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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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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 (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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Chaitali, Ingawale* Sandhya Khomane Rupali Kharat* Shrushti Uchale. "Computer Aided and AI based Drug Design." International Journal of Pharmaceutical Sciences, no. 12 (December 16, 2024): 2222–34. https://doi.org/10.5281/zenodo.14498665.
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Pharmaceutical drug discovery is an expensive and time-consuming process. The development of a drug from an initial idea to its entry into the market is a very complex process which can take around 5-10 yrs. and cost is very high upto billion. It is an development process involves use of variety of computational techniques, such as structure activity relationship, quantitative structure activity relationship, molecular mechanics, quantam mechanics, molecular dynamics and drug protein docking. The idea for a new development can come from a variety of sources which include the current necessities of the market, new emerging diseases, academic and clinical research, commercial sector. The pharmaceutical industry is under pressure in developing cost effectiveness drug molecule from the previous knowledge and established Quantitative Structure Activity Relationships. The structure-based design is one of reliable and promising techniques used in drug designing. In drug design, the main aim is to find out the three-dimensional structure of pharmacologically significant receptor ligand complexes. The aim of this review is to give an overview on the rational drug design approaches with a case study on drug discovery for influenza A virus, HER2 Receptor, targeting dopamine D3 receptor , purpose , and applications of QSAR. This article highlights the benefits and promises of developing tools for drug discovery.
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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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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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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 (2007): 133–48. http://dx.doi.org/10.2174/157340907780809516.
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Ejalonibu, Murtala A., Segun A. Ogundare, Ahmed A. Elrashedy, et al. "Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach." International Journal of Molecular Sciences 22, no. 24 (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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Suzuki, E., T. Akutsu, and S. Ohsuga. "Knowledge-based system for computer-aided drug design." Knowledge-Based Systems 6, no. 2 (1993): 114–26. http://dx.doi.org/10.1016/0950-7051(93)90026-p.
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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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Apurva, Patel1 Astha Sachdeva2. "Computer Aided Drug Design." International Journal of Pharmaceutical Sciences 3, no. 5 (2025): 2645–51. https://doi.org/10.5281/zenodo.15432398.
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Computer-Aided Drug Design (CADD) has emerged as a transformative approach in pharmaceutical research, integrating computational tools and molecular modeling techniques to accelerate and optimize the drug discovery process. This review provides a comprehensive overview of the fundamental principles, methodologies, and applications of CADD, including structure-based and ligand-based drug design, molecular docking, pharmacophore modeling, and virtual screening. Advances in bioinformatics, artificial intelligence, and high- performance computing have significantly enhanced the accuracy and efficiency of CADD, enabling the identification of novel therapeutic candidates with improved specificity and reduced development costs. Key case studies illustrating successful drug discoveries aided by CADD are discussed, highlighting its growing impact in both academia and industry. The review also addresses current challenges, such as the limitations of predictive models and the need for better integration with experimental data, while exploring future directions for innovation in this rapidly evolving field.
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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 (2005): 2457–68. http://dx.doi.org/10.1021/jm0492296.
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Pliushcheuskaya, Palina, and Georg Künze. "Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels." International Journal of Molecular Sciences 24, no. 11 (2023): 9226. http://dx.doi.org/10.3390/ijms24119226.
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Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.
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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 (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 (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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Surabhi, Surabhi, and BK Singh. "COMPUTER AIDED DRUG DESIGN: AN OVERVIEW." Journal of Drug Delivery and Therapeutics 8, no. 5 (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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Yu, Wenye, and Zhenyu Chen. "Computer Aided Drug Design Based on Artificial Intelligence Algorithm." Journal of Physics: Conference Series 2066, no. 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 (2005): 649–63. http://dx.doi.org/10.1038/nrd1799.
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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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Pranali R. Bhujade, Khemutai G. Shedame, Pooja R. Hatwar, Dr. Ravindra L. Bakal, Krushnali N. Nehar, and Ankita Y.Gawai. "A Review on Computer Aided Drug Design – In Silico." Asian Journal of Pharmaceutical Research and Development 12, no. 6 (2024): 80–85. https://doi.org/10.22270/ajprd.v12i6.1467.
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The process of drug discovery takes a long time and costs a lot of money. Computer-Aided Drug Design (CADD) has become an important part of modern drug discovery because it speeds up the process and lowers prices. CADD includes many methods, such as Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD). These use computer programs to do things like molecular docking, virtual screening, QSAR, pharmacophore modeling, and molecular dynamics. LBDD is used when the shapes of receptors are unknown, while SBDD uses machine learning. This review provides a comprehensive overview of CADD methods, classification, principles, and uses in drug creation. The article discusses about how important it is to find targets, find lead compounds, and make things work better. It also talks about the role of computers in pharmaceutical chemistry and molecular biology. CADD has increased the speed and accuracy of drug finding, making it possible to find new medicines. The review shows how CADD could change the way drugs are made, help people who don't have access to proper medical care, and make patient results better. Researchers can speed up the process of finding new drugs by using CADD strategies. This review is a great resource for researchers, clinicians, and industry workers who want to use CADD in pharmaceutical research. Using CADD has changed the way drugs are found, and its continued growth could lead to better health for everyone.
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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 (2002): 196–205. http://dx.doi.org/10.1016/s1477-3627(02)02274-2.
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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 (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 (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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Pradnya, Prashant Shinde Monika Gopal Shinde. "Fragment Based Drug Design: A Review." International Journal in Pharmaceutical Sciences 2, no. 7 (2024): 171–76. https://doi.org/10.5281/zenodo.12610829.
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Effective small molecule development can be achieved through the use of fragment based drug design. Several powerful compounds with a wide range of targets have been created with this method. Finding tiny chemical fragments that bind to biological targets and can then be refined to resemble lead molecules is known as ‘fragment-based drug design’. The methods for identifying fragments, screening them, and turning them into hits or leads—including fragment growth, fragment linking, fragment self-assembly, and targeted libraries—are covered in this article. FBDD will be more crucial to drug development since it is easily conducted using a variety of biophysical and computer-based techniques.
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Sachin, S. Padole, J. Asnani Alpana, R. Chaple Dinesh, and G. Katre Soumya. "A review of approaches in computer-aided drug design in drug discovery." GSC Biological and Pharmaceutical Sciences 19, no. 2 (2022): 075–83. https://doi.org/10.5281/zenodo.6627446.
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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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Aditee, Kagde* Dr. Mrunal Shirsat Anjali Zende. "Drug Design: A Comprehensive Review." International Journal of Pharmaceutical Sciences 3, no. 1 (2025): 2548–52. https://doi.org/10.5281/zenodo.14774296.
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Drug design is a systematic and innovative process aimed at creating pharmaceutical compounds that interact with biological targets to treat or manage diseases. With recent advancements in computational biology and artificial intelligence, traditional methods of drug discovery are now complemented by highly efficient Computer-Aided Drug Design (CADD). This evolution has minimized resource expenditure, accelerated timelines, and enabled the exploration of complex disease mechanisms. This review elaborates on the principles, methodologies, applications, and the role of advanced software in drug design. Furthermore, it outlines the integration of structure-based, ligand-based, and hybrid approaches, with a focus on their contributions to modern healthcare.
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Szarecka, Agnieszka, and Christopher Dobson. "Protein Structure Analysis: Introducing Students to Rational Drug Design." American Biology Teacher 81, no. 6 (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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Natolotriniavo Tendrinarisoa RANDRIAMAMISOLONIRINA, Olivier Fridolin MAMINIAINA, Andriambandaina Abel ANDRIANTSIMAHAVANDY, and Mirantsoa Suzanne RAZAFINDRAFARA. "Application of computer-aided drug design in drug discovery and development: Updating knowledge." GSC Advanced Research and Reviews 21, no. 1 (2024): 209–27. http://dx.doi.org/10.30574/gscarr.2024.21.1.0360.
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Coronavirus (CoV) diseases are widespread throughout the world and have caused considerable socio-economic disruptions. For this reason, efforts have been made to develop a direct or indirect antiviral drugs against these diseases. However, no specific antiviral drug has yet been approved by the Food and Drug Administration (FDA) for CoV infections. Thus, the challenge in discovering therapeutic molecules against these infections remains pertinent. Computer-aided drug design (CADD) is one of the modern techniques for drug discovery and development. It accelerates the process, minimizes costs, and reduces research time. In this article, we present the three CADD approaches, namely structure-based drug discovery (SBDD), ligand-based drug discovery (LBDD) and high-throughput virtual screening (HTVS). The different methods used in these three approaches CADD, such as molecular modelling, target structure analysis, molecular docking, molecular dynamics simulation, pharmacophore modelling, quantitative structure-activity relationship (QSAR), ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) are detailed. In addition, the bioinformatics tools and databases commonly used in these different CADD techniques are also described.
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P.L.Sujatha, K.Anbu Kumar, P.Devendran, S.P.Preetha, and Manikkavasagan Ilangopathy3. "APPLICATION OF COMPUTATIONAL METHODS IN DRUG DISCOVERY." Indian Journal of Veterinary and Animal Sciences Research 53, no. 5 (2025): 1–8. https://doi.org/10.56093/ijvasr.v53i5.161975.
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Rational drug design, is the inventive process of finding new medications based on knowledge of the biological target. Drug design involves the design of small molecules that are complementary in shape and charge to the bimolecular target to which they interact and therefore will bind to it. In the experiment based approach, drugs are discovered through trial and error. With high R&D cost and consumption, computational drug discovery helps scientists gain insight into drug receptor interactions and reduce time and cost. Scientists can predict whether the molecule will succeed or fail in the market. Currently, the process of drug designing increasingly relies on computer modeling techniques. This type of modeling is often referred to as computer-aided drug design. In computational drug discovery, different computational tools, methods, and software are used to simulate drug receptor interactions. Using computational drug discovery helps scientists gain insight into drug receptor interactions with less time and cost.
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Oli, Bharti. "Revolutionizing Drug Discovery: A Comprehensive Review of Computer-Aided Drug Design Approaches." International Journal for Research in Applied Science and Engineering Technology 12, no. 7 (2024): 308–17. http://dx.doi.org/10.22214/ijraset.2024.63563.
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Abstract: Computer-Aided Drug Design (CADD) has significantly advanced the drug discovery process, offering tools to enhance efficiency and reduce costs. This review explores essential CADD methodologies, including molecular docking, virtual screening, ADMET profiling, homology modeling, and Quantitative Structure-Activity Relationship (QSAR) models. Molecular docking predicts interactions between drugs and targets, while virtual screening evaluates large compound libraries to identify promising candidates. ADMET profiling assesses pharmacokinetic and toxicological properties early in development. Homology modeling constructs three-dimensional protein models to aid target identification, and QSAR models predict biological activities based on chemical structures. These integrated approaches streamline drug development, providing a robust framework for modern pharmaceutical research.
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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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Poroikov, V. V. "Computer-aided drug design: from discovery of novel pharmaceutical agents to systems pharmacology." Biomeditsinskaya Khimiya 66, no. 1 (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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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 (2011): 45–51. http://dx.doi.org/10.1021/ed200151b.
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Daina, Antoine, Marie-Claude Blatter, Vivienne Baillie Gerritsen, et al. "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 (2017): 335–44. http://dx.doi.org/10.1021/acs.jchemed.6b00596.
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Farber, B.S., A.V. Martynov, and I.R. Kleyn. "NEW DISCOVERIES OF PHARMACEUTICAL DRUGS BASED ON TRIZ AND COMPUTER MATHEMATICAL MODELING CREATION OF NEW MEDICAL DRUGS BASED ON TRIZ AND COMPUTER MATHEMATICAL MODELING." Annals of Mechnikov Institute, no. 4 (January 23, 2019): 15–34. https://doi.org/10.5281/zenodo.2547445.
Full textAbstract:
The article provides an overview of the current state of the use of TRIZ in the pharmaceutical industry and our R&D efforts in that area, based on TRIZ and computer mathematical modeling. Drug development is one of the most important research areas, which affects almost every family, and each one of us. However, nobody in the world has used TRIZ as a philosophy of solving problems in such important area as pharmaceutical research and development to develop new efficient medical drugs. The application of the principles of TRIZ in this arena opens up broad prospects in the creation of new classes of drugs that can independently adapt to the patient's body. The combination of contradictions, laws of development systems, algorithms, Su-field analysis, TRIZ principles, deep fundamentals of pharmaceutical industry and pharmacology, modern computer mathematical modeling, in the solution of each of the tasks at once, allows us to achieve extraordinary results and obtain significantly more effective novel drugs. For the first time in the World we have developed dynamic self-organizing, quasi live drugs, based on the principles of TRIZ and computerized mathematical modeling. These are drugs capable of adapting independently both to the human body and to molecular targets, including viruses, cancer cells and microorganisms. We have created 17 new projects, however, in this article we illustrate just 6 examples from our research and developments: 1. Novel directions to fight multidrug resistant microorganisms. 2. Polymyxin with reduced nephrotoxicity. 3. Dynamic drugs: Dynamic insulin. 4. Dynamic drugs: The dynamic anticancer drug Target-R to treat different cancers. 5. Dynamic drugs: Dynamic antiviral drug Albuvir. 6. Dynamic drugs: Hemostatic Gemma. Applying TRIZ and mathematical modeling in pharmaceutical industry, produces novel and future R&D trends. The proposed new paradigm of combating infectious diseases using TRIZ led to the creation of a unique pharmaceutical composition. The molecular modeling approach led to the intensification of research and for synthesis of drugs based on simulated inhibitor profiles. This increased the yield of novel dynamic drugs. The dynamic drugs can overcome many problems from resistance to the slippage effect, to eliminate the side effects of drugs. This will save millions of lives. We deeply integrated TRIZ and computer mathematical modeling in our R&D. In addition, our approach includes the application of the laws of quantum physics and quantum chemistry; additionally, knowledge of the behavior of molecules in different solutions and their interaction with each other at different temperatures, in the presence of salts and other compounds. Really effective drugs can be developed only on the basis of a systematic approach and in-depth knowledge in the fields of medical, pharmaceutical physical chemistry, analytical chemistry and pharmacognosy, chemistry of natural compounds, plant medicine technology, biochemistry and molecular biology, pharmacology and many other disciplines. Modeling these processes requires a large amount of not only computer time, but also knowledge in a number of broad areas: from quantum physics and chemistry to synthetic organic chemistry, in order to synthesize engineered substances. Despite changes in the concept of drug development: from banal screening (out of thousands of synthesized compounds, only one showed biological activity) to those obtained as a result of molecular modeling (another name is drug-design). (named as drug-design). The approach with the use of molecular modeling led to the intensification of research - to the synthesis of drugs based on simulated inhibitor profiles. This increased the yield of drugs - out of every hundreds of the synthesized substances, one showed the expected activity. The cost of pharmaceutical development software is currently quite high and can even reach tens of millions of dollars. But this is a reasonable amount, which makes it possible to obtain the required pharmaceutical preparations, at least for known target proteins. However, for the design of drugs of new generations at all stages of development - from building a model of a target protein to creating a drug profile and its synthesis, TRIZ has not been used systematically. Pharmaceutical industry is a huge area to be explored by TRIZ.
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Niazi, Sarfaraz K., and Zamara Mariam. "Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis." Pharmaceuticals 17, no. 1 (2023): 22. http://dx.doi.org/10.3390/ph17010022.
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In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.
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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 (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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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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Bruch, Eduardo M., Stéphanie Petrella, and Marco Bellinzoni. "Structure-Based Drug Design for Tuberculosis: Challenges Still Ahead." Applied Sciences 10, no. 12 (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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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 (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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Namitha, K. N., and Velmurugan V. "Review of bioinformatic tools used in Computer Aided Drug Design (CADD)." World Journal of Advanced Research and Reviews 14, no. 2 (2022): 453–65. https://doi.org/10.5281/zenodo.7298898.
Full textAbstract:
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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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 (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 (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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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 (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 (2004): 585–94. http://dx.doi.org/10.1002/prot.20153.
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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 (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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Farber, B.S., A.V. Martynov, and I.R. Kleyn. "CREATION OF NEW MEDICAL DRUGS BASED ON TRIZ AND COMPUTER MATHEMATICAL MODELING." Annals of Mechnikov Institute, no. 4 (December 12, 2018): 15–34. https://doi.org/10.5281/zenodo.2547580.
Full textAbstract:
The article provides an overview of the current state of the use of TRIZ in the pharmaceutical industry and our R&D efforts in that area, based on TRIZ and computer mathematical modeling. Drug development is one of the most important research areas, which affects almost every family, and each one of us. However, nobody in the world has used TRIZ as a philosophy of solving problems in such important area as pharmaceutical research and development to develop new efficient medical drugs. The application of the principles of TRIZ in this arena opens up broad prospects in the creation of new classes of drugs that can independently adapt to the patient's body. The combination of contradictions, laws of development systems, algorithms, Su-field analysis, TRIZ principles, deep fundamentals of pharmaceutical industry and pharmacology, modern computer mathematical modeling, in the solution of each of the tasks at once, allows us to achieve extraordinary results and obtain significantly more effective novel drugs. For the first time in the World we have developed dynamic self-organizing, quasi live drugs, based on the principles of TRIZ and computerized mathematical modeling. These are drugs capable of adapting independently both to the human body and to molecular targets, including viruses, cancer cells and microorganisms. We have created 17 new projects, however, in this article we illustrate just 6 examples from our research and developments: 1. Novel directions to fight multidrug resistant microorganisms. 2. Polymyxin with reduced nephrotoxicity. 3. Dynamic drugs: Dynamic insulin. 4. Dynamic drugs: The dynamic anticancer drug Target-R to treat different cancers. 5. Dynamic drugs: Dynamic antiviral drug Albuvir. 6. Dynamic drugs: Hemostatic Gemma. Applying TRIZ and mathematical modeling in pharmaceutical industry, produces novel and future R&D trends. The proposed new paradigm of combating infectious diseases using TRIZ led to the creation of a unique pharmaceutical composition. The molecular modeling approach led to the intensification of research and for synthesis of drugs based on simulated inhibitor profiles. This increased the yield of novel dynamic drugs. The dynamic drugs can overcome many problems from resistance to the slippage effect, to eliminate the side effects of drugs. This will save millions of lives. We deeply integrated TRIZ and computer mathematical modeling in our R&D. In addition, our approach includes the application of the laws of quantum physics and quantum chemistry; additionally, knowledge of the behavior of molecules in different solutions and their interaction with each other at different temperatures, in the presence of salts and other compounds. Really effective drugs can be developed only on the basis of a systematic approach and in-depth knowledge in the fields of medical, pharmaceutical physical chemistry, analytical chemistry and pharmacognosy, chemistry of natural compounds, plant medicine technology, biochemistry and molecular biology, pharmacology and many other disciplines. Modeling these processes requires a large amount of not only computer time, but also knowledge in a number of broad areas: from quantum physics and chemistry to synthetic organic chemistry, in order to synthesize engineered substances. Despite changes in the concept of drug development: from banal screening (out of thousands of synthesized compounds, only one showed biological activity) to those obtained as a result of molecular modeling (another name is drug-design). (named as drug-design). The approach with the use of molecular modeling led to the intensification of research - to the synthesis of drugs based on simulated inhibitor profiles. This increased the yield of drugs - out of every hundreds of the synthesized substances, one showed the expected activity. The cost of pharmaceutical development software is currently quite high and can even reach tens of millions of dollars. But this is a reasonable amount, which makes it possible to obtain the required pharmaceutical preparations, at least for known target proteins. However, for the design of drugs of new generations at all stages of development - from building a model of a target protein to creating a drug profile and its synthesis, TRIZ has not been used systematically. Pharmaceutical industry is a huge area to be explored by TRIZ.
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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 (2007): 3505–17. http://dx.doi.org/10.2174/138161207782794275.
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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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