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Relevant bibliographies by topics / Computer-aided drug discovery, in silico methodologies, ligand-based, structure-based

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Journal articles

Academic literature on the topic 'Computer-aided drug discovery, in silico methodologies, ligand-based, structure-based'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Computer-aided drug discovery, in silico methodologies, ligand-based, structure-based"

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Yadav, Tara Chand, Amit Kumar Srivastava, Arpita Dey, Naresh Kumar, Navdeep Raghuwanshi, and Vikas Pruthi. "Application of Computational Techniques to Unravel Structure-Function Relationship and their Role in Therapeutic Development." Current Topics in Medicinal Chemistry 18, no. 20 (2018): 1769–91. http://dx.doi.org/10.2174/1568026619666181120142141.

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Application of computational tools and techniques has emerged as an invincible instrument to unravel the structure-function relationship and offered better mechanistic insights in the designing and development of new drugs along with the treatment regime. The use of in silico tools equipped modern chemist with armamentarium of extensive methods to meticulously comprehend the structural tenacity of receptor-ligand interactions and their dynamics. In silico methods offers a striking property of being less resource intensive and economically viable as compared to experimental evaluation. These te

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de Sousa Luis, José A., Normando A. da Silva Costa, Cristiane C. S. Luis, et al. "Synthesis of New Cyclic Imides Derived From Safrole, Structure- and Ligand-based Approaches to Evaluate Potential New Multitarget Agents Against Species of Leishmania." Medicinal Chemistry 16, no. 1 (2020): 39–51. http://dx.doi.org/10.2174/1573406415666190430144950.

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Background: Leishmaniasis is a neglected disease that does not have adequate treatment. It affects around 12 million people around the world and is classified as a neglected disease by the World Health Organization. In this context, strategies to obtain new, more active and less toxic drugs should be stimulated. Sources of natural products combined with synthetic and chemoinformatic methodologies are strategies used to obtain molecules that are most likely to be effective against a specific disease. Computer-Aided Drug Design has become an indispensable tool in the pharmaceutical industry and

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Cerdan, Adrien H., Marion Sisquellas, Gilberto Pereira, Diego E. Barreto Gomes, Jean-Pierre Changeux, and Marco Cecchini. "The Glycine Receptor Allosteric Ligands Library (GRALL)." Bioinformatics 36, no. 11 (2020): 3379–84. http://dx.doi.org/10.1093/bioinformatics/btaa170.

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Abstract Motivation Glycine receptors (GlyRs) mediate fast inhibitory neurotransmission in the brain and have been recognized as key pharmacological targets for pain. A large number of chemically diverse compounds that are able to modulate GlyR function both positively and negatively have been reported, which provides useful information for the development of pharmacological strategies and models for the allosteric modulation of these ion channels. Results Based on existing literature, we have collected 218 unique chemical entities with documented modulatory activities at homomeric GlyR-α1 and

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Nero, Tracy L., Michael W. Parker, and Craig J. Morton. "Protein structure and computational drug discovery." Biochemical Society Transactions 46, no. 5 (2018): 1367–79. http://dx.doi.org/10.1042/bst20180202.

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The first protein structures revealed a complex web of weak interactions stabilising the three-dimensional shape of the molecule. Small molecule ligands were then found to exploit these same weak binding events to modulate protein function or act as substrates in enzymatic reactions. As the understanding of ligand–protein binding grew, it became possible to firstly predict how and where a particular small molecule might interact with a protein, and then to identify putative ligands for a specific protein site. Computer-aided drug discovery, based on the structure of target proteins, is now a w

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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. Opt

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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 identific

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Sanyal, Saptarshi, Sk Abdul Amin, Nilanjan Adhikari, and Tarun Jha. "Ligand-based design of anticancer MMP2 inhibitors: a review." Future Medicinal Chemistry 13, no. 22 (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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Leelananda, Sumudu P., and Steffen Lindert. "Computational methods in drug discovery." Beilstein Journal of Organic Chemistry 12 (December 12, 2016): 2694–718. http://dx.doi.org/10.3762/bjoc.12.267.

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The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power ha

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Samanta, Pabitra Narayan, Supratik Kar, and Jerzy Leszczynski. "Recent Advances of In-Silico Modeling of Potent Antagonists for the Adenosine Receptors." Current Pharmaceutical Design 25, no. 7 (2019): 750–73. http://dx.doi.org/10.2174/1381612825666190304123545.

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The rapid advancement of computer architectures and development of mathematical algorithms offer a unique opportunity to leverage the simulation of macromolecular systems at physiologically relevant timescales. Herein, we discuss the impact of diverse structure-based and ligand-based molecular modeling techniques in designing potent and selective antagonists against each adenosine receptor (AR) subtype that constitutes multitude of drug targets. The efficiency and robustness of high-throughput empirical scoring function-based approaches for hit discovery and lead optimization in the AR family

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Ramesh, Muthusamy, and Arunachalam Muthuraman. "Computer-Aided Drug Discovery (CADD) Approaches for the Management of Neuropathic Pain." Current Topics in Medicinal Chemistry 21, no. 32 (2021): 2856–68. http://dx.doi.org/10.2174/1568026621666211122161932.

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Neuropathic pain occurs due to physical damage, injury, or dysfunction of neuronal fibers. The pathophysiology of neuropathic pain is too complex. Therefore, an accurate and reliable prediction of the appropriate hits/ligands for the treatment of neuropathic pain is a challenging process. However, computer-aided drug discovery approaches contributed significantly to discovering newer hits/ligands for the treatment of neuropathic pain. The computational approaches like homology modeling, induced-fit molecular docking, structure-activity relationships, metadynamics, and virtual screening were ci

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