1
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Alshamari AK, Al-Qudah MA, Hamadeh F, Al-Momani LA, Abu-Orabi ST. Synthesis, Characterization, Antimicrobial and Antioxidant Activities of
1,2,4-triazolyl-isoxazole Moieties via Dehydration Reactions of Carbohydrazides. LETT ORG CHEM 2022. [DOI: 10.2174/1570178618666210531095246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Abstract:
For the reason of distinct place in the field of medicinal and pharmaceutical chemistry of
1,2,4-triazole derivatives, a new class of fused 1,2,4-triazolyl-isoxazole moieties was prepared from 3-
(2,4,6-trimethoxyphenyl)isoxazolo-4,5-bis[carbonyl-(4̍-phenyl) thiosemicarbazide via dehydration reactions
of carbohydrazides by using the appropriate chemical reagents. The structures of the compounds
were elucidated by both elemental and spectral (IR, NMR and MS) analyses. The in vitro antioxidant
activity of the new compounds was determined by free radical scavenging and metal chelating
activity. All the synthesized compounds showed good activity according to free radical scavenging and
metal chelating activity compared with standards. The new compounds were screened in vitro antibacterial
activity against three gram-positive bacteria and three gram-negative bacteria.
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Affiliation(s)
- Asma K. Alshamari
- Department of Chemistry, Faculty of Science, University of Ha\'il, P.O. Box 2440, Saudi Arabia
| | - Mahmoud A. Al-Qudah
- Department of Chemistry,
Faculty of Science, Yarmouk University, P.O. Box 566, Irbid 21163, Jordan
| | - Fedaa Hamadeh
- Department of Chemistry,
Faculty of Science, Yarmouk University, P.O. Box 566, Irbid 21163, Jordan
| | - Lo’ay A. Al-Momani
- Department of Chemistry, Tafila
Technical University, P.O. Box 179, Tafila 66110, Jordan
| | - Sultan T. Abu-Orabi
- Department of Chemistry,
Faculty of Science, Yarmouk University, P.O. Box 566, Irbid 21163, Jordan
- Department of Medical Analysis, Faculty of Science, Tishk
International University, Erbil, KRG, Iraq
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2
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Yao R, Ianevski A, Kainov D. Safe-in-Man Broad Spectrum Antiviral Agents. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1322:313-337. [PMID: 34258746 DOI: 10.1007/978-981-16-0267-2_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Emerging and re-emerging viral diseases occur with regularity within the human population. The conventional 'one drug, one virus' paradigm for antivirals does not adequately allow for proper preparedness in the face of unknown future epidemics. In addition, drug developers lack the financial incentives to work on antiviral drug discovery, with most pharmaceutical companies choosing to focus on more profitable disease areas. Safe-in-man broad spectrum antiviral agents (BSAAs) can help meet the need for antiviral development by already having passed phase I clinical trials, requiring less time and money to develop, and having the capacity to work against many viruses, allowing for a speedy response when unforeseen epidemics arise. In this chapter, we discuss the benefits of repurposing existing drugs as BSAAs, describe the major steps in safe-in-man BSAA drug development from discovery through clinical trials, and list several database resources that are useful tools for antiviral drug repositioning.
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Affiliation(s)
- Rouan Yao
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Aleksandr Ianevski
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Denis Kainov
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- Institute of Technology, University of Tartu, Tartu, Estonia.
- Institute for Molecule Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland.
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3
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Kuz’min V, Artemenko A, Ognichenko L, Hromov A, Kosinskaya A, Stelmakh S, Sessions ZL, Muratov EN. Simplex representation of molecular structure as universal QSAR/QSPR tool. Struct Chem 2021; 32:1365-1392. [PMID: 34177203 PMCID: PMC8218296 DOI: 10.1007/s11224-021-01793-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 05/07/2021] [Indexed: 10/24/2022]
Abstract
We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.
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Affiliation(s)
- Victor Kuz’min
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anatoly Artemenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Luidmyla Ognichenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Alexander Hromov
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anna Kosinskaya
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
- Department of Medical Chemistry, Odessa National Medical University, Odessa, 65082 Ukraine
| | - Sergij Stelmakh
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Zoe L. Sessions
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059 Brazil
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4
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Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback? Int J Mol Sci 2021; 22:ijms22105212. [PMID: 34069090 PMCID: PMC8156896 DOI: 10.3390/ijms22105212] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/11/2021] [Accepted: 05/12/2021] [Indexed: 01/01/2023] Open
Abstract
A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.
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5
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Development of quantitative structure-property relationship (QSPR) models for predicting the thermal hazard of ionic liquids: A review of methods and models. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2020.112471] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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6
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Yazdani H, Kaul E, Bazgir A, Maysinger D, Kakkar A. Telodendrimer-Based Macromolecular Drug Design using 1,3-Dipolar Cycloaddition for Applications in Biology. Molecules 2020; 25:E857. [PMID: 32075239 PMCID: PMC7071137 DOI: 10.3390/molecules25040857] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/11/2020] [Accepted: 02/12/2020] [Indexed: 12/20/2022] Open
Abstract
An architectural polymer containing hydrophobic isoxazole-based dendron and hydrophilic polyethylene glycol linear tail is prepared by a combination of the robust ZnCl2 catalyzed alkyne-nitrile oxide 1,3-dipolar cycloaddition and esterification chemistry. This water soluble amphiphilic telodendrimer acts as a macromolecular biologically active agent and shows concentration dependent reduction of glioblastoma (U251) cell survival.
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Affiliation(s)
- Hossein Yazdani
- Department of Chemistry, McGill University, 801 Sherbrooke St. West, Montréal, QC H3A 0B8, Canada;
- Department of Chemistry, Shahid Beheshti University G.C., Tehran 1983963113, Iran;
| | - Esha Kaul
- Department of Pharmacology and Therapeutics, McGill University, 3655 Promenade Sir William Osler, Montréal, QC H3G 1Y6, Canada;
| | - Ayoob Bazgir
- Department of Chemistry, Shahid Beheshti University G.C., Tehran 1983963113, Iran;
| | - Dusica Maysinger
- Department of Pharmacology and Therapeutics, McGill University, 3655 Promenade Sir William Osler, Montréal, QC H3G 1Y6, Canada;
| | - Ashok Kakkar
- Department of Chemistry, McGill University, 801 Sherbrooke St. West, Montréal, QC H3A 0B8, Canada;
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7
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Alves VM, Hwang D, Muratov E, Sokolsky-Papkov M, Varlamova E, Vinod N, Lim C, Andrade CH, Tropsha A, Kabanov A. Cheminformatics-driven discovery of polymeric micelle formulations for poorly soluble drugs. SCIENCE ADVANCES 2019; 5:eaav9784. [PMID: 31249867 PMCID: PMC6594770 DOI: 10.1126/sciadv.aav9784] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 05/16/2019] [Indexed: 05/29/2023]
Abstract
Many drug candidates fail therapeutic development because of poor aqueous solubility. We have conceived a computer-aided strategy to enable polymeric micelle-based delivery of poorly soluble drugs. We built models predicting both drug loading efficiency (LE) and loading capacity (LC) using novel descriptors of drug-polymer complexes. These models were employed for virtual screening of drug libraries, and eight drugs predicted to have either high LE and high LC or low LE and low LC were selected. Three putative positives, as well as three putative negative hits, were confirmed experimentally (implying 75% prediction accuracy). Fortuitously, simvastatin, a putative negative hit, was found to have the desired micelle solubility. Podophyllotoxin and simvastatin (LE of 95% and 87% and LC of 43% and 41%, respectively) were among the top five polymeric micelle-soluble compounds ever studied experimentally. The success of the strategy described herein suggests its broad utility for designing drug delivery systems.
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Affiliation(s)
- Vinicius M. Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Duhyeong Hwang
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Department of Pharmaceutical Sciences, Federal University of Paraíba, Joao Pessoa, PB 58059, Brazil
| | - Marina Sokolsky-Papkov
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ekaterina Varlamova
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Natasha Vinod
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- UNC/NC State Joint Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Chaemin Lim
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Alexander Kabanov
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Laboratory of Chemical Design of Bionanomaterials, Faculty of Chemistry, M.V. Lomonosov Moscow State University, Moscow 119992, Russia
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8
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Volkova YA, Averina EB, Vasilenko DA, Sedenkova KN, Grishin YK, Bruheim P, Kuznetsova TS, Zefirov NS. Unexpected Heterocyclization of Electrophilic Alkenes by Tetranitromethane in the Presence of Triethylamine. Synthesis of 5-Nitroisoxazoles. J Org Chem 2019; 84:3192-3200. [PMID: 30726081 DOI: 10.1021/acs.joc.8b03086] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A novel reaction of tetranitromethane with electrophilic alkenes in the presence of triethylamine affording substituted 5-nitroisoxazoles is described. Triethylamine reacts with tetranitromethane to generate N-nitrotriethylammonium and trinitromethanide. This process provides the heterocyclization of electrophilic alkenes. A variety of α,β-unsaturated aldehydes, ketones, esters, amides, phosphonates, nitro, and sulfur compounds was involved in the heterocyclization reaction, and a wide range of functionalized 5-nitroisoxazoles was obtained in good to high yields. The scope and limitations of the reaction and the mechanistic proposal are discussed.
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Affiliation(s)
- Yulia A Volkova
- Department of Chemistry , Lomonosov Moscow State University , Leninskie Gory, 1-3 , Moscow 119992 , Russia
| | - Elena B Averina
- Department of Chemistry , Lomonosov Moscow State University , Leninskie Gory, 1-3 , Moscow 119992 , Russia
| | - Dmitry A Vasilenko
- Department of Chemistry , Lomonosov Moscow State University , Leninskie Gory, 1-3 , Moscow 119992 , Russia
| | - Kseniya N Sedenkova
- Department of Chemistry , Lomonosov Moscow State University , Leninskie Gory, 1-3 , Moscow 119992 , Russia
| | - Yuri K Grishin
- Department of Chemistry , Lomonosov Moscow State University , Leninskie Gory, 1-3 , Moscow 119992 , Russia
| | - Per Bruheim
- Department of Biotechnology , Norwegian University of Science and Technology , Sem Selands vei 6/8 , N-7431 Trondheim , Norway
| | - Tamara S Kuznetsova
- Department of Chemistry , Lomonosov Moscow State University , Leninskie Gory, 1-3 , Moscow 119992 , Russia
| | - Nikolai S Zefirov
- Department of Chemistry , Lomonosov Moscow State University , Leninskie Gory, 1-3 , Moscow 119992 , Russia
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9
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Kalita SJ, Deka DC. A Molecular Hybridization Approach for Simple and Expeditious Synthesis of Novel Spiro[oxindoline‐3, 4′‐isoxazolo[5, 4‐
b
]pyrazolo[4, 3‐
e
]pyridines] in Water. ChemistrySelect 2018. [DOI: 10.1002/slct.201801545] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Subarna Jyoti Kalita
- Department of ChemistryUniversity of Gauhati, G. B. Nagar Guwahati 781014, Assam India
| | - Dibakar Chandra Deka
- Department of ChemistryUniversity of Gauhati, G. B. Nagar Guwahati 781014, Assam India
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10
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Chauhan S, Kumar A. Consensus QSAR modelling of SIRT1 activators using simplex representation of molecular structure. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2018; 29:277-294. [PMID: 29390919 DOI: 10.1080/1062936x.2018.1426626] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Accepted: 01/08/2018] [Indexed: 06/07/2023]
Abstract
Hierarchical QSAR technology (HiT QSAR) was used for consensus QSAR modelling of 65 SIRT1 activators. Simplex representation of molecular structure (SiRMS) has been used for descriptor generation. The predictive QSAR models were developed using the partial least squares (PLS) method. The QSAR models were built up according to OECD principles. One hundred rounds of Y-scrambling were performed for each selected model to exclude chance correlations. A successful consensus model (r2 = 0.830, [Formula: see text] = 0.754) was obtained from the five best QSAR models. Leverage, ellipsoid and local tree domain of applicability (DA) approaches have been used for evaluation of the quality of predictions. Molecular fragments responsible for an increase and decrease of the activation properties have been determined by mechanistic interpretation of the developed QSAR model.
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Affiliation(s)
- S Chauhan
- a Department of Pharmaceutical Sciences , Guru Jambheshwar University of Science and Technology , Hisar , India
| | - A Kumar
- a Department of Pharmaceutical Sciences , Guru Jambheshwar University of Science and Technology , Hisar , India
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11
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Polishchuk P. Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future. J Chem Inf Model 2017; 57:2618-2639. [DOI: 10.1021/acs.jcim.7b00274] [Citation(s) in RCA: 120] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Pavel Polishchuk
- Institute of Molecular and
Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital in Olomouc, Hněvotínská
1333/5, 779 00 Olomouc, Czech Republic
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12
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Minovski N, Novič M. Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.
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13
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Alves V, Muratov E, Capuzzi S, Politi R, Low Y, Braga R, Zakharov AV, Sedykh A, Mokshyna E, Farag S, Andrade C, Kuz'min V, Fourches D, Tropsha A. Alarms about structural alerts. GREEN CHEMISTRY : AN INTERNATIONAL JOURNAL AND GREEN CHEMISTRY RESOURCE : GC 2016; 18:4348-4360. [PMID: 28503093 PMCID: PMC5423727 DOI: 10.1039/c6gc01492e] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.
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Affiliation(s)
- Vinicius Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Stephen Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Regina Politi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Yen Low
- Netflix, San Francisco, CA 94123, USA
| | - Rodolpho Braga
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Rockville, MD 20850, USA
| | | | - Elena Mokshyna
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Sherif Farag
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Carolina Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Victor Kuz'min
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Department of Chemistry and Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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14
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Neves BJ, Dantas RF, Senger MR, Melo-Filho CC, Valente WCG, de Almeida ACM, Rezende-Neto JM, Lima EFC, Paveley R, Furnham N, Muratov E, Kamentsky L, Carpenter AE, Braga RC, Silva-Junior FP, Andrade CH. Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening. J Med Chem 2016; 59:7075-88. [PMID: 27396732 PMCID: PMC5844225 DOI: 10.1021/acs.jmedchem.5b02038] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Schistosomiasis is a debilitating neglected tropical disease, caused by flatworms of Schistosoma genus. The treatment relies on a single drug, praziquantel (PZQ), making the discovery of new compounds extremely urgent. In this work, we integrated QSAR-based virtual screening (VS) of Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) inhibitors and high content screening (HCS) aiming to discover new antischistosomal agents. Initially, binary QSAR models for inhibition of SmTGR were developed and validated using the Organization for Economic Co-operation and Development (OECD) guidance. Using these models, we prioritized 29 compounds for further testing in two HCS platforms based on image analysis of assay plates. Among them, 2-[2-(3-methyl-4-nitro-5-isoxazolyl)vinyl]pyridine and 2-(benzylsulfonyl)-1,3-benzothiazole, two compounds representing new chemical scaffolds have activity against schistosomula and adult worms at low micromolar concentrations and therefore represent promising antischistosomal hits for further hit-to-lead optimization.
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Affiliation(s)
- Bruno J. Neves
- LabMol—Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia 74605-510, Brazil
| | - Rafael F. Dantas
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Mario R. Senger
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Cleber C. Melo-Filho
- LabMol—Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia 74605-510, Brazil
| | - Walter C. G. Valente
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Ana C. M. de Almeida
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - João M. Rezende-Neto
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Elid F. C. Lima
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Ross Paveley
- Department of Infection and Immunity, London School of Hygiene and Tropical Medicine, London WC1E 7HT, United Kingdom
| | - Nicholas Furnham
- Department of Infection and Immunity, London School of Hygiene and Tropical Medicine, London WC1E 7HT, United Kingdom
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill North Carolina 27955-7568, United States
| | - Lee Kamentsky
- Imaging Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02142, United States
| | - Anne E. Carpenter
- Imaging Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02142, United States
| | - Rodolpho C. Braga
- LabMol—Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia 74605-510, Brazil
| | - Floriano P. Silva-Junior
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Carolina Horta Andrade
- LabMol—Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia 74605-510, Brazil
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15
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Afraj SN, Nuzlia C, Chen C, Lee GH. Multicomponent Coupling Reaction and Intramolecular Nitrile Oxide-Alkyne Cycloaddition towards Isoxazolo[3,4]-pyrrolizines. ASIAN J ORG CHEM 2016. [DOI: 10.1002/ajoc.201600223] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Shakil N. Afraj
- Department of Chemistry; National Dong Hwa University; Soufeng Hualien 974 Taiwan
| | - Cut Nuzlia
- Department of Chemistry; National Dong Hwa University; Soufeng Hualien 974 Taiwan
| | - Chinpiao Chen
- Department of Chemistry; National Dong Hwa University; Soufeng Hualien 974 Taiwan
- Department of Nursing; Tzu Chi University of Science and Technology; Hualien 970 Taiwan
| | - Gene-Hsian Lee
- Instrumentation Center; National Taiwan University; Taipei 10617 Taiwan
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16
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Da Costa L, Roche M, Scheers E, Coluccia A, Neyts J, Terme T, Leyssen P, Silvestri R, Vanelle P. VP1 crystal structure-guided exploration and optimization of 4,5-dimethoxybenzene-based inhibitors of rhinovirus 14 infection. Eur J Med Chem 2016; 115:453-62. [PMID: 27049678 DOI: 10.1016/j.ejmech.2016.03.049] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 03/17/2016] [Accepted: 03/18/2016] [Indexed: 01/26/2023]
Abstract
Human rhinoviruses (HRV) are the predominant cause of common colds and flu-like illnesses, but are also responsible for virus-induced exacerbations of asthma and chronic obstructive pulmonary disease. However, to date, no drug has been approved yet for clinical use. In this study, we present the results of the structure-based lead optimization of a class of new small-molecule inhibitors that we previously reported to bind into the pocket beneath the canyon of the VP1 protein. A small series of analogues that we designed based on the available structure and interaction data were synthesized and evaluated for their potency to inhibit the replication of HRV serotype 14. 2-(4,5-Dimethoxy-2-nitrophenyl)-1-(4-(pyridin-4-yl)phenyl)ethanol (3v) was found to be a potent inhibitor exhibiting micromolar activity (EC50 = 3.4 ± 1.0 μM) with a toxicity for HeLa cells that was significantly lower than that of our previous hit (LPCRW_0005, CC50 = 104.0 ± 22.2 μM; 3v, CC50 > 263 μM).
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Affiliation(s)
- Laurène Da Costa
- Aix-Marseille Université, Institut de Chimie Radicalaire, UMR 7273 CNRS, 27 Boulevard Jean Moulin, Marseille, France
| | - Manon Roche
- Aix-Marseille Université, Institut de Chimie Radicalaire, UMR 7273 CNRS, 27 Boulevard Jean Moulin, Marseille, France
| | - Els Scheers
- KU Leuven-University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, B-3000 Leuven, Belgium
| | - Antonio Coluccia
- Institut Pasteur Italy, Department of Drug Chemistry and Technologies, Sapienza University, I-00185 Rome, Italy
| | - Johan Neyts
- KU Leuven-University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, B-3000 Leuven, Belgium.
| | - Thierry Terme
- Aix-Marseille Université, Institut de Chimie Radicalaire, UMR 7273 CNRS, 27 Boulevard Jean Moulin, Marseille, France
| | - Pieter Leyssen
- KU Leuven-University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, B-3000 Leuven, Belgium
| | - Romano Silvestri
- Institut Pasteur Italy, Department of Drug Chemistry and Technologies, Sapienza University, I-00185 Rome, Italy.
| | - Patrice Vanelle
- Aix-Marseille Université, Institut de Chimie Radicalaire, UMR 7273 CNRS, 27 Boulevard Jean Moulin, Marseille, France.
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17
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Zakharov AV, Varlamova EV, Lagunin AA, Dmitriev AV, Muratov EN, Fourches D, Kuz'min VE, Poroikov VV, Tropsha A, Nicklaus MC. QSAR Modeling and Prediction of Drug-Drug Interactions. Mol Pharm 2016; 13:545-56. [PMID: 26669717 DOI: 10.1021/acs.molpharmaceut.5b00762] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.
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Affiliation(s)
- Alexey V Zakharov
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
| | - Ekaterina V Varlamova
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine.,Chemical-Technological Department, Odessa National Polytechnic University , 1 Shevchenko Ave, Odessa 65000, Ukraine
| | - Alexey A Lagunin
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia.,Medico-Biological Department, Pirogov Russian National Research Medical University , Ostrovitianov str. 1, Moscow 117997, Russia
| | - Alexander V Dmitriev
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh, North Carolina 27695, United States
| | - Victor E Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
| | - Vladimir V Poroikov
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
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18
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Vaarla K, Rao VR, Akkurt M. Crystal structure of 3-benzyl-sulfanyl-6-(5-methyl-1,2-oxazol-3-yl)-1,2,4-triazolo[3,4-b][1,3,4]thia-diazole. Acta Crystallogr E Crystallogr Commun 2015; 71:o809-10. [PMID: 26594538 PMCID: PMC4645033 DOI: 10.1107/s2056989015017351] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Accepted: 09/17/2015] [Indexed: 11/16/2022]
Abstract
In the title compound, C14H11N5OS2, the triazolo-thia-diazole system is essentially planar (r.m.s. deviation = 0.002 Å) and makes dihedral angles of 6.33 (12) and 42.95 (14)° with the planes of the oxazole and phenyl rings, respectively. In the crystal, face-to-face π-π inter-actions are observed between the thia-diazole and oxazole rings [centroid-centroid distance = 3.4707 (18) Å], leading to columns along [010].
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Affiliation(s)
- Krishnaiah Vaarla
- Department of Chemistry, National Institute of Technology, Warangal, Telangana 506004, India
| | - V. Rajeswar Rao
- Department of Chemistry, National Institute of Technology, Warangal, Telangana 506004, India
| | - Mehmet Akkurt
- Department of Physics, Faculty of Sciences, Erciyes University, 38039 Kayseri, Turkey
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19
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Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicol Appl Pharmacol 2015; 284:262-72. [PMID: 25560674 PMCID: PMC4546933 DOI: 10.1016/j.taap.2014.12.014] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/14/2014] [Accepted: 12/21/2014] [Indexed: 12/20/2022]
Abstract
Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71-88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation.
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Affiliation(s)
- Vinicius M Alves
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA; Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Judy Strickland
- ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Nicole Kleinstreuer
- ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Carolina H Andrade
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
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20
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Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization. Toxicol Appl Pharmacol 2015; 284:273-80. [PMID: 25560673 PMCID: PMC4408226 DOI: 10.1016/j.taap.2014.12.013] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/14/2014] [Accepted: 12/21/2014] [Indexed: 12/02/2022]
Abstract
Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R2=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q2ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential.
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Affiliation(s)
- Vinicius M Alves
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA; Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Judy Strickland
- ILS/Contractor supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Nicole Kleinstreuer
- ILS/Contractor supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Carolina H Andrade
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
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21
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Abstract
The emphasis of this review is particularly on multivariate statistical methods currently used in quantitative structure–activity relationship (QSAR) studies.
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Affiliation(s)
- Somayeh Pirhadi
- Drug Design in Silico Lab
- Chemistry Faculty
- K. N. Toosi University of Technology
- Tehran
- Iran
| | | | - Jahan B. Ghasemi
- Drug Design in Silico Lab
- Chemistry Faculty
- K. N. Toosi University of Technology
- Tehran
- Iran
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22
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Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A. QSAR modeling: where have you been? Where are you going to? J Med Chem 2014; 57:4977-5010. [PMID: 24351051 PMCID: PMC4074254 DOI: 10.1021/jm4004285] [Citation(s) in RCA: 1053] [Impact Index Per Article: 105.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.
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Affiliation(s)
- Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, V6H3Z6, Canada
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Alexandre Varnek
- Department of Chemistry, L. Pasteur University of Strasbourg, Strasbourg, 67000, France
| | - Igor I. Baskin
- Department of Physics, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mark Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - John Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - Paola Gramatica
- Department of Structural and Functional Biology, University of Insubria, Varese, 21100, Italy
| | | | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Victor E. Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | | | - Romualdo Benigni
- Environment and Health Department, Istituto Superiore di Sanita’, Rome, 00161, Italy
| | | | - James Rathman
- Altamira LLC, Columbus OH 43235, USA
- Department of Chemical and Biomolecular Engineering, the Ohio State University, Columbus, OH 43215, USA
| | | | | | - Ann Richard
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27519, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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23
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Koufaki M, Fotopoulou T, Heropoulos GA. Synergistic effect of dual-frequency ultrasound irradiation in the one-pot synthesis of 3,5-disubstituted isoxazoles. ULTRASONICS SONOCHEMISTRY 2014; 21:35-39. [PMID: 23769747 DOI: 10.1016/j.ultsonch.2013.05.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2013] [Revised: 05/09/2013] [Accepted: 05/18/2013] [Indexed: 06/02/2023]
Abstract
Herein is reported a one-pot three-step process for the regioselective synthesis of 3,5-disubstituted isoxazoles based on copper(I)-catalyzed cycloaddition reaction between in situ generated nitrile oxides (from the corresponding aldehydes) and alkynes, using ultrasound irradiation, avoiding toxic reagents and solvents and isolation/purification of intermediates. The combined use of 40 kHz ultrasonic bath and 20 kHz probe in the presence of copper turnings reduced reaction time to 1h and resulted in only one final purification step with increased yields, clearly indicating that there is a dual-frequency synergistic effect. In addition, under metal free conditions, the 1,3-dipolar cycloaddition was regioselective giving low to modest yields.
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Affiliation(s)
- Maria Koufaki
- National Hellenic Research Foundation, Institute of Biology, Medicinal Chemistry and Biotechnology, 48, Vas. Constantinou Ave., Athens 11635, Greece.
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24
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Polishchuk PG, Kuz'min VE, Artemenko AG, Muratov EN. Universal Approach for Structural Interpretation of QSAR/QSPR Models. Mol Inform 2013; 32:843-53. [DOI: 10.1002/minf.201300029] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Accepted: 07/29/2013] [Indexed: 11/07/2022]
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25
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Fourches D, Muratov E, Ding F, Dokholyan NV, Tropsha A. Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches. J Chem Inf Model 2013; 53:1915-22. [PMID: 23809015 PMCID: PMC3779696 DOI: 10.1021/ci400216q] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
We report on the prediction accuracy of ligand-based (2D QSAR) and structure-based (MedusaDock) methods used both independently and in consensus for ranking the congeneric series of ligands binding to three protein targets (UK, ERK2, and CHK1) from the CSAR 2011 benchmark exercise. An ensemble of predictive QSAR models was developed using known binders of these three targets extracted from the publicly available ChEMBL database. Selected models were used to predict the binding affinity of CSAR compounds toward the corresponding targets and rank them accordingly; the overall ranking accuracy evaluated by Spearman correlation was as high as 0.78 for UK, 0.60 for ERK2, and 0.56 for CHK1, placing our predictions in the top 10% among all the participants. In parallel, MedusaDock, designed to predict reliable docking poses, was also used for ranking the CSAR ligands according to their docking scores; the resulting accuracy (Spearman correlation) for UK, ERK2, and CHK1 were 0.76, 0.31, and 0.26, respectively. In addition, performance of several consensus approaches combining MedusaDock- and QSAR-predicted ranks altogether has been explored; the best approach yielded Spearman correlation coefficients for UK, ERK2, and CHK1 of 0.82, 0.50, and 0.45, respectively. This study shows that (i) externally validated 2D QSAR models were capable of ranking CSAR ligands at least as accurately as more computationally intensive structure-based approaches used both by us and by other groups and (ii) ligand-based QSAR models can complement structure-based approaches by boosting the prediction performances when used in consensus.
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Affiliation(s)
- Denis Fourches
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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26
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Kolumbin O, Ognichenko L, Artemenko A, Polischuk P, Kulinsky М, Мuratov Е, Kuz’min V, Bobeica V. Nonexperimental Screening of the Water Solubility, Lipophilicity, Bioavailability, Mutagenicity and Toxicity of Various Pesticides with QSAR Models Aid. CHEMISTRY JOURNAL OF MOLDOVA 2013. [DOI: 10.19261/cjm.2013.08(1).12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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27
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Critical assessment of two classical synthetic methods for preparation of thiophene-substituted isoxazoles. RESEARCH ON CHEMICAL INTERMEDIATES 2013. [DOI: 10.1007/s11164-013-1101-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Martin TM, Harten P, Young DM, Muratov EN, Golbraikh A, Zhu H, Tropsha A. Does rational selection of training and test sets improve the outcome of QSAR modeling? J Chem Inf Model 2012; 52:2570-8. [PMID: 23030316 DOI: 10.1021/ci300338w] [Citation(s) in RCA: 161] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.
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Affiliation(s)
- Todd M Martin
- Sustainable Technology Division, National Risk Management Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, USA.
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Ramana PV, Reddy AR. Synthesis of 1,2,3-Triazole Substituted IsoxazolesviaCopper (I) Catalyzed Cycloaddition. J Heterocycl Chem 2012. [DOI: 10.1002/jhet.837] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- P. Venkata Ramana
- Department of Chemistry; University College of Science, Osmania University; Hyderabad 500 007 India
| | - A. Ram Reddy
- Department of Chemistry; University College of Science, Osmania University; Hyderabad 500 007 India
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Oprisiu I, Varlamova E, Muratov E, Artemenko A, Marcou G, Polishchuk P, Kuz'min V, Varnek A. QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids. Mol Inform 2012; 31:491-502. [DOI: 10.1002/minf.201200006] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2012] [Accepted: 04/23/2012] [Indexed: 11/11/2022]
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Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Kuz'min VE. Existing and Developing Approaches for QSAR Analysis of Mixtures. Mol Inform 2012; 31:202-21. [PMID: 27477092 DOI: 10.1002/minf.201100129] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2011] [Accepted: 02/04/2012] [Indexed: 11/10/2022]
Abstract
This review is devoted to the critical analysis of advantages and disadvantages of existing mixture descriptors and their usage in various QSAR/QSPR tasks. We describe good practices for the QSAR modeling of mixtures, data sources for mixtures, a discussion of various mixture descriptors and their application, recommendations about proper external validation specific for mixture QSAR modeling, and future perspectives of this field. The biggest problem in QSAR of mixtures is the lack of reliable data about the mixtures' properties. Various mixture descriptors are used for the modeling of different endpoints. However, these descriptors have certain disadvantages, such as applicability only to 1 : 1 binary mixtures, and additive nature. The field of QSAR of mixtures is still under development, and existing efforts could be considered as a foundation for future approaches and studies. The usage of non-additive mixture descriptors, which are sensitive to interaction effects, in combination with best practices of QSAR model development (e.g., thorough data collection and curation, rigorous external validation, etc.) will significantly improve the quality of QSAR studies of mixtures.
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Affiliation(s)
- Eugene N Muratov
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394. , .,Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, Eshelman School of Pharmacy, University of North Carolina, Beard Hall 301, CB#7568, Chapel Hill, NC, 27599, USA tel: +19199663459, fax: +19199660204. ,
| | - Ekaterina V Varlamova
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Anatoly G Artemenko
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Pavel G Polishchuk
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Victor E Kuz'min
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
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Ognichenko LN, Kuz'min VE, Gorb L, Hill FC, Artemenko AG, Polischuk PG, Leszczynski J. QSPR Prediction of Lipophilicity for Organic Compounds Using Random Forest Technique on the Basis of Simplex Representation of Molecular Structure. Mol Inform 2012; 31:273-80. [PMID: 27477097 DOI: 10.1002/minf.201100102] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2011] [Accepted: 02/05/2012] [Indexed: 11/08/2022]
Abstract
The relationship between the octanol-water partition coefficient for more than twelve thousand organic compounds and their structures was investigated using a QSPR approach based on Simplex Representation of Molecular Structure (SiRMS). The dataset used in our study included 10973 compounds with experimental values of lipophilicity (LogKow ) for different chemical compounds. Random Forest (RF) method was used for statistical modeling at the 2D level of representation of molecular structure. Developed models are adequate and successfully validated with external test sets. Proposed models have clear interpretation due to the use of simplex representation of molecular structure and predict the LogKow values with the accuracy of the best modern models. Thus QSPR models proposed in this study represent powerful and easy-to use virtual screening tool that can be recommended for prediction of octanol-water partition coefficient.
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Affiliation(s)
- Liudmyla N Ognichenko
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Victor E Kuz'min
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Leonid Gorb
- Badger Technical Services, LLC, Vicksburg, Mississippi, USA
| | - Frances C Hill
- US Army ERDC, 3532 Manor Dr, Vicksburg, Mississippi, 39180, USA
| | - Anatoly G Artemenko
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Pavel G Polischuk
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Jerzy Leszczynski
- US Army ERDC, 3532 Manor Dr, Vicksburg, Mississippi, 39180, USA. .,Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, 39217, USA.
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Koufaki M, Tsatsaroni A, Alexi X, Guerrand H, Zerva S, Alexis MN. Isoxazole substituted chromans against oxidative stress-induced neuronal damage. Bioorg Med Chem 2011; 19:4841-50. [DOI: 10.1016/j.bmc.2011.06.074] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2011] [Revised: 06/24/2011] [Accepted: 06/26/2011] [Indexed: 11/16/2022]
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QSAR analysis of [(biphenyloxy)propyl]isoxazoles: agents against coxsackievirus B3. Future Med Chem 2011; 3:15-27. [PMID: 21428823 DOI: 10.4155/fmc.10.278] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Antiviral drugs are urgently needed for the treatment of acute and chronic diseases caused by enteroviruses such as coxsackievirus B3 (CVB3). The main goal of this study is quantitative structure-activity relationship (QSAR) analysis of anti-CVB3 activity (clinical CVB3 isolate 97927 [log IC50, µM]) and investigation of the selectivity of 25 ([biphenyloxy]propyl)isoxazoles, followed by computer-aided design and virtual screening of novel active compounds. DISCUSSION The 2D QSAR obtained models are quite satisfactory (R(2) = 0.84-0.99, Q(2) = 0.76-0.92, R(2)(ext) = 0.62-0.79). Compounds with high antiviral activity and selectivity have to contain 5-trifluoromethyl-[1,2,4]oxadiazole or 2,4-difluorophenyl fragments. Insertion of 2,5-dimethylbenzene, napthyl and especially biphenyl substituents into investigated compounds substantially decreases both their antiviral activity and selectivity. Several compounds were proposed as a result of design and virtual screening. A high level of activity of 2-methoxy-1-phenyl-1H-imidazo[4,5-c]pyridine (sm428) was confirmed experimentally. CONCLUSION Simplex representation of molecular structure allows successful QSAR analysis of anti-CVB3 activity of ([biphenyloxy]propyl)isoxazole derivatives. Two possible ways of battling CVB3 are considered as a future perspective.
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Kuz'min VE, Polishchuk PG, Artemenko AG, Andronati SA. Interpretation of QSAR Models Based on Random Forest Methods. Mol Inform 2011; 30:593-603. [DOI: 10.1002/minf.201000173] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2010] [Accepted: 05/13/2011] [Indexed: 11/07/2022]
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Artemenko A, Muratov EN, Kuz’min V, Muratov N, Varlamova E, Kuz'mina A, Gorb LG, Golius A, Hill F, Leszczynski J, Tropsha A. QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2011; 22:575-601. [PMID: 21714735 PMCID: PMC3442116 DOI: 10.1080/1062936x.2011.569950] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC₅₀) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. The Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. The Partial Least Squares (PLS) statistical approach was then used to develop 2D QSAR models. Validated PLS models were explored to: (1) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (2) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; and (3) analyse the role of various physical-chemical factors responsible for compounds' toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (r²(ext) = 0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modelling and 76% for external set).
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Affiliation(s)
- A.G. Artemenko
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
| | - E. N. Muratov
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
| | - V.E. Kuz’min
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
| | - N.N. Muratov
- Odessa National Polytechnic University, 1 Shevchenko Ave., Odessa, 65000, Ukraine
| | - E.V. Varlamova
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
| | - A.V. Kuz'mina
- Odessa National Medicinal University, 2 Ol'gievskaya Str, Odessa, 65000, Ukraine
| | - L. G. Gorb
- Badger Technical Services, LLC, Vicksburg, Mississippi, USA
| | - A. Golius
- Kharkiv National V.N. Karazin University, Department of Radophysics, Karkiv, 61077, Ukraine
| | - F.C. Hill
- US Army ERDC, 3532 Manor Dr, Vicksburg, Mississippi, 39180, USA
| | - J. Leszczynski
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
| | - A. Tropsha
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
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Rollinger JM, Schmidtke M. The human rhinovirus: human-pathological impact, mechanisms of antirhinoviral agents, and strategies for their discovery. Med Res Rev 2011; 31:42-92. [PMID: 19714577 PMCID: PMC7168442 DOI: 10.1002/med.20176] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
As the major etiological agent of the common cold, human rhinoviruses (HRV) cause millions of lost working and school days annually. Moreover, clinical studies proved an association between harmless upper respiratory tract infections and more severe diseases e.g. sinusitis, asthma, and chronic obstructive pulmonary disease. Both the medicinal and socio-economic impact of HRV infections and the lack of antiviral drugs substantiate the need for intensive antiviral research. A common structural feature of the approximately 100 HRV serotypes is the icosahedrally shaped capsid formed by 60 identical copies of viral capsid proteins VP1-4. The capsid protects the single-stranded, positive sense RNA genome of about 7,400 bases in length. Both structural as well as nonstructural proteins produced during the viral life cycle have been identified as potential targets for blocking viral replication at the step of attachment, entry, uncoating, RNA and protein synthesis by synthetic or natural compounds. Moreover, interferon and phytoceuticals were shown to protect host cells. Most of the known inhibitors of HRV replication were discovered as a result of empirical or semi-empirical screening in cell culture. Structure-activity relationship studies are used for hit optimization and lead structure discovery. The increasing structural insight and molecular understanding of viral proteins on the one hand and the advent of innovative computer-assisted technologies on the other hand have facilitated a rationalized access for the discovery of small chemical entities with antirhinoviral (anti-HRV) activity. This review will (i) summarize existing structural knowledge about HRV, (ii) focus on mechanisms of anti-HRV agents from synthetic and natural origin, and (iii) demonstrate strategies for efficient lead structure discovery.
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Affiliation(s)
- Judith M Rollinger
- Institute of Pharmacy/Pharmacognosy and Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innrain 52c, A-6020 Innsbruck, Austria.
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Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf Model 2010; 50:1189-204. [PMID: 20572635 DOI: 10.1021/ci100176x] [Citation(s) in RCA: 480] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Denis Fourches
- Laboratory for Molecular Modeling, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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Fatemi MH, Ghorbanzad’e M. Classification of drugs according to their milk/plasma concentration ratio. Eur J Med Chem 2010; 45:5051-5. [DOI: 10.1016/j.ejmech.2010.08.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2010] [Revised: 07/24/2010] [Accepted: 08/07/2010] [Indexed: 11/12/2022]
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Sushko I, Novotarskyi S, Körner R, Pandey AK, Cherkasov A, Li J, Gramatica P, Hansen K, Schroeter T, Müller KR, Xi L, Liu H, Yao X, Öberg T, Hormozdiari F, Dao P, Sahinalp C, Todeschini R, Polishchuk P, Artemenko A, Kuz’min V, Martin TM, Young DM, Fourches D, Muratov E, Tropsha A, Baskin I, Horvath D, Marcou G, Muller C, Varnek A, Prokopenko VV, Tetko IV. Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set. J Chem Inf Model 2010; 50:2094-111. [DOI: 10.1021/ci100253r] [Citation(s) in RCA: 172] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Iurii Sushko
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Sergii Novotarskyi
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Robert Körner
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Anil Kumar Pandey
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Artem Cherkasov
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Jiazhong Li
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Paola Gramatica
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Katja Hansen
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Timon Schroeter
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Klaus-Robert Müller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Lili Xi
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Huanxiang Liu
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Xiaojun Yao
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Tomas Öberg
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Farhad Hormozdiari
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Phuong Dao
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Cenk Sahinalp
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Roberto Todeschini
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Pavel Polishchuk
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Anatoliy Artemenko
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Victor Kuz’min
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Todd M. Martin
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Douglas M. Young
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Denis Fourches
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Eugene Muratov
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Alexander Tropsha
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Igor Baskin
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Dragos Horvath
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Gilles Marcou
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Christophe Muller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Alexander Varnek
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Volodymyr V. Prokopenko
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
| | - Igor V. Tetko
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen—German Research Center for Environmental Health (GmbH), Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany, University of British Columbia, Vancouver Prostate Centre, 2660 Oak str., Vancouver, BC, V6H 3Z6, Canada, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, Via Dunant 3, Varese 21100, Italy, Machine Learning Department, Technical
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Per aspera ad astra: application of Simplex QSAR approach in antiviral research. Future Med Chem 2010; 2:1205-26. [DOI: 10.4155/fmc.10.194] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
This review explores the application of the Simplex representation of molecular structure (SiRMS) QSAR approach in antiviral research. We provide an introduction to and description of SiRMS, its application in antiviral research and future directions of development of the Simplex approach and the whole QSAR field. In the Simplex approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality and symmetry). The main advantages of SiRMS are consideration of the different physical–chemical properties of atoms, high adequacy and good interpretability of models obtained and clear procedures for molecular design. The reliability of developed QSAR models as predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic and biological experiments. The SiRMS approach is realized as the complex of the computer program ‘HiT QSAR’, which is available on request.
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Roy B, De RN. Enhanced rate of intramolecular nitrile oxide cycloaddition and rapid synthesis of isoxazoles and isoxazolines. MONATSHEFTE FUR CHEMIE 2010. [DOI: 10.1007/s00706-010-0323-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Kovdienko NA, Polishchuk PG, Muratov EN, Artemenko AG, Kuz'min VE, Gorb L, Hill F, Leszczynski J. Application of Random Forest and Multiple Linear Regression Techniques to QSPR Prediction of an Aqueous Solubility for Military Compounds. Mol Inform 2010; 29:394-406. [DOI: 10.1002/minf.201000001] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2010] [Accepted: 03/11/2010] [Indexed: 11/08/2022]
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Muratov EN, Kuz'min VE, Artemenko AG, Kovdienko NA, Gorb L, Hill F, Leszczynski J. New QSPR equations for prediction of aqueous solubility for military compounds. CHEMOSPHERE 2010; 79:887-890. [PMID: 20233619 DOI: 10.1016/j.chemosphere.2010.02.030] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2010] [Accepted: 02/11/2010] [Indexed: 05/28/2023]
Abstract
The development of a new quantitative structure-property relationship (QSPR) model to predict aqueous solubility (S(w)) accurately for compounds of military interest is presented. The ability of the new model to predict solubility is assessed and compared to available experimental data. A large set of structurally diverse organic compounds was used in this analysis. SiRMS methodology was employed to develop PLS models based on 135 training compounds and predictive accuracy was tested for 155 compounds selected for that purpose. The use of descriptors calculated only from the 2D level of representation of molecular structure produces a well-fitted and robust QSPR model (R(2)=0.90; Q(2)=0.87). Predictive ability for the model produced in this study on external test set (R(test)(2)=0.81) is comparable to the predictive ability of EPI Suite 4.0. Consensus solubility predictions using SiRMS and EPI models for 25 compounds of military interest (not included into the training set) have been completed.
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Affiliation(s)
- Eugene N Muratov
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
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46
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Volkova YA, Averina EB, Grishin YK, Bruheim P, Kuznetsova TS, Zefirov NS. Unexpected Heterocyclization of Electrophilic Alkenes by Tetranitromethane in the Presence of Triethylamine. Synthesis of 3-Nitroisoxazoles. J Org Chem 2010; 75:3047-52. [DOI: 10.1021/jo100319p] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yulia A. Volkova
- Lomonosov Moscow State University,
Department of Chemistry, Leninskie Gory, 1-3, Moscow 119992, Russia
| | - Elena B. Averina
- Lomonosov Moscow State University,
Department of Chemistry, Leninskie Gory, 1-3, Moscow 119992, Russia
| | - Yuri K. Grishin
- Lomonosov Moscow State University,
Department of Chemistry, Leninskie Gory, 1-3, Moscow 119992, Russia
| | - Per Bruheim
- Department of Biotechnology,
Norwegian University of Science and Technology, Sem Selands vei 6/8,
N-7431 Trondheim, Norway
| | - Tamara S. Kuznetsova
- Lomonosov Moscow State University,
Department of Chemistry, Leninskie Gory, 1-3, Moscow 119992, Russia
| | - Nikolai S. Zefirov
- Lomonosov Moscow State University,
Department of Chemistry, Leninskie Gory, 1-3, Moscow 119992, Russia
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Polishchuk PG, Muratov EN, Artemenko AG, Kolumbin OG, Muratov NN, Kuz’min VE. Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity. J Chem Inf Model 2009; 49:2481-8. [DOI: 10.1021/ci900203n] [Citation(s) in RCA: 122] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Pavel G. Polishchuk
- Laboratory on Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine, Laboratory of Molecular Modeling, School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, Department of Chemistry, Pridnestrovskij State University, Tiraspol, MD-3300, Chemical-Technological Department, Odessa National Polytechnic University, Odessa 65000, Ukraine
| | - Eugene N. Muratov
- Laboratory on Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine, Laboratory of Molecular Modeling, School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, Department of Chemistry, Pridnestrovskij State University, Tiraspol, MD-3300, Chemical-Technological Department, Odessa National Polytechnic University, Odessa 65000, Ukraine
| | - Anatoly G. Artemenko
- Laboratory on Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine, Laboratory of Molecular Modeling, School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, Department of Chemistry, Pridnestrovskij State University, Tiraspol, MD-3300, Chemical-Technological Department, Odessa National Polytechnic University, Odessa 65000, Ukraine
| | - Oleg G. Kolumbin
- Laboratory on Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine, Laboratory of Molecular Modeling, School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, Department of Chemistry, Pridnestrovskij State University, Tiraspol, MD-3300, Chemical-Technological Department, Odessa National Polytechnic University, Odessa 65000, Ukraine
| | - Nail N. Muratov
- Laboratory on Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine, Laboratory of Molecular Modeling, School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, Department of Chemistry, Pridnestrovskij State University, Tiraspol, MD-3300, Chemical-Technological Department, Odessa National Polytechnic University, Odessa 65000, Ukraine
| | - Victor E. Kuz’min
- Laboratory on Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine, Laboratory of Molecular Modeling, School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, Department of Chemistry, Pridnestrovskij State University, Tiraspol, MD-3300, Chemical-Technological Department, Odessa National Polytechnic University, Odessa 65000, Ukraine
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Puzyn T, Leszczynski J, Cronin MT. Virtual Screening and Molecular Design Based on Hierarchical Qsar Technology. RECENT ADVANCES IN QSAR STUDIES 2009; 8. [PMCID: PMC7120998 DOI: 10.1007/978-1-4020-9783-6_5] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
This chapter is devoted to the hierarchical QSAR technology (HiT QSAR) based on simplex representation of molecular structure (SiRMS) and its application to different QSAR/QSPR tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) of the QSAR paradigm by a series of enhanced models based on molecular structure description (in a specific order from 1D to 4D). Actually, it’s a system of permanently improved solutions. Different approaches for domain applicability estimation are implemented in HiT QSAR. In the SiRMS approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality, and symmetry). The level of simplex descriptors detailed increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach presented are an ability to solve QSAR/QSPR tasks for mixtures of compounds, the absence of the “molecular alignment” problem, consideration of different physical–chemical properties of atoms (e.g., charge, lipophilicity), and the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of HiT QSAR was demonstrated by its comparison with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D–4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as the predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic, biological, etc. experiments. The HiT QSAR is realized as the suite of computer programs termed the “HiT QSAR” software that so includes powerful statistical capabilities and a number of useful utilities.
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Affiliation(s)
- Tomasz Puzyn
- Dept. Chemistry, University of Gdansk, ul. Jana Sobieskiego 18, Gdansk, 80-952 Poland
| | - Jerzy Leszczynski
- Dept. Chemistry, Jackson State University, J. R. Lynch St. 1325, Jackson, 39217 U.S.A
| | - Mark T. Cronin
- Dept. Chemistry, John Moores University, Byrom St., Liverpool, L3 3AF United Kingdom
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Ognichenko L, Kuz'min V, Artemenko A. New Structural Descriptors of Molecules on the Basis of Symbiosis of the Informational Field Model and Simplex Representation of Molecular Structure. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200860073] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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50
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Kuz'min V, Muratov E, Artemenko A, Varlamova E, Gorb L, Wang J, Leszczynski J. Consensus QSAR Modeling of Phosphor-Containing Chiral AChE Inhibitors. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200860117] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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