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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.
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Rudrapal, Mithun, and Dipak Chetia. "Virtual Screening, Molecular Docking and QSAR Studies in Drug Discovery and Development Programme." Journal of Drug Delivery and Therapeutics 10, no. 4 (July 15, 2020): 225–33. http://dx.doi.org/10.22270/jddt.v10i4.4218.
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Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two basic approaches of computer-aided drug design (CADD) used in modern drug discovery and development programme. Virtual screening (or in silico screening) has been used in drug discovery program as a complementary tool to high throughput screening (HTS) to identify bioactive compounds. It is a preliminary tool of CADD that has gained considerable interest in the pharmaceutical research as a productive and cost-effective technology in search for novel molecules of medicinal interest. Docking is also used for virtual screening of new ligands on the basis of biological structures for identification of hits and generation of leads or optimization (potency/ property) of leads in drug discovery program. Hence, docking is approach of SBDD which plays an important role in rational designing of new drug molecules. Quantitative structure-activity relationship (QSAR) is an important chemometric tool in computational drug design. It is a common practice of LBDD. The study of QSAR gives information related to structural features and/or physicochemical properties of structurally similar molecules to their biological activity. In this paper, a comprehensive review on several computational tools of SBDD and LBDD such as virtual screening, molecular docking and QSAR methods of and their applications in the drug discovery and development programme have been summarized.
Keywords: Virtual screening, Molecular docking, QSAR, Drug discovery, Lead molecule
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Nakarin, Fahsai, Kajjana Boonpalit, Jiramet Kinchagawat, Patcharapol Wachiraphan, Thanyada Rungrotmongkol, and Sarana Nutanong. "Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation." Molecules 27, no. 4 (February 11, 2022): 1226. http://dx.doi.org/10.3390/molecules27041226.
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A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure–activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model’s applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development.
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Pandey, Surabhi, and B. K. Singh. "De-novo Drug Design, Molecular Docking and In-Silico Molecular Prediction of AChEI Analogues through CADD Approaches as Anti-Alzheimer’s Agents." Current Computer-Aided Drug Design 16, no. 1 (January 6, 2020): 54–72. http://dx.doi.org/10.2174/1573409915666190301124210.
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Background: There are over 44 million persons who suffer with Alzheimer’s disease (AD) worldwide, no existence of cure and only symptomatic treatments are available for it. The aim of this study is to evaluate the anti-Alzheimer potential of designed AChEI analogues using computer simulation docking studies. AChEIs are the most potential standards for treatment of AD, because they have proven efficacy. Among all AChEIs donepezil possesses lowest adverse effects, it can treat mildmoderate- severe AD and only once-daily dosing is required. Therefore, donepezil is recognized as a significant prototype for design and development of new drug molecule. Methods: In this study the Inhibitory potential of the design compounds on acetylcholinesterase enzyme has been evaluated. Docking studies has been performed which further analyzed by in-silico pharmacokinetic evaluation through pharmacopredicta after that Interaction modes with enzyme active sites were determined. Docking studies revealed that there is a strong interaction between the active sites of AChE enzyme and analyzed compounds. Results: As a result 26 compounds have been indicates better inhibitory activity on AChE enzyme and all the screening parameters have also been satisfied by all 26 compounds. From these 26 compounds, six compounds 17, 18, 24, 30, 36 and 56 are found to be the most potent inhibitors of this series by insilico study through INVENTUS v 1.1 software, having highest bio-affinities i.e. - 8.51, - 7.67, - 8.30, - 7.59, - 8.71 and -7.62 kcal/mol respectively, while the standard or reference drug donepezil had binding affinity of - 6.32 kcal/mol. Conclusion: Computer aided drug design approach has been playing an important role in the design and development of novel anti- AD drugs. With the help of structure based drug design some novel analogues of donepezil have been designed and the molecular docking studies with structure based ADME properties prediction studies is performed for prediction of AChE inhibitory activity. The binding mode of proposed compounds with target protein i.e. AChE has been evaluated and the resulting data from docking studies explains that all of the newly designed analogues had significantly high affinity towards target protein compared to donepezil as a reference ligand.
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Brogi, Simone, Mark Tristan Quimque, Kin Israel Notarte, Jeremiah Gabriel Africa, Jenina Beatriz Hernandez, Sophia Morgan Tan, Vincenzo Calderone, and Allan Patrick Macabeo. "Virtual Combinatorial Library Screening of Quinadoline B Derivatives against SARS-CoV-2 RNA-Dependent RNA Polymerase." Computation 10, no. 1 (January 12, 2022): 7. http://dx.doi.org/10.3390/computation10010007.
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The unprecedented global health threat of SARS-CoV-2 has sparked a continued interest in discovering novel anti-COVID-19 agents. To this end, we present here a computer-based protocol for identifying potential compounds targeting RNA-dependent RNA polymerase (RdRp). Starting from our previous study wherein, using a virtual screening campaign, we identified a fumiquinazolinone alkaloid quinadoline B (Q3), an antiviral fungal metabolite with significant activity against SARS-CoV-2 RdRp, we applied in silico combinatorial methodologies for generating and screening a library of anti-SARS-CoV-2 candidates with strong in silico affinity for RdRp. For this study, the quinadoline pharmacophore was subjected to structural iteration, obtaining a Q3-focused library of over 900,000 unique structures. This chemical library was explored to identify binders of RdRp with greater affinity with respect to the starting compound Q3. Coupling this approach with the evaluation of physchem profile, we found 26 compounds with significant affinities for the RdRp binding site. Moreover, top-ranked compounds were submitted to molecular dynamics to evaluate the stability of the systems during a selected time, and to deeply investigate the binding mode of the most promising derivatives. Among the generated structures, five compounds, obtained by inserting nucleotide-like scaffolds (1, 2, and 5), heterocyclic thiazolyl benzamide moiety (compound 3), and a peptide residue (compound 4), exhibited enhanced binding affinity for SARS-CoV-2 RdRp, deserving further investigation as possible antiviral agents. Remarkably, the presented in silico procedure provides a useful computational procedure for hit-to-lead optimization, having implications in anti-SARS-CoV-2 drug discovery and in general in the drug optimization process.
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Osman, Doaa A., Mario A. Macías, Lamya H. Al-Wahaibi, Nora H. Al-Shaalan, Luke S. Zondagh, Jacques Joubert, Santiago Garcia-Granda, and Ali A. El-Emam. "Structural Insights and Docking Analysis of Adamantane-Linked 1,2,4-Triazole Derivatives as Potential 11β-HSD1 Inhibitors." Molecules 26, no. 17 (September 2, 2021): 5335. http://dx.doi.org/10.3390/molecules26175335.
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The solid-state structural analysis and docking studies of three adamantane-linked 1,2,4-triazole derivatives are presented. Crystal structure analyses revealed that compound 2 crystallizes in the triclinic P-1 space group, while compounds 1 and 3 crystallize in the same monoclinic P21/c space group. Since the only difference between them is the para substitution on the aryl group, the electronic nature of these NO2 and halogen groups seems to have no influence over the formation of the solid. However, a probable correlation with the size of the groups is not discarded due to the similar intermolecular disposition between the NO2/Cl substituted molecules. Despite the similarities, CE-B3LYP energy model calculations show that pairwise interaction energies vary between them, and therefore the total packing energy is affected. HOMO-LUMO calculated energies show that the NO2 group influences the reactivity properties characterizing the molecule as soft and with the best disposition to accept electrons. Further, in silico studies predicted that the compounds might be able to inhibit the 11β-HSD1 enzyme, which is implicated in obesity and diabetes. Self- and cross-docking experiments revealed that a number of non-native 11β-HSD1 inhibitors were able to accurately dock within the 11β-HSD1 X-ray structure 4C7J. The molecular docking of the adamantane-linked 1,2,4-triazoles have similar predicted binding affinity scores compared to the 4C7J native ligand 4YQ. However, they were unable to form interactions with key active site residues. Based on these docking results, a series of potentially improved compounds were designed using computer aided drug design tools. The docking results of the new compounds showed similar predicted 11β-HSD1 binding affinity scores as well as interactions to a known potent 11β-HSD1 inhibitor.
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Sokolova, K. V., V. V. Stavytskyi, S. О. Konovalova, O. A. Podpletnya, S. I. Kovalenko, and A. P. Avdeenko. "Design and search for prospective diuretics (CA II Inhibitors) among aroylhydrazones of esters quinone oxime using in silico and in vivo methodology." Medicni perspektivi 27, no. 4 (December 29, 2022): 27–37. http://dx.doi.org/10.26641/2307-0404.2022.4.271120.
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The design and search for new selective inhibitors of CA II with a better pharmacological profile, which would cause minimal electrolyte disturbances in the body, remains an urgent problem of medical chemistry and pharmacology today. It is important that the discovered new classes of inhibitors do not always contain the main “pharmacophoric” function (sulfamide), which is characteristic of “classic” drugs (Acetazolamide, Methazolamide, Ethoxzolamide, Dorzolamide and others), but are derivatives of phenols, polyamines, coumarins/thiocoumarins, ureas, thioureas, hydroxamates, etc. These molecules also bind in the active site of the enzyme, but do not interact directly with the catalytic zinc ion or interact through zinc-coordinated water molecules/hydroxide ion. However, this leads to an increase in their selectivity and, as a result, pharmacological action. Continuing the search for compounds that affect urination, we were interested in aroylhydrazones of esters of quinone oxime. Firstly, they are characterized by certain structural features (dynamic and geometric isomerism); secondly, they exhibit redox properties; thirdly, the presence of aromatic fragments makes it possible to create a voluminous combinatorial library for analysis. These compounds are ligands in complexation reactions, and an additional increase in the number of hydrogen acceptors in the molecule due to structural modification will improve ligand-enzymatic interactions with carbonic anhydrase (CAII) and, as a result, reveal new promising diuretics. The aim – design and search for potential diuretics (CA II inhibitors) among aroylhydrazones of esters of quinone oxime using in silico, traditional synthesis and in vivo methodologies. Methods of organic synthesis, physico-chemical methods of analysis of organic compounds (NMR 1H-spectroscopy, elemental analysis). Prediction of affinity to the biological target, prediction of toxicity and lipophilicity of the combinatorial library of benzohydrazides O-aroyl esters of quinone oxime using computer services. The study of compounds affecting the excretory function of rat kidneys was carried out according to the generally accepted method of E.B.Berkhin with water load. The investigation of the probable mechanism was carried out using flexible molecular docking, as an approach to search for molecules that have affinity for human carbonic anhydrase type II (CA II). Macromolecular data of the crystal structure of CA II (PDB ID – 3HS4) were downloaded from the Protein Data Bank (PDB). The design was developed and the search for diuretic agents among benzohydrazides of O-aroyl esters of quinone oximes was developed using in silico methods (prediction of affinity, lipophilicity, toxicity and enzyme-ligand interactions), traditional organic synthesis, and in vivo methods (effect on excretory function of rat kidneys). The synthesis of benzohydrazides of O-aroyl esters of quinone oxime was carried out by the interaction of aroylhydrazines with 4-[(aroylimino)]cyclohexa-2,5-dien-1-ones. The structure of the synthesized compounds was confirmed by elemental analysis and 1H NMR spectra. Studies of the effect of synthesized compounds on the excretory function of rat kidneys allowed us to identify a number of promising compounds among aroylhydrazones of quinonexime esters, which increase daily diuresis by 54.2-352.8% compared to the control group. At the same time, it was established that the most active was N'-(4-[(2-chlorobenzoyloxy)imino]cyclohexa-2,5-dien-1-ylidene)-3-nitrobenzohydrazide, which increased daily diuresis by 352.8% in comparison with the control group, while exceeding the effect of “Hydrochlorothiazide” (170.8%). The developed and implemented strategy for the search for diuretics among benzohydrazides of O-aroylesters of quinone oxime allowed the identification of an effective compound, which in terms of diuretic effect exceeds the comparison drug “Hydrochlorothiazide”. Visualization of the molecular docking of the active compounds showed that their geometry makes it difficult to place them in the pocket of the active site of CA II, but the pronounced diuretic effect can also be associated with their ability to form coordination bonds with the zinc cation. The obtained results justify the further targeted search for potential diuretics among this class of compounds for a more detailed understanding and study of the mechanism of action.
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Moreira, Bernardo Pereira, Izabella Cristina Andrade Batista, Naiara Clemente Tavares, Tom Armstrong, Sandra Grossi Gava, Gabriella Parreiras Torres, Marina Moraes Mourão, and Franco H. Falcone. "Docking-Based Virtual Screening Enables Prioritizing Protein Kinase Inhibitors With In Vitro Phenotypic Activity Against Schistosoma mansoni." Frontiers in Cellular and Infection Microbiology 12 (July 5, 2022). http://dx.doi.org/10.3389/fcimb.2022.913301.
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Schistosomiasis is a parasitic neglected disease with praziquantel (PZQ) utilized as the main drug for treatment, despite its low effectiveness against early stages of the worm. To aid in the search for new drugs to tackle schistosomiasis, computer-aided drug design has been proved a helpful tool to enhance the search and initial identification of schistosomicidal compounds, allowing fast and cost-efficient progress in drug discovery. The combination of high-throughput in silico data followed by in vitro phenotypic screening assays allows the assessment of a vast library of compounds with the potential to inhibit a single or even several biological targets in a more time- and cost-saving manner. Here, we describe the molecular docking for in silico screening of predicted homology models of five protein kinases (JNK, p38, ERK1, ERK2, and FES) of Schistosoma mansoni against approximately 85,000 molecules from the Managed Chemical Compounds Collection (MCCC) of the University of Nottingham (UK). We selected 169 molecules predicted to bind to SmERK1, SmERK2, SmFES, SmJNK, and/or Smp38 for in vitro screening assays using schistosomula and adult worms. In total, 89 (52.6%) molecules were considered active in at least one of the assays. This approach shows a much higher efficiency when compared to using only traditional high-throughput in vitro screening assays, where initial positive hits are retrieved from testing thousands of molecules. Additionally, when we focused on compound promiscuity over selectivity, we were able to efficiently detect active compounds that are predicted to target all kinases at the same time. This approach reinforces the concept of polypharmacology aiming for “one drug-multiple targets”. Moreover, at least 17 active compounds presented satisfactory drug-like properties score when compared to PZQ, which allows for optimization before further in vivo screening assays. In conclusion, our data support the use of computer-aided drug design methodologies in conjunction with high-throughput screening approach.
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Simoben, Conrad V., Fidele Ntie-Kang, Dina Robaa, and Wolfgang Sippl. "Case studies on computer-based identification of natural products as lead molecules." Physical Sciences Reviews 5, no. 10 (May 15, 2020). http://dx.doi.org/10.1515/psr-2018-0119.
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AbstractThe development and application of computer-aided drug design/discovery (CADD) techniques (such as structured-base virtual screening, ligand-based virtual screening and neural networks approaches) are on the point of disintermediation in the pharmaceutical drug discovery processes. The application of these CADD methods are standing out positively as compared to other experimental approaches in the identification of hits. In order to venture into new chemical spaces, research groups are exploring natural products (NPs) for the search and identification of new hits and more efficient leads as well as the repurposing of approved NPs. The chemical space of NPs is continuously increasing as a result of millions of years of evolution of species and these data are mainly stored in the form of databases providing access to scientists around the world to conduct studies using them. Investigation of these NP databases with the help of CADD methodologies in combination with experimental validation techniques is essential to identify and propose new drug molecules. In this chapter, we highlight the importance of the chemical diversity of NPs as a source for potential drugs as well as some of the success stories of NP-derived candidates against important therapeutic targets. The focus is on studies that applied a healthy dose of the emerging CADD methodologies (structure-based, ligand-based and machine learning).
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Rahman, Md Mominur, Md Rezaul Islam, Shopnil Akash, Sadia Afsana Mim, Md Saidur Rahaman, Talha Bin Emran, Esra Küpeli Akkol, et al. "In silico investigation and potential therapeutic approaches of natural products for COVID-19: Computer-aided drug design perspective." Frontiers in Cellular and Infection Microbiology 12 (August 22, 2022). http://dx.doi.org/10.3389/fcimb.2022.929430.
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a substantial number of deaths around the world, making it a serious and pressing public health hazard. Phytochemicals could thus provide a rich source of potent and safer anti-SARS-CoV-2 drugs. The absence of approved treatments or vaccinations continues to be an issue, forcing the creation of new medicines. Computer-aided drug design has helped to speed up the drug research and development process by decreasing costs and time. Natural compounds like terpenoids, alkaloids, polyphenols, and flavonoid derivatives have a perfect impact against viral replication and facilitate future studies in novel drug discovery. This would be more effective if collaboration took place between governments, researchers, clinicians, and traditional medicine practitioners’ safe and effective therapeutic research. Through a computational approach, this study aims to contribute to the development of effective treatment methods by examining the mechanisms relating to the binding and subsequent inhibition of SARS-CoV-2 ribonucleic acid (RNA)-dependent RNA polymerase (RdRp). The in silico method has also been employed to determine the most effective drug among the mentioned compound and their aquatic, nonaquatic, and pharmacokinetics’ data have been analyzed. The highest binding energy has been reported -11.4 kcal/mol against SARS-CoV-2 main protease (7MBG) in L05. Besides, all the ligands are non-carcinogenic, excluding L04, and have good water solubility and no AMES toxicity. The discovery of preclinical drug candidate molecules and the structural elucidation of pharmacological therapeutic targets have expedited both structure-based and ligand-based drug design. This review article will assist physicians and researchers in realizing the enormous potential of computer-aided drug design in the design and discovery of therapeutic molecules, and hence in the treatment of deadly diseases.
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Proj, Matic, Steven De Jonghe, Tom Van Loy, Marko Jukič, Anže Meden, Luka Ciber, Črtomir Podlipnik, et al. "A Set of Experimentally Validated Decoys for the Human CC Chemokine Receptor 7 (CCR7) Obtained by Virtual Screening." Frontiers in Pharmacology 13 (March 18, 2022). http://dx.doi.org/10.3389/fphar.2022.855653.
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We present a state-of-the-art virtual screening workflow aiming at the identification of novel CC chemokine receptor 7 (CCR7) antagonists. Although CCR7 is associated with a variety of human diseases, such as immunological disorders, inflammatory diseases, and cancer, this target is underexplored in drug discovery and there are no potent and selective CCR7 small molecule antagonists available today. Therefore, computer-aided ligand-based, structure-based, and joint virtual screening campaigns were performed. Hits from these virtual screenings were tested in a CCL19-induced calcium signaling assay. After careful evaluation, none of the in silico hits were confirmed to have an antagonistic effect on CCR7. Hence, we report here a valuable set of 287 inactive compounds that can be used as experimentally validated decoys.
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Sahu, Ankita, Saurabh Verma, Dibyabhaba Pradhan, Khalid Raza, Sahar Qazi, and A. K. Jain. "Computational screening for finding new potent cox-2 inhibitors as anticancer agents." Letters in Drug Design & Discovery 19 (January 28, 2022). http://dx.doi.org/10.2174/1570180819666220128122553.
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Background: Breast cancer ranks first in women and the second most common type of cancer overall. It is the most important barrier to the rise of life expectancy, globally affecting disease modalities. Cyclooxygenase-2 (COX-2) has become a prominent hallmark as inhibition target for breast cancer, and this therapeutic target for anti-inflammatory drugs regulates cell proliferation, angiogenesis, tumor growth and apoptosis. There is a need to explore new anti-cancerous drugs for searching the best possible hit candidates for cancer treatment. The computer-aided drug design approach was conducted to discover the new alternative COX-2 inhibitors. Objective: The research framework of this study is to identify new potent inhibitors for the COX-2 using computer-aided drug design. Methods: In the present investigation, an in-silico approach was used to screen with the best established three biological databases (Zinc15, ChemSpider and BindingDB) and docked against the COX-2 protein structure (PDB ID: 5IKR). Molecular docking was carried out using the Schrodinger Maestro suite. The compounds were filtered out based on their physicochemical, ADMET, and other drug-like properties. Several computational approaches such as molecular docking, binding free energy calculation, ADMET analysis, protein-ligand interaction and MD simulation were performed to determine the suitability of correct ligands for selected COX-2 target. Results: The two ligands showed relatively better binding affinities (-10.028 kcal/mol for compound A and -10.007 kcal/mol for ZINC000048442590) than the standard (-9.751 kcal/mol). These compounds followed Lipinski’s rule and drug-likeness index, which exhibited a good predicted therapeutic druggability profile. The interaction of the protein-ligand complex correlates with the COX-2. The MD simulation of the protein-ligand complex showed good stability in the time period of 10ns. Conclusion: It is the first study in which two new compounds ZINC000048442590 and compound A were found to be highly promising and have active potential in inhibiting cyclooxygenase-2 enzyme and could be effective as the potential drug candidates for breast cancer against COX-2 protein. Hopefully, in the future, these compounds as anti-inflammatory drug molecules could be used as new templates for the development of anticancer agents.
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Gahbauer, Stefan, Galen J. Correy, Marion Schuller, Matteo P. Ferla, Yagmur Umay Doruk, Moira Rachman, Taiasean Wu, et al. "Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2." Proceedings of the National Academy of Sciences 120, no. 2 (January 4, 2023). http://dx.doi.org/10.1073/pnas.2212931120.
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The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small-molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic, there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high-resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 153 Mac1-ligand complex crystal structures were determined, typically to 1 Å resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated conformational changes within the active site, and key inhibitor motifs that will template future drug development against Mac1.