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Journal articles on the topic 'Ligand based drug design'

Author: Grafiati

Published: 12 August 2021

Last updated: 25 July 2025

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1

Solo, Peter, Sangdintuile Zeliang, Muluvelu Lohe, Avünü Neikha, and Akumsunep Jamir. "Structure-based Drug Design, ADME and Molecular Docking analyses of anti-viral agents against SARS-CoV-2 virus, Zika virus and Hepatitis C virus." Journal of Drug Delivery and Therapeutics 13, no. 7 (2023): 65–74. http://dx.doi.org/10.22270/jddt.v13i7.5909.

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Computer-aided drug design has been taking an increasing role in the field of modern drug discovery. These in silico computational methods are cost-effective, reduce the use of animal models in pharmacological research, and can be used to study pathogenic organisms without the need for any facilities. Based on the structure of known anti-viral agents, a total of 812 ligands have been designed. All ligands were screened for drug-likeness based on Lipinski rule of five. A database of ligands was constructed and in silico docking analyses were performed using MOE 2015.10 program against three sel

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Hajduk, Philip J., Robert P. Meadows, and Stephen W. Fesik. "NMR-based screening in drug discovery." Quarterly Reviews of Biophysics 32, no. 3 (1999): 211–40. http://dx.doi.org/10.1017/s0033583500003528.

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1. Introduction 2112. Screening methods 2132.1 Chemical shifts 2132.2 Diffusion 2142.3 Transverse relaxation 2182.4 Nuclear Overhauser effects 2183. Strategies for drug discovery and design 2213.1 Fragment-based methods 2213.1.1 Linked-fragment approach 2213.1.2 Directed combinatorial libraries 2223.1.3 Modification of high-affinity ligands 2233.1.4 Solvent mapping techniques 2233.2 High-throughput NMR-based screening 2243.3 Enzymatic assays 2264. Discovery of novel ligands 2274.1 High-affinity ligands for FKBP 2274.2 Potent inhibitors of stromelysin 2294.3 Ligands for the DNA-binding domain o

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Amitesh, Chakraborty Tushar Adhikari*. "The Basic Journey of A Molecule From Pharmacophore To Successful Drug Candidate By Computer Aided Drug Design – A Detailed Review." International Journal of Pharmaceutical Sciences 2, no. 7 (2024): 781–98. https://doi.org/10.5281/zenodo.12736660.

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Computer Aided Drug Design aims at developing <em>in – silico </em>or computer software-based techniques and methods to design and develop a drug molecule which have Pharmacological activity when binds to the desired target and have minimum side effect. For a drug to show desired biological effect, the drug target should be chosen with special emphasis such that normal body functioning does not get hampered. The bioactive conformer of the ligand molecule is chosen which have highest docking score and shows three point attachment to the receptor’s active site. Drug designing can be

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Heller, Markus, and Horst Kessler. "NMR spectroscopy in drug design." Pure and Applied Chemistry 73, no. 9 (2001): 1429–36. http://dx.doi.org/10.1351/pac200173091429.

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The process of preclinical drug discovery consists of two steps: finding of initial hits (binding ligands to a medicinal relevant target, usually a protein) and lead optimization. Nuclear magnetic resonance spectroscopy is a powerful tool that can provide valuable information to every step of drug development. NMR is commonly used for characterizing the structure and molecular dynamics of target or ligand molecules. During the structure-based lead optimization, NMR provides insight into the structural and dynamical properties of the target-ligand complex. Recently, the use of NMR in the lead f

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Barelier, Sarah, Julien Pons, Kalle Gehring, Jean-Marc Lancelin, and Isabelle Krimm. "Ligand Specificity in Fragment-Based Drug Design." Journal of Medicinal Chemistry 53, no. 14 (2010): 5256–66. http://dx.doi.org/10.1021/jm100496j.

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6

Kuhn, Bernd, Jens-Uwe Peters, Markus G. Rudolph, Peter Mohr, Martin Stahl, and Andreas Tosstorff. "Details Matter in Structure-based Drug Design." CHIMIA 77, no. 7/8 (2023): 489. http://dx.doi.org/10.2533/chimia.2023.489.

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Successful structure-based drug design (SBDD) requires the optimization of interactions with the target protein and the minimization of ligand strain. Both factors are often modulated by small changes in the chemical structure which can lead to profound changes in the preferred conformation and interaction preferences of the ligand. We draw from examples of a Roche project targeting phosphodiesterase 10 to highlight that details matter in SBDD. Data mining in crystal structure databases can help to identify these sometimes subtle effects, but it is also a great resource to learn about molecula

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Apostolakist, J., and A. Caflisch. "Computational Ligand Design." Combinatorial Chemistry & High Throughput Screening 2, no. 2 (1999): 91–104. http://dx.doi.org/10.2174/1386207302666220203193501.

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Abstract: A variety of computational tools that are used to assist drug design are reviewed. Particular emphasis is given to the limitations and merits of different methodologies. Recently, a number of general methods have been proposed for clustering compounds in classes of drug like and non-drug-like molecules. The usefulness of this classification for drug design is discussed. The estimation of (relative) binding affinities is from a theoretical point of view the most challenging part of ligand design. We review three methods for the estimation of binding energies. Firstly, quantitative st

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8

Zheng, Fang, and Chang-Guo Zhan. "Computational Modeling of Solvent Effects on Protein-Ligand Interactions Using Fully Polarizable Continuum Model and Rational Drug Design." Communications in Computational Physics 13, no. 1 (2013): 31–60. http://dx.doi.org/10.4208/cicp.130911.121011s.

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AbstractThis is a brief review of the computational modeling of protein-ligand interactions using a recently developed fully polarizable continuum model (FPCM) and rational drug design. Computational modeling has become a powerful tool in understanding detailed protein-ligand interactions at molecular level and in rational drug design. To study the binding of a protein with multiple molecular species of a ligand, one must accurately determine both the relative free energies of all of the molecular species in solution and the corresponding microscopic binding free energies for all of the molecu

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Loganathan, Lakshmanan, and Karthikeyan Muthusamy. "Current Scenario in Structure and Ligand-Based Drug Design on Anti-colon Cancer Drugs." Current Pharmaceutical Design 24, no. 32 (2019): 3829–41. http://dx.doi.org/10.2174/1381612824666181114114513.

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Worldwide, colorectal cancer takes up the third position in commonly detected cancer and fourth in cancer mortality. Recent progress in molecular modeling studies has led to significant success in drug discovery using structure and ligand-based methods. This study highlights aspects of the anticancer drug design. The structure and ligand-based drug design are discussed to investigate the molecular and quantum mechanics in anti-cancer drugs. Recent advances in anticancer agent identification driven by structural and molecular insights are presented. As a result, the recent advances in the field

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10

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 identific

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Yuan, Xiaojing, and Yechun Xu. "Recent Trends and Applications of Molecular Modeling in GPCR–Ligand Recognition and Structure-Based Drug Design." International Journal of Molecular Sciences 19, no. 7 (2018): 2105. http://dx.doi.org/10.3390/ijms19072105.

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G protein-coupled receptors represent the largest family of human membrane proteins and are modulated by a variety of drugs and endogenous ligands. Molecular modeling techniques, especially enhanced sampling methods, have provided significant insight into the mechanism of GPCR–ligand recognition. Notably, the crucial role of the membrane in the ligand-receptor association process has earned much attention. Additionally, docking, together with more accurate free energy calculation methods, is playing an important role in the design of novel compounds targeting GPCRs. Here, we summarize the rece

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12

Clark, David. "Ligand-based drug design in the AlphaFold age." Biomedical & Life Sciences Collection 2025, no. 6 (2025): e1006819. https://doi.org/10.69645/biuu5747.

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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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14

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

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15

Huang, Zhilin, Ling Yang, Zaixi Zhang, et al. "Binding-Adaptive Diffusion Models for Structure-Based Drug Design." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12671–79. http://dx.doi.org/10.1609/aaai.v38i11.29162.

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Structure-based drug design (SBDD) aims to generate 3D ligand molecules that bind to specific protein targets. Existing 3D deep generative models including diffusion models have shown great promise for SBDD. However, it is complex to capture the essential protein-ligand interactions exactly in 3D space for molecular generation. To address this problem, we propose a novel framework, namely Binding-Adaptive Diffusion Models (BindDM). In BindDM, we adaptively extract subcomplex, the essential part of binding sites responsible for protein-ligand interactions. Then the selected protein-ligand subco

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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

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Bacilieri, Magdalena, and Stefano Moro. "Ligand-Based Drug Design Methodologies in Drug Discovery Process: An Overview." Current Drug Discovery Technologies 3, no. 3 (2006): 155–65. http://dx.doi.org/10.2174/157016306780136781.

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18

Zhang, Changsheng, and Luhua Lai. "Towards structure-based protein drug design." Biochemical Society Transactions 39, no. 5 (2011): 1382–86. http://dx.doi.org/10.1042/bst0391382.

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Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein–protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein–ligand interactions, protein–protein interactions are more

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19

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 (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,

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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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21

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

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Májeková, Magdaléna. "Ligand-based drug design of novel aldose reductase inhibitors." Future Medicinal Chemistry 10, no. 21 (2018): 2493–96. http://dx.doi.org/10.4155/fmc-2018-0127.

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23

Barnickel, G. "A receptor–ligand database for structure-based drug design." Journal of Molecular Structure: THEOCHEM 463, no. 1-2 (1999): 4–5. http://dx.doi.org/10.1016/s0166-1280(99)00008-1.

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24

Mooij, Wijnand T M., Michael J Hartshorn, Ian J Tickle, Andrew J Sharff, Marcel L Verdonk, and Harren Jhoti. "Automated Protein–Ligand Crystallography for Structure-Based Drug Design." ChemMedChem 1, no. 8 (2006): 827–38. http://dx.doi.org/10.1002/cmdc.200600074.

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Luginina, А. P., A. N. Khnykin, P. А. Khorn, et al. "Rational drug design targeting G-protein-coupled receptors: ligand search and screening (review)." Biohimiâ 89, no. 5 (2024): 945–60. http://dx.doi.org/10.31857/s0320972524050158.

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G protein-coupled receptors (GPCRs) are transmembrane proteins that participate in most physiological processes and serve as key pharmacological targets. Recent advances in structural biology of GPCRs have enabled the development of drugs based on structure (Structure Based Drug Design, SBDD). SBDD utilizes information about the receptor– ligand complex to search for suitable compounds, expanding the chemical space of search without the need for experimental screening. In our review we include a description of Structural-base Virtual Screening (SBVS) of ligands to GPCRs and a description of me

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Mazanetz, Michael P., Charlotte H. F. Goode, and Ewa I. Chudyk. "Ligand- and Structure-Based Drug Design and Optimization using KNIME." Current Medicinal Chemistry 27, no. 38 (2020): 6458–79. http://dx.doi.org/10.2174/0929867326666190409141016.

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In recent years there has been a paradigm shift in how data is being used to progress early drug discovery campaigns from hit identification to candidate selection. Significant developments in data mining methods and the accessibility of tools for research scientists have been instrumental in reducing drug discovery timelines and in increasing the likelihood of a chemical entity achieving drug development milestones. KNIME, the Konstanz Information Miner, is a leading open source data analytics platform and has supported drug discovery endeavours for over a decade. KNIME provides a rich palett

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Dutkiewicz, Zbigniew, and Renata Mikstacka. "Structure-Based Drug Design for Cytochrome P450 Family 1 Inhibitors." Bioinorganic Chemistry and Applications 2018 (July 25, 2018): 1–21. http://dx.doi.org/10.1155/2018/3924608.

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Cytochromes P450 are a class of metalloproteins which are responsible for electron transfer in a wide spectrum of reactions including metabolic biotransformation of endogenous and exogenous substrates. The superfamily of cytochromes P450 consists of families and subfamilies which are characterized by a specific structure and substrate specificity. Cytochromes P450 family 1 (CYP1s) play a distinctive role in the metabolism of drugs and chemical procarcinogens. In recent decades, these hemoproteins have been intensively studied with the use of computational methods which have been recently devel

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Pradeepkiran, Jangampalli, and P. Reddy. "Structure Based Design and Molecular Docking Studies for Phosphorylated Tau Inhibitors in Alzheimer’s Disease." Cells 8, no. 3 (2019): 260. http://dx.doi.org/10.3390/cells8030260.

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The purpose of our study is to identify phosphorylated tau (p-tau) inhibitors. P-tau has recently received great interest as a potential drug target in Alzheimer’s disease (AD). The continuous failure of Aβ-targeted therapeutics recommends an alternative drug target to treat AD. There is increasing evidence and growing awareness of tau, which plays a central role in AD pathophysiology, including tangles formation, abnormal activation of phosphatases/kinases, leading p-tau aggregation in AD neurons. In the present study, we performed computational pharmacophore models, molecular docking, and si

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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 effici

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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 m

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Wilson, Gregory L., and Markus A. Lill. "Integrating structure-based and ligand-based approaches for computational drug design." Future Medicinal Chemistry 3, no. 6 (2011): 735–50. http://dx.doi.org/10.4155/fmc.11.18.

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Huang, Hung-Jin, Kuei-Jen Lee, Hsin Wei Yu, et al. "Structure-Based and Ligand-Based Drug Design for HER 2 Receptor." Journal of Biomolecular Structure and Dynamics 28, no. 1 (2010): 23–37. http://dx.doi.org/10.1080/07391102.2010.10507341.

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Zhang, Xu, Huiru Tang, Chaohui Ye, and Maili Liu. "Structure-based drug design: NMR-based approach for ligand–protein interactions." Drug Discovery Today: Technologies 3, no. 3 (2006): 241–45. http://dx.doi.org/10.1016/j.ddtec.2006.09.002.

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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,

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Kashyap, Aanchal, Pankaj Kumar Singh, and Om Silakari. "Counting on Fragment Based Drug Design Approach for Drug Discovery." Current Topics in Medicinal Chemistry 18, no. 27 (2019): 2284–93. http://dx.doi.org/10.2174/1568026619666181130134250.

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Fragment based drug design (FBDD) is a structure guided ligand design approach used in the process of drug discovery. It involves identification of low molecular weight fragments as hits followed by determination of their binding mode using X-ray crystallography and/or NMR spectroscopy. X-ray protein crystallography is one of the most sensitive biophysical methods used for screening and is least prone to false positives. It also provides detailed structural information of the protein–fragment complex at the atomic level. The retrieved binding information facilitates the optimization of fragmen

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Hung, Che-Lun, and Guan-Jie Hua. "Cloud Computing for Protein-Ligand Binding Site Comparison." BioMed Research International 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/170356.

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The proteome-wide analysis of protein-ligand binding sites and their interactions with ligands is important in structure-based drug design and in understanding ligand cross reactivity and toxicity. The well-known and commonly used software, SMAP, has been designed for 3D ligand binding site comparison and similarity searching of a structural proteome. SMAP can also predict drug side effects and reassign existing drugs to new indications. However, the computing scale of SMAP is limited. We have developed a high availability, high performance system that expands the comparison scale of SMAP. Thi

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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 v

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Munir, Anum, Shaukat I. Malik, and Khalid A. Malik. "De-Novo Ligand Design against Mutated Huntington Gene by Ligand-based Pharmacophore Modeling Approach." Current Computer-Aided Drug Design 16, no. 2 (2020): 134–44. http://dx.doi.org/10.2174/1573409915666181207104437.

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Background: Huntington's disease is characterized by three side effects, including motor disturbances, psychiatric elements, and intellectual weakness. The onset for HD has nonlinear converse associations with the number of repeat sequences of the polyglutamine mutations, so that younger patients have a tendency for longer repeats length. This HD variation is because of the development of a polyglutamine (CAG) repeats in the exon 1 of the Huntingtin protein. Methods: In the present study, a few derivatives utilized as a part of the treatment of HD, are used to create the pharmacophore model an

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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 con

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

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For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using

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León, Roberto, Jorge Soto-Delgado, Elizabeth Montero, and Matías Vargas. "Development of Computational Approaches with a Fragment-Based Drug Design Strategy: In Silico Hsp90 Inhibitors Discovery." International Journal of Molecular Sciences 22, no. 24 (2021): 13226. http://dx.doi.org/10.3390/ijms222413226.

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A semi-exhaustive approach and a heuristic search algorithm use a fragment-based drug design (FBDD) strategy for designing new inhibitors in an in silico process. A deconstruction reconstruction process uses a set of known Hsp90 ligands for generating new ones. The deconstruction process consists of cutting off a known ligand in fragments. The reconstruction process consists of coupling fragments to develop a new set of ligands. For evaluating the approaches, we compare the binding energy of the new ligands with the known ligands.

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Yadav, Brijesh Singh, and Vijay Tripathi. "Recent Advances in the System Biology-based Target Identification and Drug Discovery." Current Topics in Medicinal Chemistry 18, no. 20 (2018): 1737–44. http://dx.doi.org/10.2174/1568026618666181025112344.

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The enormous quantity of publicly available active chemical ligand and biological receptor data knowledge allows scientists to retreat several open questions by the analysis and systematic integration of these complex unique data. Systems biology plays a crucial role through the constructive alignment of bio-physiochemical monitoring of gene, protein along with metabolites from the complex data. Further, it integrates information within the data and responses (metabolic and signaling pathway) which lead to the formulation of computational models for the elucidation of structure and function of

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Khorn, P. A., A. P. Luginina, V. A. Pospelov, et al. "Rational drug design targeting g-protein-coupled receptors: a structural biology perspective." Biohimiâ 89, no. 4 (2024): 705–25. http://dx.doi.org/10.31857/s0320972524040124.

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G protein-coupled Receptors (G protein-coupled Receptors, GPCRs) play a key role in the transmission of extracellular signals and regulation of many biological processes, which makes these membrane proteins one of the most important classes of targets for pharmacological agents. The significant increase in the number of atomic structures of GPCRs recently has paved the way for Structure Based Drug Design (SBDD). SBDD uses information on the structure of the receptor-ligand complex to search for affinity and selective ligands without the need for high-throughput experimental ligand screening an

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Deane, Charlotte M., Ian D. Wall, Darren V. S. Green, Brian D. Marsden, and Anthony R. Bradley. "WONKAandOOMMPPAA: analysis of protein–ligand interaction data to direct structure-based drug design." Acta Crystallographica Section D Structural Biology 73, no. 3 (2017): 279–85. http://dx.doi.org/10.1107/s2059798316009529.

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In this work, two freely available web-based interactive computational tools that facilitate the analysis and interpretation of protein–ligand interaction data are described. Firstly,WONKA, which assists in uncovering interesting and unusual features (for example residue motions) within ensembles of protein–ligand structures and enables the facile sharing of observations between scientists. Secondly,OOMMPPAA, which incorporates protein–ligand activity data with protein–ligand structural data using three-dimensional matched molecular pairs.OOMMPPAAhighlights nuanced structure–activity relations

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Panda, Debashis, Abhishek Chowdhury, Monjur Ahmed Laskar, and Manabendra Dutta Choudhury. "Molecular designing and Virtual Screening Based Drug design for MABA Enzyme of Mycobacterium Tuberculosis." Pakistan Journal of Medical and Health Sciences 16, no. 7 (2022): 870–72. http://dx.doi.org/10.53350/pjmhs22167870.

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Objective: Computer Aided Drug Designing of the NAP binding site of MabA enzymes. Methodology: Mycobacterium tuberculosis causes global infectious disease tuberculosis, which has remained among the top 10 causes of death worldwide. The emerged multidrug-resistant TB (MDR TB) and extensively drug-resistant TB (XDR TB) makes this problem more complex. β-ketoacyl-ACP reductase (MabA), a member of the type-II fatty acid elongation system (FAS-II) is primarily involved in the creation of very long chain fatty acid derivatives. These derivatives are essential precursors for mycolic acids, primary co

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Sperandio, Olivier, Maria Miteva, and Bruno Villoutreix. "Combining Ligand- and Structure-Based Methods in Drug Design Projects." Current Computer Aided-Drug Design 4, no. 3 (2008): 250–58. http://dx.doi.org/10.2174/157340908785747447.

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Carbone, Jacopo, Alessia Ghidini, Antonio Romano, Luca Gentilucci, and Francesco Musiani. "PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results." Molecules 27, no. 20 (2022): 6884. http://dx.doi.org/10.3390/molecules27206884.

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Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file f

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Krimm, Isabelle. "INPHARMA-based identification of ligand binding site in fragment-based drug design." MedChemComm 3, no. 5 (2012): 605. http://dx.doi.org/10.1039/c2md20035j.

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Defelipe, Lucas, Juan Arcon, Carlos Modenutti, Marcelo Marti, Adrián Turjanski, and Xavier Barril. "Solvents to Fragments to Drugs: MD Applications in Drug Design." Molecules 23, no. 12 (2018): 3269. http://dx.doi.org/10.3390/molecules23123269.

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Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognit

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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 high

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