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Stankovic B, Marinkovic F. A novel procedure for selection of molecular descriptors: QSAR model for mutagenicity of nitroaromatic compounds. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:54603-54617. [PMID: 39207617 DOI: 10.1007/s11356-024-34800-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 08/22/2024] [Indexed: 09/04/2024]
Abstract
Nitroaromatic compounds (NACs) stand out as pervasive organic pollutants, prompting an imperative need to investigate their hazardous effects. Computational chemistry methods play a crucial role in this exploration, offering a safer and more time-efficient approach, mandated by various legislations. In this study, our focus lay on the development of transparent, interpretable, reproducible, and publicly available methodologies aimed at deriving quantitative structure-activity relationship models and testing them by modelling the mutagenicity of NACs against the Salmonella typhimurium TA100 strain. Descriptors were selected from Mordred and RDKit molecular descriptors, along with several quantum chemistry descriptors. For that purpose, the genetic algorithm (GA), as the most widely used method in the literature, and three alternative algorithms (Boruta, Featurewiz, and ForwardSelector) combined with the forward stepwise selection technique were used. The construction of models utilized the multiple linear regression method, with subsequent scrutiny of fitting and predictive performance, reliability, and robustness through various statistical validation criteria. The models were ranked by the Multi-Criteria Decision Making procedure. Findings have revealed that the proposed methodology for descriptor selection outperforms GA, with Featurewiz showing a slight advantage over Boruta and ForwardSelector. These constructed models can serve as valuable tools for the quick and reliable prediction of NACs mutagenicity.
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Affiliation(s)
- Branislav Stankovic
- Department for Nuclear and Plasma Physics, Vinča Institute of Nuclear Sciences -National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia.
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2
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Melo-Filho CC, Su G, Liu K, Muratov EN, Tropsha A, Liu J. Modeling interactions between Heparan sulfate and proteins based on the Heparan sulfate microarray analysis. Glycobiology 2024; 34:cwae039. [PMID: 38836441 PMCID: PMC11180703 DOI: 10.1093/glycob/cwae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 04/30/2024] [Accepted: 05/29/2024] [Indexed: 06/06/2024] Open
Abstract
Heparan sulfate (HS), a sulfated polysaccharide abundant in the extracellular matrix, plays pivotal roles in various physiological and pathological processes by interacting with proteins. Investigating the binding selectivity of HS oligosaccharides to target proteins is essential, but the exhaustive inclusion of all possible oligosaccharides in microarray experiments is impractical. To address this challenge, we present a hybrid pipeline that integrates microarray and in silico techniques to design oligosaccharides with desired protein affinity. Using fibroblast growth factor 2 (FGF2) as a model protein, we assembled an in-house dataset of HS oligosaccharides on microarrays and developed two structural representations: a standard representation with all atoms explicit and a simplified representation with disaccharide units as "quasi-atoms." Predictive Quantitative Structure-Activity Relationship (QSAR) models for FGF2 affinity were developed using the Random Forest (RF) algorithm. The resulting models, considering the applicability domain, demonstrated high predictivity, with a correct classification rate of 0.81-0.80 and improved positive predictive values (PPV) up to 0.95. Virtual screening of 40 new oligosaccharides using the simplified model identified 15 computational hits, 11 of which were experimentally validated for high FGF2 affinity. This hybrid approach marks a significant step toward the targeted design of oligosaccharides with desired protein interactions, providing a foundation for broader applications in glycobiology.
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Affiliation(s)
- Cleber C Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Beard Hall, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Guowei Su
- Glycan Therapeutics, 617 Hutton Street, Raleigh, NC 27606, United States
| | - Kevin Liu
- Glycan Therapeutics, 617 Hutton Street, Raleigh, NC 27606, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Beard Hall, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Beard Hall, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Jian Liu
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, 1044 Genetic Medicine Bldg., University of North Carolina, Chapel Hill, NC 27599, United States
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3
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Rath M, Wellnitz J, Martin HJ, Melo-Filho C, Hochuli JE, Silva GM, Beasley JM, Travis M, Sessions ZL, Popov KI, Zakharov AV, Cherkasov A, Alves V, Muratov EN, Tropsha A. Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles. J Med Chem 2024; 67:6508-6518. [PMID: 38568752 DOI: 10.1021/acs.jmedchem.3c02446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.
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Affiliation(s)
- Marielle Rath
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - James Wellnitz
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Holli-Joi Martin
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Cleber Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Joshua E Hochuli
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Guilherme Martins Silva
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Jon-Michael Beasley
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Maxfield Travis
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Zoe L Sessions
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Konstantin I Popov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H3Z6, Canada
| | - Vinicius Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
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4
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Alves VM, Yasgar A, Wellnitz J, Rai G, Rath M, Braga RC, Capuzzi SJ, Simeonov A, Muratov EN, Zakharov AV, Tropsha A. Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds. J Med Chem 2023; 66:12828-12839. [PMID: 37677128 DOI: 10.1021/acs.jmedchem.3c00482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.
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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, North Carolina 27599, United States
| | - Adam Yasgar
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - James Wellnitz
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Ganesha Rai
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Marielle Rath
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | | | - Stephen J Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059, Brazil
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
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5
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Daghighi A, Casanola-Martin GM, Timmerman T, Milenković D, Lučić B, Rasulev B. In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach. TOXICS 2022; 10:toxics10120746. [PMID: 36548579 PMCID: PMC9786026 DOI: 10.3390/toxics10120746] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/10/2022] [Accepted: 11/28/2022] [Indexed: 06/02/2023]
Abstract
In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD50). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R2 = 0.88), validation set (R2 = 0.95), and true external test set (R2 = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.
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Affiliation(s)
- Amirreza Daghighi
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58105, USA
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | | | - Troy Timmerman
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Department of Computer Science, North Dakota State University, Fargo, ND 58105, USA
| | - Dejan Milenković
- Department of Science, Institute for Information Technologies, University of Kragujevac, 34000 Kragujevac, Serbia
| | - Bono Lučić
- NMR Centre, Ruđer Bošković Institute, 10000 Zagreb, Croatia
| | - Bakhtiyor Rasulev
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58105, USA
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
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6
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Alves VM, Borba JVB, Braga RC, Korn DR, Kleinstreuer N, Causey K, Tropsha A, Rua D, Muratov EN. PreS/MD: Predictor of Sensitization Hazard for Chemical Substances Released From Medical Devices. Toxicol Sci 2022; 189:250-259. [PMID: 35916740 PMCID: PMC9516038 DOI: 10.1093/toxsci/kfac078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
In the United States, a pre-market regulatory submission for any medical device that comes into contact with either a patient or the clinical practitioner must include an adequate toxicity evaluation of chemical substances that can be released from the device during its intended use. These substances, also referred to as extractables and leachables, must be evaluated for their potential to induce sensitization/allergenicity, which traditionally has been done in animal assays such as the guinea pig maximization test (GPMT). However, advances in basic and applied science are continuously presenting opportunities to employ new approach methodologies, including computational methods which, when qualified, could replace animal testing methods to support regulatory submissions. Herein, we developed a new computational tool for rapid and accurate prediction of the GPMT outcome that we have named PreS/MD (predictor of sensitization for medical devices). To enable model development, we (1) collected, curated, and integrated the largest publicly available dataset for GPMT results; (2) succeeded in developing externally predictive (balanced accuracy of 70%-74% as evaluated by both 5-fold external cross-validation and testing of novel compounds) quantitative structure-activity relationships (QSAR) models for GPMT using machine learning algorithms, including deep learning; and (3) developed a publicly accessible web portal integrating PreS/MD models that can predict GPMT outcomes for any molecule of interest. We expect that PreS/MD will be used by both industry and regulatory scientists in medical device safety assessments and help replace, reduce, or refine the use of animals in toxicity testing. PreS/MD is freely available at https://presmd.mml.unc.edu/.
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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, North Carolina 27599, USA
| | - Joyce V B Borba
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | | | - Daniel R Korn
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - Nicole Kleinstreuer
- National Toxicology Program, Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27560, USA
| | - Kevin Causey
- Predictive, LLC, Research Triangle Park, North Carolina, 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, North Carolina 27599, USA
- Predictive, LLC, Research Triangle Park, North Carolina, USA
| | - Diego Rua
- Division of Biology, Chemistry, and Materials Science, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, USA
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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7
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Hochuli J, Jain S, Melo-Filho C, Sessions ZL, Bobrowski T, Choe J, Zheng J, Eastman R, Talley DC, Rai G, Simeonov A, Tropsha A, Muratov EN, Baljinnyam B, Zakharov AV. Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents. ACS Pharmacol Transl Sci 2022; 5:468-478. [PMID: 35821746 PMCID: PMC9236207 DOI: 10.1021/acsptsci.2c00049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for acute treatment of the disease. We investigate whether compounds that bind the human angiotensin-converting enzyme 2 (ACE2) protein can decrease SARS-CoV-2 replication without impacting ACE2's natural enzymatic function. Initial screening of a diversity library resulted in hit compounds active in an ACE2-binding assay, which showed little inhibition of ACE2 enzymatic activity (116 actives, success rate ∼4%), suggesting they were allosteric binders. Subsequent application of in silico techniques boosted success rates to ∼14% and resulted in 73 novel confirmed ACE2 binders with K d values as low as 6 nM. A subsequent SARS-CoV-2 assay revealed that five of these compounds inhibit the viral life cycle in human cells. Further effort is required to completely elucidate the antiviral mechanism of these ACE2-binders, but they present a valuable starting point for both the development of acute treatments for COVID-19 and research into the host-directed therapy.
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Affiliation(s)
- Joshua
E. Hochuli
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
- Curriculum
in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Sankalp Jain
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Cleber Melo-Filho
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Zoe L. Sessions
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Tesia Bobrowski
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Jun Choe
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Johnny Zheng
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Richard Eastman
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Daniel C. Talley
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Ganesha Rai
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Anton Simeonov
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Alexander Tropsha
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Eugene N. Muratov
- Molecular
Modeling Laboratory, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Bolormaa Baljinnyam
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
| | - Alexey V. Zakharov
- National
Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States
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8
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Hochuli JE, Jain S, Melo-filho C, Sessions ZL, Bobrowski T, Choe J, Zheng J, Eastman R, Talley DC, Rai G, Simeonov A, Tropsha A, Muratov EN, Baljinnyam B, Zakharov AV. Allosteric binders of ACE2 are promising anti-SARS-CoV-2 agents.. [PMID: 35313579 PMCID: PMC8936107 DOI: 10.1101/2022.03.15.484484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
AbstractThe COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for an acute treatment for the disease. We investigate whether compounds that bind the human ACE2 protein can interrupt SARS-CoV-2 replication without damaging ACE2’s natural enzymatic function. Initial compounds were screened for binding to ACE2 but little interruption of ACE2 enzymatic activity. This set of compounds was extended by application of quantitative structure-activity analysis, which resulted in 512 virtual hits for further confirmatory screening. A subsequent SARS-CoV-2 replication assay revealed that five of these compounds inhibit SARS-CoV-2 replication in human cells. Further effort is required to completely determine the antiviral mechanism of these compounds, but they serve as a strong starting point for both development of acute treatments for COVID-19 and research into the mechanism of infection.Abstract FigureTOC Graphic: Overall study design.
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9
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Huang T, Sun G, Zhao L, Zhang N, Zhong R, Peng Y. Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review. Int J Mol Sci 2021; 22:8557. [PMID: 34445263 PMCID: PMC8395302 DOI: 10.3390/ijms22168557] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/05/2021] [Accepted: 08/05/2021] [Indexed: 01/22/2023] Open
Abstract
Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.
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Affiliation(s)
- Tao Huang
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Guohui Sun
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Lijiao Zhao
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Na Zhang
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Rugang Zhong
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, College of Environmental and Chemical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China;
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10
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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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11
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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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12
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Soares Rodrigues GC, Maia MDS, Silva Cavalcanti AB, Costa Barros RP, Scotti L, Cespedes-Acuña CL, Muratov EN, Scotti MT. Computer-assisted discovery of compounds with insecticidal activity against Musca domestica and Mythimna separata. Food Chem Toxicol 2020; 147:111899. [PMID: 33279675 DOI: 10.1016/j.fct.2020.111899] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/25/2020] [Accepted: 11/30/2020] [Indexed: 11/18/2022]
Abstract
Pesticides are used to control and combat insects and pests in the agricultural sector, households, and public health programs. The frequent and disorderly use of these pesticides may lead to variety of undesired effects. Therefore, natural products have many advantages over to synthetic compounds to be used as insecticides. The goal of this study was to find natural products with insecticidal potential against Musca domestica and Mythimna separata. To achieve this goal, we developed predictive QSAR models using MuDRA, PLS, and RF approaches and performed virtual screening of 117 natural products. As a result of QSAR modeling, we formulated the recommendations regarding physico-chemical characteristics for promising compounds active against Musca domestica and Mythimna separata. Homology models were successfully built for both species and molecular docking of QSAR hits vs known insecticides allowed us to prioritize twenty-two compounds against Musca domestica and six against Mythimna separata. Our results suggest that pimarane diterpenes, abietanes diterpenes, dimeric diterpenes and scopadulane diterpenes obtained from aerial parts of species of the genus Calceolaria (Calceolariaceae: Scrophulariaceae) can be considered as potential insecticidal.
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Affiliation(s)
- Gabriela Cristina Soares Rodrigues
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil
| | - Mayara Dos Santos Maia
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil
| | - Andreza Barbosa Silva Cavalcanti
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil
| | - Renata Priscila Costa Barros
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil
| | - Luciana Scotti
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil
| | - Carlos L Cespedes-Acuña
- Plant biochemistry and phytochemical ecotoxicology lab. Departamento de Ciencias Basicas, Facultad de Ciencias, Universidad del Bio Bio, Chillan, Chile
| | - Eugene N Muratov
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil; 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
| | - Marcus Tullius Scotti
- Laboratory of Cheminformatics. Postgraduate Program in Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraíba, Castelo Branco 58051900, João Pessoa, PB, Brazil.
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13
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Alves VM, Bobrowski T, Melo-Filho CC, Korn D, Auerbach S, Schmitt C, Muratov EN, Tropsha A. QSAR Modeling of SARS-CoV M pro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2. Mol Inform 2020; 40:e2000113. [PMID: 33405340 DOI: 10.1002/minf.202000113] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 07/28/2020] [Indexed: 12/22/2022]
Abstract
The main protease (Mpro) of the SARS-CoV-2 has been proposed as one of the major drug targets for COVID-19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96 % sequence identity, 100 % active site conservation) Mpro of SARS-CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database. Similarity searching and molecular docking were explored in parallel, but docking failed to correctly discriminate between experimentally active and inactive compounds, so it was not relied upon for prospective virtual screening. Forty-two compounds were identified by our models as consensus computational hits. Subsequent to our computational studies, NCATS reported the results of experimental screening of their drug collection in SARS-CoV-2 cytopathic effect assay (https://opendata.ncats.nih.gov/covid19/). Coincidentally, NCATS tested 11 of our 42 hits, and three of them, cenicriviroc (AC50 of 8.9 μM), proglumetacin (tested twice independently, with AC50 of 8.9 μM and 12.5 μM), and sufugolix (AC50 12.6 μM), were shown to be active. These observations support the value of our modeling approaches and models for guiding the experimental investigations of putative anti-COVID-19 drug candidates. All data and models used in this study are publicly available via Supplementary Materials, GitHub (https://github.com/alvesvm/sars-cov-mpro), and Chembench web portal (https://chembench.mml.unc.edu/).
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Affiliation(s)
- Vinicius M Alves
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Cleber C Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Daniel Korn
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Scott Auerbach
- Toxinformatics Group, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Charles Schmitt
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, 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, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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14
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Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. QSAR without borders. Chem Soc Rev 2020; 49:3525-3564. [PMID: 32356548 PMCID: PMC8008490 DOI: 10.1039/d0cs00098a] [Citation(s) in RCA: 327] [Impact Index Per Article: 81.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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Affiliation(s)
- Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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15
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Bobrowski T, Alves VM, Melo-Filho CC, Korn D, Auerbach S, Schmitt C, Muratov EN, Tropsha A. Computational Models Identify Several FDA Approved or Experimental Drugs as Putative Agents Against SARS-CoV-2. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:12153594. [PMID: 32511287 PMCID: PMC7252448 DOI: 10.26434/chemrxiv.12153594] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 04/22/2020] [Indexed: 01/09/2023]
Abstract
The outbreak of a novel human coronavirus (SARS-CoV-2) has evolved into global health emergency, infecting hundreds of thousands of people worldwide. We have identified experimental data on the inhibitory activity of compounds tested against closely related (96% sequence identity, 100% active site conservation) protease of SARS-CoV and employed this data to build QSAR models for this dataset. We employed these models for virtual screening of all drugs from DrugBank, including compounds in clinical trials. Molecular docking and similarity search approaches were explored in parallel with QSAR modeling, but molecular docking failed to correctly discriminate between experimentally active and inactive compounds. As a result of our studies, we recommended 41 approved, experimental, or investigational drugs as potential agents against SARS-CoV-2 acting as putative inhibitors of Mpro. Ten compounds with feasible prices were purchased and are awaiting the experimental validation. .
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Affiliation(s)
- Tesia Bobrowski
- 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
| | - Vinicius M. Alves
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Cleber C. Melo-Filho
- 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
| | - Daniel Korn
- 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 Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Scott Auerbach
- Toxinformatics Group, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Charles Schmitt
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Eugene N. 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
| | - 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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16
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Mondal D, Ghosh K, Baidya ATK, Gantait AM, Gayen S. Identification of structural fingerprints for in vivo toxicity by using Monte Carlo based QSTR modeling of nitroaromatics. Toxicol Mech Methods 2020; 30:257-265. [DOI: 10.1080/15376516.2019.1709238] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Dipayan Mondal
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
| | - Kalyan Ghosh
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
| | - Anurag T. K. Baidya
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
| | | | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
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17
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Keshavarz MH, Akbarzadeh AR. A simple approach for assessment of toxicity of nitroaromatic compounds without using complex descriptors and computer codes. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:347-361. [PMID: 31020866 DOI: 10.1080/1062936x.2019.1595135] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 03/11/2019] [Indexed: 06/09/2023]
Abstract
A simple approach is introduced to assess the toxicity of nitroaromatic compounds in terms of an oral LD50 dose (50% lethal dose) for rats. Most of the presented Quantitative Structure-Activity Relationship (QSAR) models for prediction of in vivo toxicity of nitroaromatics are calculated by quantum computing descriptors which are more difficult to interpret and apply, while the new model requires only the molecular structure of a desirable nitroaromatic compound. The novel model is based on the constitutional descriptors, such as the number of oxygen, sulphur, phosphorous and molecular fragments. Experimental data of 90 nitroaromatics are used to derive and test the new model as the logarithm of LD50 values, i.e. -log (LD50). Although it is based on only simple structural parameters, the reliability of the new model is also higher than the complex QSAR model because the values of the root-mean-square deviation (RMSD) of -log (LD50) for the new and the outputs of the latest QSAR method are 0.342 and 0.377, respectively.
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Affiliation(s)
- M H Keshavarz
- a Department of Chemistry , Malek-ashtar University of Technology , Shahin-shahr , Islamic Republic of Iran
| | - A R Akbarzadeh
- b Department of Chemistry , Iran University of Science and Technology , Tehran , Islamic Republic of Iran
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18
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Klimenko K, Lyakhov S, Shibinskaya M, Karpenko A, Marcou G, Horvath D, Zenkova M, Goncharova E, Amirkhanov R, Krysko A, Andronati S, Levandovskiy I, Polishchuk P, Kuz'min V, Varnek A. Virtual screening, synthesis and biological evaluation of DNA intercalating antiviral agents. Bioorg Med Chem Lett 2017; 27:3915-3919. [PMID: 28666733 DOI: 10.1016/j.bmcl.2017.06.035] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 06/09/2017] [Accepted: 06/11/2017] [Indexed: 01/01/2023]
Abstract
This paper describes computer-aided design of new anti-viral agents against Vaccinia virus (VACV) potentially acting as nucleic acid intercalators. Earlier obtained experimental data for DNA intercalation affinities and activities against Vesicular stomatitis virus (VSV) have been used to build, respectively, pharmacophore and QSAR models. These models were used for virtual screening of a database of 245 molecules generated around typical scaffolds of known DNA intercalators. This resulted in 12 hits which then were synthesized and tested for antiviral activity against VaV together with 43 compounds earlier studied against VSV. Two compounds displaying high antiviral activity against VaV and low cytotoxicity were selected for further antiviral activity investigations.
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Affiliation(s)
- Kyrylo Klimenko
- Laboratoire de Chemoinformatique, (UMR 7140 CNRS/UniStra), Université de Strasbourg, 4, rue B. Pascal, Strasbourg 67000, France; A.V. Bogatsky Physico-Chemical Institute of NAS of Ukraine, Lyustdorfskaya doroga, 86, Odessa 65080, Ukraine
| | - Sergey Lyakhov
- A.V. Bogatsky Physico-Chemical Institute of NAS of Ukraine, Lyustdorfskaya doroga, 86, Odessa 65080, Ukraine
| | - Marina Shibinskaya
- A.V. Bogatsky Physico-Chemical Institute of NAS of Ukraine, Lyustdorfskaya doroga, 86, Odessa 65080, Ukraine
| | - Alexander Karpenko
- A.V. Bogatsky Physico-Chemical Institute of NAS of Ukraine, Lyustdorfskaya doroga, 86, Odessa 65080, Ukraine
| | - Gilles Marcou
- Laboratoire de Chemoinformatique, (UMR 7140 CNRS/UniStra), Université de Strasbourg, 4, rue B. Pascal, Strasbourg 67000, France
| | - Dragos Horvath
- Laboratoire de Chemoinformatique, (UMR 7140 CNRS/UniStra), Université de Strasbourg, 4, rue B. Pascal, Strasbourg 67000, France
| | - Marina Zenkova
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, 8 Lavrentiev Avenue, Novosibirsk 630090, Russia
| | - Elena Goncharova
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, 8 Lavrentiev Avenue, Novosibirsk 630090, Russia
| | - Rinat Amirkhanov
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, 8 Lavrentiev Avenue, Novosibirsk 630090, Russia
| | - Andrei Krysko
- A.V. Bogatsky Physico-Chemical Institute of NAS of Ukraine, Lyustdorfskaya doroga, 86, Odessa 65080, Ukraine
| | - Sergei Andronati
- A.V. Bogatsky Physico-Chemical Institute of NAS of Ukraine, Lyustdorfskaya doroga, 86, Odessa 65080, Ukraine
| | - Igor Levandovskiy
- Department of Organic Chemistry, Kiev Polytechnic Institute, Pr. Pobedy 37, 03056 Kiev, Ukraine
| | - Pavel Polishchuk
- A.V. Bogatsky Physico-Chemical Institute of NAS of Ukraine, Lyustdorfskaya doroga, 86, Odessa 65080, Ukraine; Institute of Molecular and Translational Medicine, Palacky University Olomouc, Hněvotínská 1333/5, Olomouc 779 00, Czech Republic
| | - Victor Kuz'min
- A.V. Bogatsky Physico-Chemical Institute of NAS of Ukraine, Lyustdorfskaya doroga, 86, Odessa 65080, Ukraine
| | - Alexandre Varnek
- Laboratoire de Chemoinformatique, (UMR 7140 CNRS/UniStra), Université de Strasbourg, 4, rue B. Pascal, Strasbourg 67000, France; Federal University of Kazan, Kremlevskaya str., 18, Kazan, Russia.
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19
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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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20
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Piskov VB, Chernyshev VP, Karakotov SD. M-Dinitroaromatic Moiety as a Fragment of Biologically Active Compounds. Pharm Chem J 2016. [DOI: 10.1007/s11094-016-1361-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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21
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Computational assessment of environmental hazards of nitroaromatic compounds: influence of the type and position of aromatic ring substituents on toxicity. Struct Chem 2015. [DOI: 10.1007/s11224-015-0715-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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22
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A new approach for accurate prediction of toxicity of amino compounds. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2014. [DOI: 10.1007/s13738-014-0506-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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23
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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: 1061] [Impact Index Per Article: 106.1] [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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Mokshina EG, Kuz’min VE, Nedostup VI. QSPR Modeling of critical parameters of organic compounds belonging to different classes in terms of the simplex representation of molecular structure. RUSSIAN JOURNAL OF ORGANIC CHEMISTRY 2014. [DOI: 10.1134/s1070428014030026] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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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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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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Keshavarz MH, Gharagheizi F, Shokrolahi A, Zakinejad S. Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes. JOURNAL OF HAZARDOUS MATERIALS 2012; 237-238:79-101. [PMID: 22959133 DOI: 10.1016/j.jhazmat.2012.07.048] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2011] [Revised: 03/30/2012] [Accepted: 07/25/2012] [Indexed: 06/01/2023]
Abstract
Most of benzoic acid derivatives are toxic, which may cause serious public health and environmental problems. Two novel simple and reliable models are introduced for desk calculations of the toxicity of benzoic acid compounds in mice via oral LD(50) with more reliance on their answers as one could attach to the more complex outputs. They require only elemental composition and molecular fragments without using any computer codes. The first model is based on only the number of carbon and hydrogen atoms, which can be improved by several molecular fragments in the second model. For 57 benzoic compounds, where the computed results of quantitative structure-toxicity relationship (QSTR) were recently reported, the predicted results of two simple models of present method are more reliable than QSTR computations. The present simple method is also tested with further 324 benzoic acid compounds including complex molecular structures, which confirm good forecasting ability of the second model.
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Affiliation(s)
- Mohammad Hossein Keshavarz
- Department of Chemistry, Malek-ashtar University of Technology, Shahin-shahr P.O. Box 83145/115, Isfahan, Islamic Republic of Iran.
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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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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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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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Abstract
This chapter reviews the application of fragment descriptors at different stages of virtual screening: filtering, similarity search, and direct activity assessment using QSAR/QSPR models. Several case studies are considered. It is demonstrated that the power of fragment descriptors stems from their universality, very high computational efficiency, simplicity of interpretation, and versatility.
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Affiliation(s)
- Alexandre Varnek
- Laboratory of Chemoinformatics, UMR7177 CNRS, University of Strasbourg, Strasbourg, France
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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: 482] [Impact Index Per Article: 34.4] [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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Roy K, Ghosh G. QSTR with extended topochemical atom (ETA) indices. 12. QSAR for the toxicity of diverse aromatic compounds to Tetrahymena pyriformis using chemometric tools. CHEMOSPHERE 2009; 77:999-1009. [PMID: 19709717 DOI: 10.1016/j.chemosphere.2009.07.072] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2009] [Accepted: 07/30/2009] [Indexed: 05/28/2023]
Abstract
We have developed QSTR models for the toxicity of 384 diverse aromatic compounds to Tetrahymena pyriformis with recently introduced extended topochemical atom (ETA) indices and compared the ETA models with those derived from various non-ETA topological descriptors and also combined set of descriptors encompassing the ETA and non-ETA parameters. The data set was split into test (25% compounds of total data points) and training (remaining 75%) sets based on K-mean clustering technique. Different statistical analyses (factor analysis followed by multiple linear regression (FA-MLR), stepwise regression and partial least squares (PLS)) were performed with the training set compounds to develop QSTR models using the topological descriptors. All the developed models were cross-validated using leave-one-out (LOO) technique. The best models were selected on the basis of predicted R(2) values for test set compounds. The best models (based on external validation) developed from different techniques came from the combined set of descriptors. The above results indicate that the use of ETA descriptors with non-ETA descriptors improved the statistical quality of the non-ETA models. From the best models involving ETA parameters, it is observed that functionality of halogen atoms (hydrophobicity), volume parameter (bulk) and nitrogen containing functionalities (polarity) are important for developing QSTR models for the current data set. This study suggests that ETA parameters are sufficient power to encode chemical information contributing significantly to the toxicity of diverse aromatic compounds to T. pyriformis.
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Affiliation(s)
- Kunal Roy
- Drug Theoretics and Cheminformatics Lab, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India.
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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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Toropov AA, Toropova AP, Benfenati E. QSAR-modeling of toxicity of organometallic compounds by means of the balance of correlations for InChI-based optimal descriptors. Mol Divers 2009; 14:183-92. [DOI: 10.1007/s11030-009-9156-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2009] [Accepted: 04/27/2009] [Indexed: 10/20/2022]
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