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Danieli A, Colombo E, Raitano G, Lombardo A, Roncaglioni A, Manganaro A, Sommovigo A, Carnesecchi E, Dorne JLCM, Benfenati E. The VEGA Tool to Check the Applicability Domain Gives Greater Confidence in the Prediction of In Silico Models. Int J Mol Sci 2023; 24:9894. [PMID: 37373049 PMCID: PMC10298077 DOI: 10.3390/ijms24129894] [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: 04/28/2023] [Revised: 05/31/2023] [Accepted: 06/03/2023] [Indexed: 06/29/2023] Open
Abstract
A sound assessment of in silico models and their applicability domain can support the use of new approach methodologies (NAMs) in chemical risk assessment and requires increasing the users' confidence in this approach. Several approaches have been proposed to evaluate the applicability domain of such models, but their prediction power still needs a thorough assessment. In this context, the VEGA tool capable of assessing the applicability domain of in silico models is examined for a range of toxicological endpoints. The VEGA tool evaluates chemical structures and other features related to the predicted endpoints and is efficient in measuring applicability domain, enabling the user to identify less accurate predictions. This is demonstrated with many models addressing different endpoints, towards toxicity of relevance to human health, ecotoxicological endpoints, environmental fate, physicochemical and toxicokinetic properties, for both regression models and classifiers.
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Affiliation(s)
- Alberto Danieli
- Department of Biotechnology and Life Science, University of Insubria, Via Dunant 3, 21100 Varese, Italy;
| | - Erika Colombo
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Giuseppa Raitano
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Anna Lombardo
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Alessandra Roncaglioni
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | | | | | - Edoardo Carnesecchi
- European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy; (E.C.); (J.-L.C.M.D.)
| | - Jean-Lou C. M. Dorne
- European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy; (E.C.); (J.-L.C.M.D.)
| | - Emilio Benfenati
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
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Zhang Z, Xu Z, Wang R, Li F, Gong H, Jiang H. The solid/liquid phase transfer effect of 18-crown-6 for the potassium salts catalyzed β-hydroxyethylation of mercaptan with ethylene carbonate. MONATSHEFTE FUR CHEMIE 2023. [DOI: 10.1007/s00706-023-03056-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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Kumar P, Kumar A. Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling. Nanotoxicology 2021; 15:1199-1214. [PMID: 34961428 DOI: 10.1080/17435390.2021.2008039] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Liver toxicity of quantum dots varies with size, concentration, and other structural as well as experimental parameters. For modeling hepatotoxicity, the eclectic data associated with cadmium containing quantum dots have been used in the creation of quasiSMILES for their representation. The core diameter is normalized for wider applicability and the index of the ideality of correlation is applied to construct the better quantitative features toxicity relationship models. Total eight splits are created and the best model is obtained through split 1 with better prediction criteria of validation set objects. The values of all statistical criteria used in the quality determination of a QSAR model are within the specified range for all the eight toxicity models developed here. Factors like TGA ligand and 0.6-0.7 nm diameter are favorable for liver toxicity while L-cysteine ligand and 0.5-0.6 nm core diameter are helpful in the reduction of toxicity. Further, the intelligent consensus modeling process forms a total of 40 individual and 20 consensus models and the best individual and consensus models are 'Good' in MAE-based criteria. The consensus modeling enhances the prediction ability as well as the accuracy of the developed models and increases the applicability space of the built models for hepatotoxicity prediction of quantum dots.
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Affiliation(s)
- Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
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Zivkovic M, Zlatanovic M, Zlatanovic N, Golubović M, Veselinović AM. The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development. Mini Rev Med Chem 2020; 20:1389-1402. [DOI: 10.2174/1389557520666200212111428] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 01/18/2023]
Abstract
In recent years, one of the promising approaches in the QSAR modeling Monte Carlo optimization
approach as conformation independent method, has emerged. Monte Carlo optimization has
proven to be a valuable tool in chemoinformatics, and this review presents its application in drug discovery
and design. In this review, the basic principles and important features of these methods are discussed
as well as the advantages of conformation independent optimal descriptors developed from the
molecular graph and the Simplified Molecular Input Line Entry System (SMILES) notation compared
to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results
from Monte Carlo optimization-based QSAR modeling with the further addition of molecular
docking studies applied for various pharmacologically important endpoints. SMILES notation based
optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/
decrease of biological activity, which are used further to design compounds with targeted activity
based on computer calculation, are presented. In this mini-review, research papers in which molecular
docking was applied as an additional method to design molecules to validate their activity further,
are summarized. These papers present a very good correlation among results obtained from Monte
Carlo optimization modeling and molecular docking studies.
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Affiliation(s)
| | | | | | - Mladjan Golubović
- Clinic for Anesthesiology and Intensive Care, Clinical Center Nis, Nis, Serbia
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Toropov AA, Toropova AP, Roncaglioni A, Benfenati E. Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results. Methods Mol Biol 2018; 1800:573-583. [PMID: 29934912 DOI: 10.1007/978-1-4939-7899-1_27] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Quantitative structure-activity relationships (QSARs) for prediction of toxicological endpoints built up with the CORAL software are discussed. Prejudices related to these QSAR models are listed. Possible ways to improve the software are discussed.
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Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
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Raies AB, Bajic VB. In silico toxicology: computational methods for the prediction of chemical toxicity. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2016; 6:147-172. [PMID: 27066112 PMCID: PMC4785608 DOI: 10.1002/wcms.1240] [Citation(s) in RCA: 315] [Impact Index Per Article: 39.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 10/27/2015] [Accepted: 11/10/2015] [Indexed: 01/08/2023]
Abstract
Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147-172. doi: 10.1002/wcms.1240 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Arwa B Raies
- King Abdullah University of Science and Technology (KAUST) Computational Bioscience Research Centre (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE) Thuwal Saudi Arabia
| | - Vladimir B Bajic
- King Abdullah University of Science and Technology (KAUST) Computational Bioscience Research Centre (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE) Thuwal Saudi Arabia
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Predicting the cytotoxicity of ionic liquids using QSAR model based on SMILES optimal descriptors. J Mol Liq 2015. [DOI: 10.1016/j.molliq.2015.04.049] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Yilmaz H, Sizochenko N, Rasulev B, Toropov A, Guzel Y, Kuz'min V, Leszczynska D, Leszczynski J. Amino substituted nitrogen heterocycle ureas as kinase insert domain containing receptor (KDR) inhibitors: Performance of structure–activity relationship approaches. J Food Drug Anal 2015; 23:168-175. [PMID: 28911371 PMCID: PMC9351780 DOI: 10.1016/j.jfda.2015.03.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
A quantitative structure–activity relationship (QSAR) study was performed on a set of amino-substituted nitrogen heterocyclic urea derivatives. Two novel approaches were applied: (1) the simplified molecular input-line entry systems (SMILES) based optimal descriptors approach; and (2) the fragment-based simplex representation of molecular structure (SiRMS) approach. Comparison with the classic scheme of building up the model and balance of correlation (BC) for optimal descriptors approach shows that the BC scheme provides more robust predictions than the classic scheme for the considered pIC50 of the heterocyclic urea derivatives. Comparison of the SMILES-based optimal descriptors and SiRMS approaches has confirmed good performance of both techniques in prediction of kinase insert domain containing receptor (KDR) inhibitory activity, expressed as a logarithm of inhibitory concentration (pIC50) of studied compounds.
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Affiliation(s)
- Hayriye Yilmaz
- Kayseri Vocational School, Biomedical Devices and Technologies, Erciyes University, 38039, Kayseri, Turkey; Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, 39217, USA
| | - Natalia Sizochenko
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, 39217, USA; Odessa I.I. Mechnikov National University, Department of Chemistry, Dvoryanskaya Street, 2, 65082, Odessa, Ukraine
| | - Bakhtiyor Rasulev
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, 39217, USA
| | - Andrey Toropov
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milano, Italy
| | - Yahya Guzel
- Department of Chemistry, Faculty of Science, Erciyes University, 38039, Kayseri, Turkey
| | - Viktor Kuz'min
- Odessa I.I. Mechnikov National University, Department of Chemistry, Dvoryanskaya Street, 2, 65082, Odessa, Ukraine
| | - Danuta Leszczynska
- Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS, 39217, USA
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, 39217, USA.
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Veselinović JB, Nikolić GM, Trutić NV, Živković JV, Veselinović AM. Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:449-460. [PMID: 26043064 DOI: 10.1080/1062936x.2015.1049665] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
A series of 278 organophosphate compounds acting as acetylcholinesterase inhibitors has been studied. The Monte Carlo method was used as a tool for building up one-variable quantitative structure-activity relationship (QSAR) models for acetylcholinesterase inhibition activity based on the principle that the target endpoint is treated as a random event. As an activity, bimolecular rate constants were used. The QSAR models were based on optimal descriptors obtained from Simplified Molecular Input-Line Entry System (SMILES) used for the representation of molecular structure. Two modelling approaches were examined: (1) 'classic' training-test system where the QSAR model was built with one random split into a training, test and validation set; and (2) the correlation balance based QSAR models were built with two random splits into a sub-training, calibration, test and validation set. The DModX method was used for defining the applicability domain. The obtained results suggest that studied activity can be determined with the application of QSAR models calculated with the Monte Carlo method since the statistical quality of all build models was very good. Finally, structural indicators for the increase and the decrease of the bimolecular rate constant are defined. The possibility of using these results for the computer-aided design of new organophosphate compounds is presented.
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Fatemi MH, Malekzadeh H. CORAL: predictions of retention indices of volatiles in cooking rice using representation of the molecular structure obtained by combination of SMILES and graph approaches. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2014. [DOI: 10.1007/s13738-014-0497-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Toropov AA, Veselinović JB, Veselinović AM, Miljković FN, Toropova AP. QSAR models for 1,2,4-benzotriazines as Src inhibitors based on Monte Carlo method. Med Chem Res 2014. [DOI: 10.1007/s00044-014-1132-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Deng F, Ma S, Xie M, Zhang X, Li P, Zhai H. Study on the agonists for the human Toll-like receptor-8 by molecular modeling. MOLECULAR BIOSYSTEMS 2014; 10:2202-14. [DOI: 10.1039/c4mb00183d] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Yan J, Zhu WW, Kong B, Lu HB, Yun YH, Huang JH, Liang YZ. A Combinational Strategy of Model Disturbance and Outlier Comparison to Define Applicability Domain in Quantitative Structural Activity Relationship. Mol Inform 2014; 33:503-13. [PMID: 27486037 DOI: 10.1002/minf.201300161] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Accepted: 04/16/2014] [Indexed: 01/21/2023]
Abstract
In order to define an applicability domain for quantitative structure-activity relationship modeling, a combinational strategy of model disturbance and outlier comparison is developed. An indicator named model disturbance index was defined to estimate the prediction error. Moreover, the information of the outliers in the training set was used to filter the unreliable samples in the test set based on "structural similarity". Chromatography retention indices data were used to investigate this approach. The relationship between model disturbance index and prediction error can be found. Also, the comparison between the outlier set and the test set could provide additional information about which unknown samples should be paid more attentions. A novel technique based on model population analysis was used to evaluate the validity of applicability domain. Finally, three commonly used methods, i.e. Leverage, descriptor range-based and model perturbation method, were compared with the proposed approach.
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Affiliation(s)
- Jun Yan
- Research Center of Modernization of Traditional Chinese Medicine, Central South University, Changsha 410083, P. R. China tel: +86 731 8830831; fax: +86 731 8830831
| | - Wei-Wei Zhu
- Department of Chemical and Bioscience, HeChi University, YiZhou 546300, P. R. China
| | - Bo Kong
- Technology Center of China Tobacco Hunan Industrial Co., LTD, Changsha 410014, P. R. China
| | - Hong-Bing Lu
- Technology Center of China Tobacco Hunan Industrial Co., LTD, Changsha 410014, P. R. China
| | - Yong-Huan Yun
- Research Center of Modernization of Traditional Chinese Medicine, Central South University, Changsha 410083, P. R. China tel: +86 731 8830831; fax: +86 731 8830831
| | - Jian-Hua Huang
- Research Center of Modernization of Traditional Chinese Medicine, Central South University, Changsha 410083, P. R. China tel: +86 731 8830831; fax: +86 731 8830831
| | - Yi-Zeng Liang
- Research Center of Modernization of Traditional Chinese Medicine, Central South University, Changsha 410083, P. R. China tel: +86 731 8830831; fax: +86 731 8830831.
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Toropova AP, Toropov AA, Veselinović JB, Miljković FN, Veselinović AM. QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method. Eur J Med Chem 2014; 77:298-305. [DOI: 10.1016/j.ejmech.2014.03.013] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2013] [Revised: 01/31/2014] [Accepted: 03/05/2014] [Indexed: 01/30/2023]
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Veselinović AM, Milosavljević JB, Toropov AA, Nikolić GM. SMILES-based QSAR model for arylpiperazines as high-affinity 5-HT1A receptor ligands using CORAL. Eur J Pharm Sci 2013; 48:532-41. [DOI: 10.1016/j.ejps.2012.12.021] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2012] [Revised: 12/06/2012] [Accepted: 12/22/2012] [Indexed: 10/27/2022]
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Veselinović AM, Milosavljević JB, Toropov AA, Nikolić GM. SMILES-Based QSAR Models for the Calcium Channel-Antagonistic Effect of 1,4-Dihydropyridines. Arch Pharm (Weinheim) 2012; 346:134-9. [DOI: 10.1002/ardp.201200373] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2012] [Revised: 11/01/2012] [Accepted: 11/02/2012] [Indexed: 01/07/2023]
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Toropova AP, Toropov AA, Lombardo A, Roncaglioni A, Benfenati E, Gini G. Coral: QSAR models for acute toxicity in fathead minnow (Pimephales promelas). J Comput Chem 2012; 33:1218-23. [DOI: 10.1002/jcc.22953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2011] [Revised: 10/17/2011] [Accepted: 01/13/2012] [Indexed: 11/09/2022]
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Toropova AP, Toropov AA, Benfenati E, Gini G. QSAR Models for Toxicity of Organic Substances to Daphnia magna Built up by Using the CORAL Freeware. Chem Biol Drug Des 2012; 79:332-8. [DOI: 10.1111/j.1747-0285.2011.01279.x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Garro Martinez JC, Duchowicz PR, Estrada MR, Zamarbide GN, Castro EA. QSAR study and molecular design of open-chain enaminones as anticonvulsant agents. Int J Mol Sci 2011; 12:9354-68. [PMID: 22272137 PMCID: PMC3257134 DOI: 10.3390/ijms12129354] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2011] [Revised: 11/07/2011] [Accepted: 11/24/2011] [Indexed: 11/24/2022] Open
Abstract
Present work employs the QSAR formalism to predict the ED50 anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED50 values lower than 10 mg·kg−1 for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project.
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Affiliation(s)
- Juan C. Garro Martinez
- Department of Chemistry, National University of San Luis, Chacabuco 917, San Luis 5700, Argentine; E-Mails: (M.R.E.); (G.N.Z.)
- Author to whom correspondence should be addressed; E-Mail: ; Tel./Fax: +54-2652-423789 ext. 122
| | - Pablo R. Duchowicz
- INIFTA, (CCT-La Plata-CONICET), Diag. 113 y 64, C.C. 16, Suc.4, La Plata 1900, Argentine; E-Mails: (P.R.D.); (E.A.C.)
| | - Mario R. Estrada
- Department of Chemistry, National University of San Luis, Chacabuco 917, San Luis 5700, Argentine; E-Mails: (M.R.E.); (G.N.Z.)
| | - Graciela N. Zamarbide
- Department of Chemistry, National University of San Luis, Chacabuco 917, San Luis 5700, Argentine; E-Mails: (M.R.E.); (G.N.Z.)
| | - Eduardo A. Castro
- INIFTA, (CCT-La Plata-CONICET), Diag. 113 y 64, C.C. 16, Suc.4, La Plata 1900, Argentine; E-Mails: (P.R.D.); (E.A.C.)
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García J, Duchowicz PR, Rozas MF, Caram JA, Mirífico MV, Fernández FM, Castro EA. A comparative QSAR on 1,2,5-thiadiazolidin-3-one 1,1-dioxide compounds as selective inhibitors of human serine proteinases. J Mol Graph Model 2011; 31:10-9. [PMID: 21908217 DOI: 10.1016/j.jmgm.2011.07.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2011] [Revised: 07/28/2011] [Accepted: 07/31/2011] [Indexed: 11/30/2022]
Abstract
Selective inhibitors of target serine proteinases have a potential therapeutic role for the treatment of various inflammatory and related diseases. We develop a comparative quantitative structure-activity relationships based analysis on compounds embodying the 1,2,5-thiadiazolidin-3-one 1,1-dioxide scaffold. By means of classical Molecular Dynamics we obtain the conformation of each lowest-energy molecular structure from which we derive more than a thousand of structural descriptors necessary for building predictive QSAR models. We resort to two different modeling approaches with the purpose of testing the consistency of our results: (a) multivariable linear regressions based on the replacement method and forward stepwise regression, and (b) the calculation of flexible descriptors with the CORAL program. All the models are properly validated by means of standard procedures. The resulting QSAR models are supposed to be of great utility for the rational search and design (including synthesis and/or in vitro biochemical studies) of new effective non-peptidyl inhibitors of serine proteinases.
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Affiliation(s)
- Javier García
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA, CCT La Plata-CONICET), Casilla de Correo 16, La Plata, Argentina
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Toropova AP, Toropov AA, Benfenati E, Gini G, Leszczynska D, Leszczynski J. CORAL: QSPR models for solubility of [C60] and [C70] fullerene derivatives. Mol Divers 2010; 15:249-56. [PMID: 20349134 DOI: 10.1007/s11030-010-9245-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2010] [Accepted: 03/02/2010] [Indexed: 01/29/2023]
Abstract
Quantitative structure-property relationships (QSPRs) between the molecular structure of [C(60)] and [C(70)] fullerene derivatives and their solubility in chlorobenzene (mg/mL) have been established by means of CORAL (CORrelations And Logic) freeware. The CORAL models are based on representation of the molecular structure by simplified molecular input line entry system (SMILES). Three random splits into the training and the external validation sets have been examined. The ranges of statistical characteristics of these models are as follows: n = 18, r (2) = 0.748-0.815, s = 15.1 -17.5 (mg/mL), F = 47-71 (training set); n = 9, r (2) = 0.806-0.936, s = 12.5-17.5 (mg/mL), F = 29-103 (validation set).
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Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milano, Italy
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Fechner N, Jahn A, Hinselmann G, Zell A. Estimation of the applicability domain of kernel-based machine learning models for virtual screening. J Cheminform 2010; 2:2. [PMID: 20222949 PMCID: PMC2851576 DOI: 10.1186/1758-2946-2-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2009] [Accepted: 03/11/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The virtual screening of large compound databases is an important application of structural-activity relationship models. Due to the high structural diversity of these data sets, it is impossible for machine learning based QSAR models, which rely on a specific training set, to give reliable results for all compounds. Thus, it is important to consider the subset of the chemical space in which the model is applicable. The approaches to this problem that have been published so far mostly use vectorial descriptor representations to define this domain of applicability of the model. Unfortunately, these cannot be extended easily to structured kernel-based machine learning models. For this reason, we propose three approaches to estimate the domain of applicability of a kernel-based QSAR model. RESULTS We evaluated three kernel-based applicability domain estimations using three different structured kernels on three virtual screening tasks. Each experiment consisted of the training of a kernel-based QSAR model using support vector regression and the ranking of a disjoint screening data set according to the predicted activity. For each prediction, the applicability of the model for the respective compound is quantitatively described using a score obtained by an applicability domain formulation. The suitability of the applicability domain estimation is evaluated by comparing the model performance on the subsets of the screening data sets obtained by different thresholds for the applicability scores. This comparison indicates that it is possible to separate the part of the chemspace, in which the model gives reliable predictions, from the part consisting of structures too dissimilar to the training set to apply the model successfully. A closer inspection reveals that the virtual screening performance of the model is considerably improved if half of the molecules, those with the lowest applicability scores, are omitted from the screening. CONCLUSION The proposed applicability domain formulations for kernel-based QSAR models can successfully identify compounds for which no reliable predictions can be expected from the model. The resulting reduction of the search space and the elimination of some of the active compounds should not be considered as a drawback, because the results indicate that, in most cases, these omitted ligands would not be found by the model anyway.
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Affiliation(s)
- Nikolas Fechner
- Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Sand 1, 72076 Tübingen, Germany
| | - Andreas Jahn
- Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Sand 1, 72076 Tübingen, Germany
| | - Georg Hinselmann
- Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Sand 1, 72076 Tübingen, Germany
| | - Andreas Zell
- Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Sand 1, 72076 Tübingen, Germany
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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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Additive SMILES-based carcinogenicity models: Probabilistic principles in the search for robust predictions. Int J Mol Sci 2009; 10:3106-3127. [PMID: 19742127 PMCID: PMC2738914 DOI: 10.3390/ijms10073106] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2009] [Revised: 06/23/2009] [Accepted: 07/02/2009] [Indexed: 11/16/2022] Open
Abstract
Optimal descriptors calculated with the simplified molecular input line entry system (SMILES) have been utilized in modeling of carcinogenicity as continuous values (logTD50). These descriptors can be calculated using correlation weights of SMILES attributes calculated by the Monte Carlo method. A considerable subset of these attributes includes rare attributes. The use of these rare attributes can lead to overtraining. One can avoid the influence of the rare attributes if their correlation weights are fixed to zero. A function, limS, has been defined to identify rare attributes. The limS defines the minimum number of occurrences in the set of structures of the training (subtraining) set, to accept attributes as usable. If an attribute is present less than limS, it is considered “rare”, and thus not used. Two systems of building up models were examined: 1. classic training-test system; 2. balance of correlations for the subtraining and calibration sets (together, they are the original training set: the function of the calibration set is imitation of a preliminary test set). Three random splits into subtraining, calibration, and test sets were analysed. Comparison of abovementioned systems has shown that balance of correlations gives more robust prediction of the carcinogenicity for all three splits (split 1: rtest2=0.7514, stest=0.684; split 2: rtest2=0.7998, stest=0.600; split 3: rtest2=0.7192, stest=0.728).
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Abstract
In this final chapter a new perspective for the application of QSAR in the nanosciences is discussed. The role of nanomaterials is rapidly increasing in many aspects of everyday life. This is promoting a wide range of research needs related to both the design of new materials with required properties and performing a comprehensive risk assessment of the manufactured nanoparticles. The development of nanoscience also opens new areas for QSAR modelers. We have begun this contribution with a detailed discussion on the remarkable physical–chemical properties of nanomaterials and their specific toxicities. Both these factors should be considered as potential endpoints for further nano-QSAR studies. Then, we have highlighted the status and research needs in the area of molecular descriptors applicable to nanomaterials. Finally, we have put together currently available nano-QSAR models related to the physico-chemical endpoints of nanoparticles and their activity. Although we have observed many problems (i.e., a lack of experimental data, insufficient and inadequate descriptors), we do believe that application of QSAR methodology will significantly support nanoscience in the near future. Development of reliable nano-QSARs can be considered as the next challenging task for the QSAR community.
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Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Igglessi-Markopoulou O, Kollias G. A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs. Mol Divers 2009; 14:225-35. [DOI: 10.1007/s11030-009-9163-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2009] [Accepted: 05/09/2009] [Indexed: 11/28/2022]
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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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Toropov A, Toropova A, Benfenati E. QSPR modeling for enthalpies of formation of organometallic compounds by means of SMILES-based optimal descriptors. Chem Phys Lett 2008. [DOI: 10.1016/j.cplett.2008.07.027] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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