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Journal articles on the topic 'Ligand-based design'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Stalke, D. "Charge density based ligand design." Acta Crystallographica Section A Foundations of Crystallography 64, a1 (2008): C69. http://dx.doi.org/10.1107/s010876730809778x.

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2

Kargbo, Robert B. "Ligand Design for Cereblon Based Immunomodulatory Therapy." ACS Medicinal Chemistry Letters 11, no. 6 (2020): 1088–89. http://dx.doi.org/10.1021/acsmedchemlett.0c00214.

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3

Joseph-McCarthy, D. "Computational approaches to structure-based ligand design." Pharmacology & Therapeutics 84, no. 2 (1999): 179–91. http://dx.doi.org/10.1016/s0163-7258(99)00031-5.

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4

Böhm, Hans-Joachim. "Computational tools for structure-based ligand design." Progress in Biophysics and Molecular Biology 66, no. 3 (1996): 197–210. http://dx.doi.org/10.1016/s0079-6107(97)00005-9.

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5

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

Katz, Bradley A., Robert T. Cass, Beishan Liu, Rafael Arze, and Nathan Collins. "Topochemical Catalysis Achieved by Structure-based Ligand Design." Journal of Biological Chemistry 270, no. 52 (1995): 31210–18. http://dx.doi.org/10.1074/jbc.270.52.31210.

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7

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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8

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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9

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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10

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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11

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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12

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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13

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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14

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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15

Karoli, Tomislav, Bernd Becker, Johannes Zuegg, et al. "Identification of Antitubercular Benzothiazinone Compounds by Ligand-Based Design." Journal of Medicinal Chemistry 55, no. 17 (2012): 7940–44. http://dx.doi.org/10.1021/jm3008882.

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16

Alberg, D., and S. Schreiber. "Structure-based design of a cyclophilin-calcineurin bridging ligand." Science 262, no. 5131 (1993): 248–50. http://dx.doi.org/10.1126/science.8211144.

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17

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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18

Sem, Daniel S., Bonnie Bertolaet, Brian Baker, et al. "Systems-Based Design of Bi-Ligand Inhibitors of Oxidoreductases." Chemistry & Biology 11, no. 2 (2004): 185–94. http://dx.doi.org/10.1016/j.chembiol.2004.02.012.

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19

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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20

Abubakar, Oluwafemi David, and Thomas Hamann. "Ligand Design for Enhanced Ruthenium-Based Electrocatalytic Ammonia Oxidation." ECS Meeting Abstracts MA2025-01, no. 52 (2025): 2589. https://doi.org/10.1149/ma2025-01522589mtgabs.

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The electrocatalytic oxidation of ammonia remains a critical challenge in energy and environmental technologies, with current molecular catalysts struggling with high overpotentials, limited stability, and low turnover frequencies. Since the first molecular catalyst, [Ru(tpy)(dmabpy)NH3)]2+, was reported in 2019, there has been progress in investigating alternative ligand frameworks and transition metal centers to better understand and control the reaction. This presentation will highlight a novel strategy for improving the electrocatalytic performance by systematically optimizing ligand bite

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21

Speck-Planche, A., F. Luan, and M. N.D.S. Cordeiro. "Role of Ligand-Based Drug Design Methodologies toward the Discovery of New Anti- Alzheimer Agents: Futures Perspectives in Fragment-Based Ligand Design." Current Medicinal Chemistry 19, no. 11 (2012): 1635–45. http://dx.doi.org/10.2174/092986712799945058.

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22

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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23

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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24

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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25

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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26

Posy, Shana L., Brian L. Claus, Matt E. Pokross, and Stephen R. Johnson. "3D Matched Pairs: Integrating Ligand- and Structure-Based Knowledge for Ligand Design and Receptor Annotation." Journal of Chemical Information and Modeling 53, no. 7 (2013): 1576–88. http://dx.doi.org/10.1021/ci400201k.

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27

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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28

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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29

Foloppe, N., and R. Hubbard. "Towards Predictive Ligand Design With Free-Energy Based Computational Methods?" Current Medicinal Chemistry 13, no. 29 (2006): 3583–608. http://dx.doi.org/10.2174/092986706779026165.

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30

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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31

Khalaf, Reema A., Dalal Masalha, and Dima Sabbah. "DPP-IV Inhibitory Phenanthridines: Ligand, Structure-Based Design and Synthesis." Current Computer-Aided Drug Design 16, no. 3 (2020): 295–307. http://dx.doi.org/10.2174/1573409915666181211114743.

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Background: Lately, diabetes has become the main health concern for millions of people around the world. Dipeptidyl peptidase-IV (DPP-IV) inhibitors have emerged as a new class of oral antidiabetic agents. Formerly, acridines, N4-sulfonamido-succinamic, phthalamic, acrylic and benzoyl acetic acid derivatives, and sulfamoyl-phenyl acid esters were designed and developed as new DPP-IV inhibitors. Objective: This study aims to develop a pharmacophore model of DPP-IV inhibitors and to evaluate phenanthridines as a novel scaffold for inhibiting DPP-IV enzyme. In addition, to assess their binding in

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32

McPhillie, Martin J., Rachel Trowbridge, Katherine R. Mariner, et al. "Structure-Based Ligand Design of Novel Bacterial RNA Polymerase Inhibitors." ACS Medicinal Chemistry Letters 2, no. 10 (2011): 729–34. http://dx.doi.org/10.1021/ml200087m.

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33

Larsson, E. Andreas, Anna Jansson, Fui Mee Ng, et al. "Fragment-Based Ligand Design of Novel Potent Inhibitors of Tankyrases." Journal of Medicinal Chemistry 56, no. 11 (2013): 4497–508. http://dx.doi.org/10.1021/jm400211f.

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34

Dey, Fabian, and Amedeo Caflisch. "Fragment-Based de Novo Ligand Design by Multiobjective Evolutionary Optimization." Journal of Chemical Information and Modeling 48, no. 3 (2008): 679–90. http://dx.doi.org/10.1021/ci700424b.

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35

Chandramohan, Arun, Nikhil K. Tulsian, and Ganesh S. Anand. "Dissecting Orthosteric Contacts for a Reverse-Fragment-Based Ligand Design." Analytical Chemistry 89, no. 15 (2017): 7876–85. http://dx.doi.org/10.1021/acs.analchem.7b00587.

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36

Monte, Isabel, Mats Hamberg, Andrea Chini, et al. "Rational design of a ligand-based antagonist of jasmonate perception." Nature Chemical Biology 10, no. 8 (2014): 671–76. http://dx.doi.org/10.1038/nchembio.1575.

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37

Almahmoud, Suliman, Wei Jin, Liying Geng, et al. "Ligand-based design of GLUT inhibitors as potential antitumor agents." Bioorganic & Medicinal Chemistry 28, no. 7 (2020): 115395. http://dx.doi.org/10.1016/j.bmc.2020.115395.

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38

Bohari, Mohammad H., Xing Yu, Chandan Kishor, et al. "Structure-Based Design of a Monosaccharide Ligand Targeting Galectin-8." ChemMedChem 13, no. 16 (2018): 1664–72. http://dx.doi.org/10.1002/cmdc.201800224.

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39

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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40

ZHANG, DAWEI, LIU ZE YU, PHILIP LIN HUANG, SYLVIA LEE-HUANG, and JOHN Z. H. ZHANG. "COMPUTATIONAL DESIGN OF NORBORNANE-BASED HIV-1 PROTEASE INHIBITORS." Journal of Theoretical and Computational Chemistry 09, no. 02 (2010): 471–85. http://dx.doi.org/10.1142/s0219633610005773.

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A series of norbornane-based HIV-1 protease (PR) inhibitors are designed theoretically to displace the tetrahedrally coordinated internal water molecule that bridges inhibitor to flaps via hydrogen bonds. These designed inhibitors use the norbornenone oxygen atom to mimic this structural water molecule and contain diols to interact with the carboxylate oxygens of catalytic aspartates. The binding free energies were estimated by modified linear interaction energy approach [Zoete H, Michielin O, Karplus M, J Comput Aided Mol Des17:861, 2003], in which the binding free energy is written as a line

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41

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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42

Nash, Jessica A., Matthew D. Manning, Alexey V. Gulyuk, Aleksey E. Kuznetsov, and Yaroslava G. Yingling. "Gold nanoparticle design for RNA compaction." Biointerphases 17, no. 6 (2022): 061001. http://dx.doi.org/10.1116/6.0002043.

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RNA-based therapeutics hold a great promise in treating a variety of diseases. However, double-stranded RNAs (dsRNAs) are inherently unstable, highly charged, and stiff macromolecules that require a delivery vehicle. Cationic ligand functionalized gold nanoparticles (AuNPs) are able to compact nucleic acids and assist in RNA delivery. Here, we use large-scale all-atom molecular dynamics simulations to show that correlations between ligand length, metal core size, and ligand excess free volume control the ability of nanoparticles to bend dsRNA far below its persistence length. The analysis of a

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43

Guterres, Hugo, Sang-Jun Park, Yiwei Cao, and Wonpil Im. "CHARMM-GUI Ligand Designer for Template-Based Virtual Ligand Design in a Binding Site." Journal of Chemical Information and Modeling 61, no. 11 (2021): 5336–42. http://dx.doi.org/10.1021/acs.jcim.1c01156.

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44

Moro, Stefano, Magdalena Bacilieri, and Francesca Deflorian. "Combining ligand-based and structure-based drug design in the virtual screening arena." Expert Opinion on Drug Discovery 2, no. 1 (2007): 37–49. http://dx.doi.org/10.1517/17460441.2.1.37.

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45

Moon, Joseph B., and W. Jeffrey Howe. "Computer design of bioactive molecules: A method for receptor-based de novo ligand design." Proteins: Structure, Function, and Genetics 11, no. 4 (1991): 314–28. http://dx.doi.org/10.1002/prot.340110409.

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46

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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47

Grant, Marianne. "Protein Structure Prediction in Structure-Based Ligand Design and Virtual Screening." Combinatorial Chemistry & High Throughput Screening 12, no. 10 (2009): 940–60. http://dx.doi.org/10.2174/138620709789824718.

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48

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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49

Sun, Tian-Yang, Qi Wang, Jin Zhang, Tao Wu, and Fan Zhang. "Trastuzumab-Peptide Interactions: Mechanism and Application in Structure-Based Ligand Design." International Journal of Molecular Sciences 14, no. 8 (2013): 16836–50. http://dx.doi.org/10.3390/ijms140816836.

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50

Ui, Mihoko, and Kouhei Tsumoto. "An Approach to Rational Ligand-Design Based on a Thermodynamic Analysis." Recent Patents on Biotechnology 4, no. 3 (2010): 183–88. http://dx.doi.org/10.2174/187220810793611482.

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