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Academic literature on the topic 'Computer-aided drug discovery, in silico methodologies, ligand-based, structure-based'
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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 (December 31, 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 techniques have proved their mettle in the designing of potential lead compounds to combat life-threatening diseases such as AIDS, cancer, tuberculosis, malaria, etc. In the present scenario, computer-aided drug designing has ascertained an essential and indispensable gizmo in therapeutic development. This review will present a brief outline of computational methods used at different facets of drug designing and its latest advancements. The aim of this review article is to briefly highlight the methodologies and techniques used in structure-based/ ligand-based drug designing viz., molecular docking, pharmacophore modeling, density functional theory, protein-hydration and molecular dynamics simulation which helps in better understanding of macromolecular events and complexities.
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de Sousa Luis, José A., Normando A. da Silva Costa, Cristiane C. S. Luis, Bruno F. Lira, Petrônio F. Athayde-Filho, Tatjana K. de Souza Lima, Juliana da Câmara Rocha, Luciana Scotti, and Marcus T. Scotti. "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 (January 16, 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 academia in recent years and has been employed during various stages of the drug design process. Objectives: Perform structure- and ligand-based approaches, synthesize and characterize some compounds with materials available in our laboratories to verify the method’s efficiency. Methods: We created a database with 33 cyclic imides and evaluated their potential anti- Leishmanial activity (L. amazonensis and L. donovani) through ligand- and structure-based virtual screening. A diverse set selected from ChEMBL databanks of 818 structures (L. donovani) and 722 structures (L. amazonensis), with tested anti-Leishmanial activity against promastigotes forms, were classified according to pIC50 values to generate and validate a Random Forest model that shows higher statistical indices values. The structures of four different L. donovani enzymes were downloaded from the Protein Data Bank and the imides’ structures were submitted to molecular docking. So, with available materials and technical feasibility of our laboratories, we have synthesized and characterized seven compounds through cyclization reactions between isosafrole and maleic anhydride followed by treatment with different amines to obtain new cyclic imides to evaluate their anti-Leishmanial activity. Results: In silico study allowed us to suggest that the cyclic imides 516, 25, 31, 24, 32, 2, 3, 22 can be tested as potential multitarget molecules for leishmanial treatment, presenting activity probability against four strategic enzymes (Topoisomerase I, N-myristoyltransferase, cyclophilin and Oacetylserine sulfhydrylase). The compounds synthesized and tested presented pIC50 values less than 4.7 for Leishmania amazonensis. Conclusion: After combined approach evaluation, we have synthesized and characterized seven cyclic imides by IR, 1H NMR, 13C-APT NMR, COSY, HETCOR and HMBC. The compounds tested against promastigote forms of L. amazonensis presented pIC50 values less than 4.7, showing that our method was efficient in predicting true negative molecules.
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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 (March 12, 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 -α3 and built a database named GRALL. This collection includes agonists, antagonists, positive and negative allosteric modulators and a number of experimentally inactive compounds. Most importantly, for a large fraction of them a structural annotation based on their putative binding site on the receptor is provided. This type of annotation, which is currently missing in other drug banks, along with the availability of cooperativity factors from radioligand displacement experiments are expected to improve the predictivity of in silico methodologies for allosteric drug discovery and boost the development of conformation-based pharmacological approaches. Availability and implementation The GRALL library is distributed as a web-accessible database at the following link: https://ifm.chimie.unistra.fr/grall. For each molecular entry, it provides information on the chemical structure, the ligand-binding site, the direction of modulation, the potency, the 3D molecular structure and quantum-mechanical charges as determined by our in-house pipeline. Contact mcecchini@unistra.fr Supplementary information Supplementary data are available at Bioinformatics online.
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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 (September 21, 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 well-established technique that has produced several marketed drugs. We present here an overview of the various methodologies being used for structure-based computer-aided drug discovery and comment on possible future developments in the field.
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Sehgal, Vijay Kumar, Supratik Das, and Anand Vardhan. "Computer Aided Drug Designing." International Journal of Medical and Dental Sciences 6, no. 1 (January 1, 2017): 1433. http://dx.doi.org/10.18311/ijmds/2017/18804.
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Designing of drugs and their development are a time and resource consuming process. There is an increasing effort to introduce the role of computational approach to chemical and biological space in order to organise the design and development of drugs and their optimisation. The role of Computer Aided Drug Designing (CADD) are nowadays expressed in Nanotechnology, Molecular biology, Biochemistry etc. It is a diverse discipline where various forms of applied and basic researches are interlinked with each other. Computer aided or in Silico drug designing is required to detect hits and leads. Optimise/ alter the absorption, distribution, metabolism, excretion and toxicity profile and prevent safety issues. Some commonly used computational approaches include ligand-based drug design, structure-based drug design, and quantitative structure-activity and quantitative structure-property relationships. In today's world, due to an avid interest of regulatory agencies and, even pharmaceutical companies in advancing drug discovery and development process by computational means, it is expected that its power will grow as technology continues to evolve. The main purpose of this review article is to give a brief glimpse about the role Computer Aided Drug Design has played in modern medical science and the scope it carries in the near future, in the service of designing newer drugs along with lesser expenditure of time and money.
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De, Baishakhi, Koushik Bhandari, Francisco J. B. Mendonça, Marcus T. Scotti, and Luciana Scotti. "Computational Studies in Drug Design Against Cancer." Anti-Cancer Agents in Medicinal Chemistry 19, no. 5 (June 27, 2019): 587–91. http://dx.doi.org/10.2174/1871520618666180911125700.
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Background: The application of in silico tools in the development of anti cancer drugs. Objective: The summing of different computer aided drug design approaches that have been applied in the development of anti cancer drugs. Methods: Structure based, ligand based, hybrid protein-ligand pharmacophore methods, Homology modeling, molecular docking aids in different steps of drug discovery pipeline with considerable saving in time and expenditure. In silico tools also find applications in the domain of cancer drug development. Results: Structure-based pharmacophore modeling aided in the identification of PUMA inhibitors, structure based approach with high throughput screening for the development of Bcl-2 inhibitors, to derive the most relevant protein-protein interactions, anti mitotic agents; I-Kappa-B Kinase β (IKK- β) inhibitor, screening of new class of aromatase inhibitors that can be important targets in cancer therapy. Conclusion: Application of computational methods in the design of anti cancer drugs was found to be effective.
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Sanyal, Saptarshi, Sk Abdul Amin, Nilanjan Adhikari, and Tarun Jha. "Ligand-based design of anticancer MMP2 inhibitors: a review." Future Medicinal Chemistry 13, no. 22 (November 2021): 1987–2013. http://dx.doi.org/10.4155/fmc-2021-0262.
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MMP2, a Zn2+-dependent metalloproteinase, is related to cancer and angiogenesis. Inhibition of this enzyme might result in a potential antimetastatic drug to leverage the anticancer drug armory. In silico or computer-aided ligand-based drug design is a method of rational drug design that takes multiple chemometrics (i.e., multi-quantitative structure–activity relationship methods) into account for virtually selecting or developing a series of probable selective MMP2 inhibitors. Though existing matrix metalloproteinase inhibitors have shown plausible pan-matrix metalloproteinase (MMP) activity, they have resulted in various adverse effects leading to their being rescinded in later phases of clinical trials. Therefore a review of the ligand-based designing methods of MMP2 inhibitors would result in an explicit route map toward successfully designing and synthesizing novel and selective MMP2 inhibitors.
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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 have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.
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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 (June 17, 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 are assessed with the help of illustrative examples that have led to nanomolar to sub-micromolar inhibition activities. Recent progress in computer-aided drug discovery through homology modeling, quantitative structure-activity relation, pharmacophore models, and molecular docking coupled with more accurate free energy calculation methods are reported and critically analyzed within the framework of structure-based virtual screening of AR antagonists. Later, the potency and applicability of integrated molecular dynamics (MD) methods are addressed in the context of diligent inspection of intricated AR-antagonist binding processes. MD simulations are exposed to be competent for studying the role of the membrane as well as the receptor flexibility toward the precise evaluation of the biological activities of antagonistbound AR complexes such as ligand binding modes, inhibition affinity, and associated thermodynamic and kinetic parameters.
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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 (December 23, 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 cited in the literature for the identification of potential hit molecules against neuropathic pain. These hit molecules act as inducible nitric oxide synthase inhibitors, FLAT antagonists, TRPA1 modulators, voltage-gated sodium channel binder, cannabinoid receptor-2 agonists, sigma-1 receptor antagonists, etc. Sigma-1 receptor is a distinct type of opioid receptor and several patents were obtained for sigma-1 receptor antagonists for the treatment of neuropathic pain. These molecules were found to have a profound role in the management of neuropathic pain. The present review describes the validated therapeutic targets, potential chemical scaffolds, and crucial protein-ligand interactions for the management of neuropathic pain based on the recently reported computational methodologies of the present and past decades. The study can help the researcher to discover newer drugs/drug-like molecules against neuropathic pain.
Dissertations / Theses on the topic "Computer-aided drug discovery, in silico methodologies, ligand-based, structure-based"
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Chemi, Giulia. "Computer-aided drug discovery methodologies for the identification and optimization of bioactive compounds." Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1095491.
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In silico methodologies have become an essential part of the modern drug discovery process. This thesis covers a wide range of computational approaches, and all the techniques employed can be grouped and divided in two big different classes: Ligand-Based methods and Structure-Based methods.
Particular attention was payed to the evaluation of drug-like features of the identified molecules during both Structure-Based and Ligand-Based projects, in order to propose hit compounds with satisfactory pharmacokinetic profiles. This thesis work is divided in two main chapters according to the two methods applied to different medicinal chemistry issues.