–51. http://dx.doi.org/10.1021/ed200151b.

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Daina, Antoine, Marie-Claude Blatter, Vivienne Baillie Gerritsen, Patricia M. Palagi, Diana Marek, Ioannis Xenarios, Torsten Schwede, Olivier Michielin, and Vincent Zoete. "Drug Design Workshop: A Web-Based Educational Tool To Introduce Computer-Aided Drug Design to the General Public." Journal of Chemical Education 94, no. 3 (February 27, 2017): 335–44. http://dx.doi.org/10.1021/acs.jchemed.6b00596.

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Szarecka, Agnieszka, and Christopher Dobson. "Protein Structure Analysis: Introducing Students to Rational Drug Design." American Biology Teacher 81, no. 6 (August 1, 2019): 423–29. http://dx.doi.org/10.1525/abt.2019.81.6.423.

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We describe a series of engaging exercises in which students emulate the process that researchers use to efficiently develop new pharmaceutical drugs, that of rational drug design. The activities are taken from a three- to four-hour workshop regularly conducted with first-year college students and presented here to take place over three to four class periods. Although targeted at college students, these activities may be appropriate at the high school level as well, particularly in an AP Biology course. The exercises introduce students to the topics of bioinformatics and computer modeling, in the context of rational drug design, using free online resources such as databases and computer programs. Through the process of learning about computational drug design and drug optimization, students also learn content such as elements of protein structure and protein–ligand interactions. Based on our assessment, students enjoy the exercises, become more interested in bioinformatics and computer modeling, and demonstrate an increase in content knowledge relevant to the topics.

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Poroikov, V. V. "Computer-aided drug design: from discovery of novel pharmaceutical agents to systems pharmacology." Biomeditsinskaya Khimiya 66, no. 1 (January 2020): 30–41. http://dx.doi.org/10.18097/pbmc20206601030.

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New drug discovery is based on the analysis of public information about the mechanisms of the disease, molecular targets, and ligands, which interaction with the target could lead to the normalization of the pathological process. The available data on diseases, drugs, pharmacological effects, molecular targets, and drug-like substances, taking into account the combinatorics of the associative relations between them, correspond to the Big Data. To analyze such data, the application of computer-aided drug design methods is necessary. An overview of the studies in this area performed by the Laboratory for Structure-Function Based Drug Design of IBMC is presented. We have developed the approaches to identifying promising pharmacological targets, predicting several thousand types of biological activity based on the structural formula of the compound, analyzing protein-ligand interactions based on assessing local similarity of amino acid sequences, identifying likely molecular mechanisms of side effects of drugs, calculating the integral toxicity of drugs taking into account their metabolism, have been developed in the human body, predicting sustainable and sensitive options strains and evaluating the effectiveness of combinations of antiretroviral drugs in patients, taking into account the molecular genetic characteristics of the clinical isolates of HIV-1. Our computer programs are implemented as the web-services freely available on the Internet, which are used by thousands of researchers from many countries of the world to select the most promising substances for the synthesis and determine the priority areas for experimental testing of their biological activity.

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Bruch, Eduardo M., Stéphanie Petrella, and Marco Bellinzoni. "Structure-Based Drug Design for Tuberculosis: Challenges Still Ahead." Applied Sciences 10, no. 12 (June 20, 2020): 4248. http://dx.doi.org/10.3390/app10124248.

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Structure-based and computer-aided drug design approaches are commonly considered to have been successful in the fields of cancer and antiviral drug discovery but not as much for antibacterial drug development. The search for novel anti-tuberculosis agents is indeed an emblematic example of this trend. Although huge efforts, by consortiums and groups worldwide, dramatically increased the structural coverage of the Mycobacterium tuberculosis proteome, the vast majority of candidate drugs included in clinical trials during the last decade were issued from phenotypic screenings on whole mycobacterial cells. We developed here three selected case studies, i.e., the serine/threonine (Ser/Thr) kinases—protein kinase (Pkn) B and PknG, considered as very promising targets for a long time, and the DNA gyrase of M. tuberculosis, a well-known, pharmacologically validated target. We illustrated some of the challenges that rational, target-based drug discovery programs in tuberculosis (TB) still have to face, and, finally, discussed the perspectives opened by the recent, methodological developments in structural biology and integrative techniques.

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Wang, Ge, Yuhao Bai, Jiarui Cui, Zirui Zong, Yuan Gao, and Zhen Zheng. "Computer-Aided Drug Design Boosts RAS Inhibitor Discovery." Molecules 27, no. 17 (September 5, 2022): 5710. http://dx.doi.org/10.3390/molecules27175710.

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The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

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Paiman, Arif, Ahmad Mohammad, and Mubashar Rehman. "Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction." Global Drug Design & Development Review II, no. I (December 30, 2017): 1–8. http://dx.doi.org/10.31703/gdddr.2017(ii-i).01.

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In modern day, Data on different diseases and drug substances with their properties like modification, side effects, and dose requires documentation data and building library exploring, such library with vast information in every aspect needs computational methods used in CADD. Recognition of specific targets for the drug tested and defining pharmacological activity of a drug candidate based on the structure of both drug and its target, finding outside effects of drugs at the molecular level and calculation of toxicity caused by metabolism of drug applications of Computer aided drug design in the drug discovery process. We can get additional tools and websites which serve As a tool for the source of data and computational drug design are available on the web interface and being used extensively by researchers and scientists to save time and budget for speeding up the process of experiments for Novel Drug compound.

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Ma, Jing. "The Application of Pattern Recognition Technology in Quantitative Drug Design." Advanced Materials Research 926-930 (May 2014): 3414–17. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3414.

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Quantitative design focuses on drugs between biological activity and structure parameters of quantitative change rule, so as to apply these rules to guide the design and synthesis of new drugs to predict unknown compounds of biological activity, agent theory and inference mechanism of drugs. This paper briefly introduces the concept of quantitative drug design and computer graphics and its typical applications in pattern recognition, quantitative drug design, and introduces a quantitative drug design system based on pattern recognition, finally will point out their application prospects and some problems to be solved. Quantitative drug design is of great significance for the diagnosis of the disease.

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Sachin S Padole, Alpana J Asnani, Dinesh R Chaple, and Soumya G Katre. "A review of approaches in computer-aided drug design in drug discovery." GSC Biological and Pharmaceutical Sciences 19, no. 2 (May 30, 2022): 075–83. http://dx.doi.org/10.30574/gscbps.2022.19.2.0161.

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The process of discovering and developing a new medication is often seen as a lengthy and expensive endeavors. As a result, computer-aided drug design methods are now frequently utilized to improve the efficiency of the drug discovery and development process. Various CADD approaches are regarded as potential techniques based on their needs; nevertheless, structure-based drug design and ligand-based drug design approaches are well-known as highly efficient and powerful strategies in drug discovery and development. Both of these approaches may be used in conjunction with molecular docking to conduct virtual screening for the purpose of identifying and optimizing leads. In recent years, computational tools have become increasingly popular in the pharmaceutical industry and academic fields as a means of improving the efficiency and effectiveness of the drug discovery and development pipeline. In this post, we'll go over computational methods, which are a creative way of discovering new leads and assisting in drug discovery and development research.

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Branson, Kim M., and Brian J. Smith. "The Role of Virtual Screening in Computer Aided Structure-Based Drug Design." Australian Journal of Chemistry 57, no. 11 (2004): 1029. http://dx.doi.org/10.1071/ch04161.

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The pharmaceutical industry has embraced computational methods to improve the successful negotiation of hits and leads into drugs in the clinic. This review examines the current status of in silico screening methods and aspects of compound library design.

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Barbany, Montserrat, Hugo Gutiérrez-de Terán, Ferran Sanz, and Jordi Villà-Freixa. "Towards a MIP-based alignment and docking in computer-aided drug design." Proteins: Structure, Function, and Bioinformatics 56, no. 3 (May 7, 2004): 585–94. http://dx.doi.org/10.1002/prot.20153.

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Namitha K N and V Velmurugan. "Review of bioinformatic tools used in Computer Aided Drug Design (CADD)." World Journal of Advanced Research and Reviews 14, no. 2 (May 30, 2022): 453–65. http://dx.doi.org/10.30574/wjarr.2022.14.2.0394.

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Drug discovery is а time consuming рrосess of finding out a new drug molecule. The process takes many years to complete and needs human resource. These is difficulties have been overcome by introducing computer programmes in drug discovery (CADD) which includes target identification, hit identification, and molecular modification of а lead compound to optimize desired effects and minimize side effects, based on the knowledge of their biological targets. Molecular modelling is the process of designing a molecule with a computer-based collection of programmes (in-silico design) for deriving, representing, and manipulating the structures and reactions of molecules. Numerous Software tools, online data bases and computer programmes are used in the field of CADD in which some relevant, user friendly and precise ones are reviewed in this article. Software is available for personal use and for commercial purposes. All these tools are highly useful in the field of drug design and discovery. The article will be helpful for selecting a tool for computer aided drug design.

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Farhadi, Tayebeh, and Seyed MohammadReza Hashemian. "Computer-aided design of amino acid-based therapeutics: a review." Drug Design, Development and Therapy Volume 12 (May 2018): 1239–54. http://dx.doi.org/10.2147/dddt.s159767.

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Nayarisseri, Anuraj. "Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery." Current Topics in Medicinal Chemistry 20, no. 19 (September 14, 2020): 1651–60. http://dx.doi.org/10.2174/156802662019200701164759.

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Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

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Bodor, N., P. Buchwald, and M. J. Huang. "Computer-Assisted Design of New Drugs Based on Retrometabolic Concepts." SAR and QSAR in Environmental Research 8, no. 1-2 (January 1998): 41–92. http://dx.doi.org/10.1080/10629369808033261.

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Reddy, R., Ravichandra Mutyala, P. Aparoy, P. Reddanna, and M. Reddy. "Computer Aided Drug Design Approaches to Develop Cyclooxygenase Based Novel Anti-Inflammatory and Anti-Cancer Drugs." Current Pharmaceutical Design 13, no. 34 (December 1, 2007): 3505–17. http://dx.doi.org/10.2174/138161207782794275.

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Patel, Jimish R., Hirak V. Joshi, Ujashkumar A. Shah, and Jayvadan K. Patel. "A Review on Computational Software Tools for Drug Design and Discovery." Indo Global Journal of Pharmaceutical Sciences 12 (2022): 53–81. http://dx.doi.org/10.35652/igjps.2022.12006.

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In the current era of modern drug design & development via computer-aided drug design, the potential role of computational software tools is widely enlarged in use. Computer-based drug design is revolutionary in the new drug discovery process because these processes are fast, time, and cost-saving with more efficient pharmacological activity. Computer-Based drug design is mainly applied for the drug-design and gets many successes in new drug research. There is plenty of software available in drug design; however; still, many issues are rising during its use. To clarify these issues, an attempt has been provided here in this article about the information about worldwide used 189 computation tools along with citation of software tools, download links, computer operative system and application of tools for available software such as Molecular modeling, docking, proteins conformation, pharmacophore mapping, ADMET, Docking pose visualization, force field calculation, homology modeling, 3D structure generator, Computational Crystallography, protein Database, and calculation software. This vital information enlightens all the software right from old to a recent one. Review article important for choice and application of wide-reaching used Drug Design software.©2022iGlobal Research and PublishingFoundation. All rights reserved.

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Patel, Preeti, Vijay K. Patel, Avineesh Singh, Talha Jawaid, Mehnaz Kamal, and Harish Rajak. "Identification of Hydroxamic Acid Based Selective HDAC1 Inhibitors: Computer Aided Drug Design Studies." Current Computer-Aided Drug Design 15, no. 2 (March 12, 2019): 145–66. http://dx.doi.org/10.2174/1573409914666180502113135.

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Background: Overexpression of Histone deacetylase 1 (HDAC1) is responsible for carcinogenesis by promoting epigenetic silence of tumour suppressor genes. Thus, HDAC1 inhibitors have emerged as the potential therapeutic leads against multiple human cancers, as they can block the activity of particular HDACs, renovate the expression of several tumour suppressor genes and bring about cell differentiation, cell cycle arrest and apoptosis. Methods: The present research work comprises atom-based 3D-QSAR, docking, molecular dynamic simulations and DFT (density functional theory) studies on a diverse series of hydroxamic acid derivatives as selective HDAC1 inhibitors. Two pharmacophoric models were generated and validated by calculating the enrichment factors with the help of the decoy set. The Four different 3D-QSAR models i.e., PLS (partial least square) model, MLR (multiple linear regression) model, Field-based model and GFA (Genetic function approximation) model were developed using ‘PHASE’ v3.4 (Schrödinger) and Discovery Studio (DS) 4.1 software and validated using different statistical parameters like internal and external validation. Results and Discussion: The results showed that the best PLS model has R2=0.991 and Q2=0.787, the best MLR model has R2= 0.993 and Q2= 0.893, the best Field-based model has R2= 0.974 and Q2= 0.782 and the best GFA model has R2= 0.868 and Q2= 0.782. Cross-validated coefficients, (rcv 2) of 0.967, 0.926, 0.966 and 0.829 was found for PLS model, MLR, Field based and GFA model, respectively, indicated the satisfactory correlativity and prediction. The docking studies were accomplished to find out the conformations of the molecules and their essential binding interactions with the target protein. The trustworthiness of the docking results was further confirmed by molecular dynamics (MD) simulations studies. Density Functional Theory (DFT) study was performed which promptly optimizes the geometry, stability and reactivity of the molecule during receptor-ligand interaction. Conclusion: Thus, the present research work provides spatial fingerprints which would be beneficial for the development of potent HDAC1 inhibitors.

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Kumar, Sanjiv. "ROLE OF COMPUTER-AIDED DRUG DESIGN IN THE DISCOVERY AND DEVELOPMENT OF NEW MEDICINAL AGENTS A REVIEW." Journal of medical pharmaceutical and allied sciences 11, no. 3 (June 30, 2022): 4794–801. http://dx.doi.org/10.55522/jmpas.v11i3.2300.

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Drug design and development is a time consuming and costly process. Nowadays, computer-aided drug design approaches are usually used to improve drug discovery and advancement efficiency. The role of Computer-Aided Drug Design (CADD) is a diverse discipline in which various versions of applied and basic analysis are interlinked. It is being implemented in various fields including biochemistry, molecular biology, nanotechnology etc. Various employed computational approaches includes ligand-based drug design, structure-based drug design, quantitative structure-property relationships and quantitative structure-activity. Computational techniques are commonly utilized in pharmaceutical industry and in research to improving the effectiveness of drug discovery and development. In this review, the authors have attempted to provide a broad overview of the function of CADD in modern medicine science.

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Groza, Vlad, Mihai Udrescu, Alexandru Bozdog, and Lucreţia Udrescu. "Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug–Gene Interactions." Pharmaceutics 13, no. 12 (December 8, 2021): 2117. http://dx.doi.org/10.3390/pharmaceutics13122117.

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Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug–gene interaction data. We obtained drug–gene interaction data from an earlier version of DrugBank, built a drug–gene interaction network, and projected it as a drug–drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug–gene interaction data to generate a comprehensive drug repurposing hint list.

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Kanwar, Gurtej, Anish Kumar, and Anshika Mahajan. "Open source software tools for computer aided drug design." International Journal of Research in Pharmaceutical Sciences 9, no. 1 (March 12, 2018): 86. http://dx.doi.org/10.26452/ijrps.v9i1.1191.

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Computer-aided drug design (CADD) has revolutionized the drug discovery arena and it has reduced the costs associated with finding novel compounds which are having pharmaceutical importance. In CADD, the scientists use the computer software to discover biological active compounds. Molecular docking and energy minimization tools are essential components of structure based drug design. It is a significant tool in structural molecular biology and computer-assisted drug design. It reduces the laboratory workload of the end user and allows researchers to restrict their docking studies to the smallest and the most representative set of macromolecules and small molecules possible. This greatly enhances the productivity of researchers. Energy minimization is an important criterion for selecting a potential 3D molecule. In modeled structures, the 3D structure is affected is due to steric clashes. These clashes happen in a protein structure due to the overlap of non bonding atoms and with the assistance of energy minimization, steric clashes can be eradicated. The open software’s and databases provides a platform for scientists and scholars to carry out their research work in a better way. The docking tools are discussed in this review cover protein-ligand, protein-peptide as well as protein-nucleic acid docking. The tools described include AutoDock 4 and Vina, UCSF DOCK, FLIPDock, EADock, HADDOCK 2.2, SwissDock, PatchDock and ClusPro. In addition to the docking tools, energy minimization tools such as YASARA minimization server, KoBaMIN server and 3D refine server have also been discussed. This mini-review concentrates on open software tools which are free of cost and can be easily downloaded in the computers that are useful for CADD. Keywords: Molecular docking; Energy minimization; Structure refinement; Drug design; CADD

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Sanyal, Saptarshi, Sk Abdul Amin, Nilanjan Adhikari, and Tarun Jha. "Ligand-based design of anticancer MMP2 inhibitors: a review." Future Medicinal Chemistry 13, no. 22 (November 2021): 1987–2013. http://dx.doi.org/10.4155/fmc-2021-0262.

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MMP2, a Zn2+-dependent metalloproteinase, is related to cancer and angiogenesis. Inhibition of this enzyme might result in a potential antimetastatic drug to leverage the anticancer drug armory. In silico or computer-aided ligand-based drug design is a method of rational drug design that takes multiple chemometrics (i.e., multi-quantitative structure–activity relationship methods) into account for virtually selecting or developing a series of probable selective MMP2 inhibitors. Though existing matrix metalloproteinase inhibitors have shown plausible pan-matrix metalloproteinase (MMP) activity, they have resulted in various adverse effects leading to their being rescinded in later phases of clinical trials. Therefore a review of the ligand-based designing methods of MMP2 inhibitors would result in an explicit route map toward successfully designing and synthesizing novel and selective MMP2 inhibitors.

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Zauhar, Randy J., Guillermo Moyna, LiFeng Tian, ZhiJian Li, and William J. Welsh. "Shape Signatures: A New Approach to Computer-Aided Ligand- and Receptor-Based Drug Design." Journal of Medicinal Chemistry 46, no. 26 (December 2003): 5674–90. http://dx.doi.org/10.1021/jm030242k.

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Shahin, Rand, Iman Mansi, Lubna Swellmeen, Tahani Alwidyan, Nabil Al-Hashimi, Yaser Al-Qarar’h, and Omar Shaheen. "Ligand-based computer aided drug design reveals new tropomycin receptor kinase a (TrkA) inhibitors." Journal of Molecular Graphics and Modelling 80 (March 2018): 327–52. http://dx.doi.org/10.1016/j.jmgm.2018.01.004.

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Heinke, Ralf, Luca Carlino, Srinivasaraghavan Kannan, Manfred Jung, and Wolfgang Sippl. "Computer- and structure-based lead design for epigenetic targets." Bioorganic & Medicinal Chemistry 19, no. 12 (June 2011): 3605–15. http://dx.doi.org/10.1016/j.bmc.2011.01.029.

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de Araújo, Rodrigo Santos Aquino, Edeildo Ferreira da Silva-Junior, Thiago Mendonça de Aquino, Marcus Tullius Scotti, Hamilton M. Ishiki, Luciana Scotti, and Francisco Jaime Bezerra Mendonça-Junior. "Computer-Aided Drug Design Applied to Secondary Metabolites as Anticancer Agents." Current Topics in Medicinal Chemistry 20, no. 19 (September 14, 2020): 1677–703. http://dx.doi.org/10.2174/1568026620666200607191838.

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: Computer-Aided Drug Design (CADD) techniques have garnered a great deal of attention in academia and industry because of their great versatility, low costs, possibilities of cost reduction in in vitro screening and in the development of synthetic steps; these techniques are compared with highthroughput screening, in particular for candidate drugs. The secondary metabolism of plants and other organisms provide substantial amounts of new chemical structures, many of which have numerous biological and pharmacological properties for virtually every existing disease, including cancer. In oncology, compounds such as vimblastine, vincristine, taxol, podophyllotoxin, captothecin and cytarabine are examples of how important natural products enhance the cancer-fighting therapeutic arsenal. : In this context, this review presents an update of Ligand-Based Drug Design and Structure-Based Drug Design techniques applied to flavonoids, alkaloids and coumarins in the search of new compounds or fragments that can be used in oncology. : A systematical search using various databases was performed. The search was limited to articles published in the last 10 years. : The great diversity of chemical structures (coumarin, flavonoids and alkaloids) with cancer properties, associated with infinite synthetic possibilities for obtaining analogous compounds, creates a huge chemical environment with potential to be explored, and creates a major difficulty, for screening studies to select compounds with more promising activity for a selected target. CADD techniques appear to be the least expensive and most efficient alternatives to perform virtual screening studies, aiming to selected compounds with better activity profiles and better “drugability”.

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Hasan, Md Rifat, Ahad Amer Alsaiari, Burhan Zain Fakhurji, Mohammad Habibur Rahman Molla, Amer H. Asseri, Md Afsar Ahmed Sumon, Moon Nyeo Park, Foysal Ahammad, and Bonglee Kim. "Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process." Molecules 27, no. 13 (June 29, 2022): 4169. http://dx.doi.org/10.3390/molecules27134169.

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The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure–activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

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Castro, Larissa Henriques Evangelista, and Carlos Mauricio R. Sant'Anna. "Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications." Current Topics in Medicinal Chemistry 22, no. 5 (February 2022): 333–46. http://dx.doi.org/10.2174/1568026621666211129140958.

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: Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional “one-target, one disease” paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice due to its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated with the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

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K. Ahmed, Shaimaa, Zobeda H. Naji, Yousif N. Hatif, and Meaad Hussam. "Design and Implementation of a Computerized Drug Inventory Management Information System Using ASP.NET MVC." Diyala Journal of Engineering Sciences 13, no. 4 (December 9, 2020): 80–90. http://dx.doi.org/10.24237/djes.2020.13410.

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Automation in the drug inventory distribution process is helpful to pharmacist. Pharmacy management has kept paper record in filing cabinets. Managing a very large pharmacy with records on papers will be tedious and difficult to keep track ofinventories with regards to the drugs in the store, expiry date, quantity of drugs available based on the categories and their functions. A Drug Inventory Management Information System(DIMIS) is basically a computer system that can manage all the information to allow pharmacists to do their jobs faster and more effective in real time. These jobs include: dispensing of drugs, drug regulation, and the sale of these drugs. The developed methodology adopted in the implementation of the software is iterative model of system development life cycle. This method produces an early stage of model. The development of the system is based on using new technologies like AJAX, ASP.NET MVC 5, Bootstrap, HTML and CSS which make the user interface more interactive. C#.NET language was used as server language whereas as a database server Microsoft SQL server 2012 was utilized. DIMIS is able to search and check a huge number and expiration date of drug in the purchase as well as delivery records. These attributes and functions are powerful, efficient and subsequently useful in patient's safety and cost containment

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Wang, Liuying, Yongzhen Song, Hesong Wang, Xuan Zhang, Meng Wang, Jia He, Shuang Li, Liuchao Zhang, Kang Li, and Lei Cao. "Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade." Pharmaceuticals 16, no. 2 (February 7, 2023): 253. http://dx.doi.org/10.3390/ph16020253.

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Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

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Garofalo, Mariangela, Giovanni Grazioso, Andrea Cavalli, and Jacopo Sgrignani. "How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs." Molecules 25, no. 7 (April 10, 2020): 1756. http://dx.doi.org/10.3390/molecules25071756.

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The early and late development of new anticancer drugs, small molecules or peptides can be slowed down by some issues such as poor selectivity for the target or poor ADME properties. Computer-aided drug design (CADD) and target drug delivery (TDD) techniques, although apparently far from each other, are two research fields that can give a significant contribution to overcome these problems. Their combination may provide mechanistic understanding resulting in a synergy that makes possible the rational design of novel anticancer based therapies. Herein, we aim to discuss selected applications, some also from our research experience, in the fields of anticancer small organic drugs and peptides.

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Hanessian, Stephen, and Nicolas Moitessier. "Sulfonamide-Based Acyclic and Conformationally Constrained MMP Inhibitors: From Computer-Assisted Design to Nanomolar Compounds." Current Topics in Medicinal Chemistry 4, no. 12 (August 1, 2004): 1269–87. http://dx.doi.org/10.2174/1568026043387953.

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Journal articles on the topic 'Computer-based drug design'

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