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Wang Y, Liu JX, Wang J, Shang J, Gao YL. A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug-Disease Associations. J Comput Biol 2023; 30:937-947. [PMID: 37486669 DOI: 10.1089/cmb.2023.0078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2023] Open
Abstract
Determining the association between drug and disease is important in drug development. However, existing approaches for drug-disease associations (DDAs) prediction are too homogeneous in terms of feature extraction. Here, a novel graph representation approach based on light gradient boosting machine (GRLGB) is proposed for prediction of DDAs. After the introduction of the protein into a heterogeneous network, nodes features were extracted from two perspectives: network topology and biological knowledge. Finally, the GRLGB classifier was applied to predict potential DDAs. GRLGB achieved satisfactory results on Bdataset and Fdataset through 10-fold cross-validation. To further prove the reliability of the GRLGB, case studies involving anxiety disorders and clozapine were conducted. The results suggest that GRLGB can identify novel DDAs.
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Affiliation(s)
- Ying Wang
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Juan Wang
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Ying-Lian Gao
- Qufu Normal University Library, Qufu Normal University, Rizhao, Shandong, China
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2
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CORAL: Quantitative Structure Retention Relationship (QSRR) of flavors and fragrances compounds studied on the stationary phase methyl silicone OV-101 column in gas chromatography using correlation intensity index and consensus modelling. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.133437] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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3
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Nesměrák K. Medicinal Chemistry Meets Electrochemistry: Redox Potential in the Role of Endpoint or Molecular Descriptor in QSAR/QSPR. Mini Rev Med Chem 2021; 20:1341-1356. [PMID: 32013846 DOI: 10.2174/1389557520666200204121806] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 11/22/2022]
Abstract
Many biochemical reactions are based on redox reactions. Therefore, the redox potential of a chemical compound may be related to its therapeutic or physiological effects. The study of redox properties of compounds is a domain of electrochemistry. The subject of this review is the relationship between electrochemistry and medicinal chemistry, with a focus on quantifying these relationships. A summary of the relevant achievements in the correlation between redox potential and structure, therapeutic activity, resp., is presented. The first part of the review examines the applicability of QSPR for the prediction of redox properties of medically important compounds. The second part brings the exhaustive review of publications using redox potential as a molecular descriptor in QSAR of biological activity. Despite the complexity of medicinal chemistry and biological reactions, it is possible to employ redox potential in QSAR/QSPR. In many cases, this electrochemical parameter plays an essential but rarely absolute role.
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Affiliation(s)
- Karel Nesměrák
- Department of Analytical Chemistry, Faculty of Science, Charles University, Prague, Czech Republic
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4
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Toropova AP. Medicinal Chemistry and Computational Chemistry: Mutual Influence and Harmonization. Mini Rev Med Chem 2020; 20:1320-1321. [PMID: 32600227 DOI: 10.2174/138955752014200626163614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology Istituto di Ricerche Farmacologiche Mario Negri IRCCS Via Mario Negri 2, 20156 Milano, Italy
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5
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Toropov AA, Toropova AP. The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR. Curr Comput Aided Drug Des 2020; 16:197-206. [DOI: 10.2174/1573409915666190328123112] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 02/15/2019] [Accepted: 03/19/2019] [Indexed: 11/22/2022]
Abstract
Background:
The Monte Carlo method has a wide application in various scientific researches.
For the development of predictive models in a form of the quantitative structure-property / activity relationships
(QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the
Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints.
Methods:
Molecular descriptors are a mathematical function of so-called correlation weights of various
molecular features. The numerical values of the correlation weights give the maximal value of a target
function. The target function leads to a correlation between endpoint and optimal descriptor for the visible
training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that
are not involved in the process of building up the model.
Results:
The approach gave quite good models for a large number of various physicochemical, biochemical,
ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL
models are collected in the present review. In addition, the extended version of the approach for more
complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions
besides the molecular structure is demonstrated.
Conclusion:
The Monte Carlo technique available via the CORAL software can be a useful and convenient
tool for the QSPR/QSAR analysis.
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Affiliation(s)
- Andrey A. Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milan, Italy
| | - Alla P. Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milan, Italy
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Chauhan S, Kumar P, Kumar A. Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo method. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:145-159. [PMID: 30777782 DOI: 10.1080/1062936x.2019.1568299] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Indexed: 06/09/2023]
Abstract
Fructose-1,6-bisphosphatase (FBPase) is an enzyme important for regulation of gluconeogenesis, which is a major process in the liver responsible for glucose production. Inhibition of FBPase enzyme causing blockage of the gluconeogenesis process represents a newer scheme in the progress of anti-diabetic drugs. The current research describes the development of hybrid optimal descriptors-based quantitative structure-activity relationship (QSAR) models intended for a set of 62 FBPase inhibitors with the Monte Carlo method. The molecular structures were expressed by the simplified molecular input line entry system (SMILES) notation. Three splits were prepared by random division of the molecules into training set, calibration set and validation set. Statistical parameters obtained from QSAR modelling were good for various designed splits. The best QSAR model showed the following parameters: the values of r2 for calibration set and validation set of the best model were 0.6837 and 0.8623 and of Q2 were 0.6114 and 0.8036, respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoter of the endpoint. Further, these structural attributes were used in designing of new FBPase inhibitors and a molecular docking study was completed for the determination of interactions of the designed molecules with the enzyme.
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Affiliation(s)
- S Chauhan
- a Department of Pharmaceutical Sciences , Guru Jambheshwar University of Science & Technology , Hisar , India
| | - P Kumar
- b Department of Chemistry , Kurukshetra University , Kurukshetra , India
| | - A Kumar
- a Department of Pharmaceutical Sciences , Guru Jambheshwar University of Science & Technology , Hisar , India
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Gaikwad R, Ghorai S, Amin SA, Adhikari N, Patel T, Das K, Jha T, Gayen S. Monte Carlo based modelling approach for designing and predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cell line MCF7. Toxicol In Vitro 2018; 52:23-32. [DOI: 10.1016/j.tiv.2018.05.016] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 04/23/2018] [Accepted: 05/31/2018] [Indexed: 12/20/2022]
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Amata E, Marrazzo A, Dichiara M, Modica MN, Salerno L, Prezzavento O, Nastasi G, Rescifina A, Romeo G, Pittalà V. Heme Oxygenase Database (HemeOxDB) and QSAR Analysis of Isoform 1 Inhibitors. ChemMedChem 2017; 12:1873-1881. [PMID: 28708269 DOI: 10.1002/cmdc.201700321] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Revised: 07/04/2017] [Indexed: 11/12/2022]
Abstract
Due to increasing interest in the field of heme oxygenases (HOs), we built a ligand database called HemeOxDB that includes the entire set of known HO-1 and HO-2 inhibitors, resulting in more than 400 compounds. The HemeOxDB is available online at http://www.researchdsf.unict.it/hemeoxdb/, and having a robust search engine allows end users to build complex queries, sort tabulated results, and generate color-coded two- and three-dimensional graphs. This database will grow to be a tool for the design of potent and selective HO-1 or HO-2 inhibitors. We were also interested in virtually searching for alternative inhibitors, and, for the first time in the field of HOs, a quantitative structure-activity relationship (QSAR) model was built using half-maximal inhibitory concentration (IC50 ) values of the whole set of known HO-1 inhibitors, taken from the HemeOxDB and employing the Monte Carlo technique. The statistical quality suggested that the model is robust and possesses desirable predictive potential. The screening of US Food and Drug Administration (FDA)-approved drugs, external to our dataset, suggested new predicted inhibitors, opening the way for replacing imidazole groups. The HemeOxDB and the QSAR model reported herein may help in prospectively identifying or repurposing new drugs with optimal structural attributes for HO enzyme inhibition.
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Affiliation(s)
- Emanuele Amata
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125, Catania, Italy
| | - Agostino Marrazzo
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125, Catania, Italy
| | - Maria Dichiara
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125, Catania, Italy
| | - Maria N Modica
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125, Catania, Italy
| | - Loredana Salerno
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125, Catania, Italy
| | - Orazio Prezzavento
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125, Catania, Italy
| | - Giovanni Nastasi
- Department of Mathematics and Computer Sciences, University of Catania, Viale A. Doria 6, 95125, Catania, Italy
| | - Antonio Rescifina
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125, Catania, Italy
| | - Giuseppe Romeo
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125, Catania, Italy
| | - Valeria Pittalà
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125, Catania, Italy
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Rescifina A, Floresta G, Marrazzo A, Parenti C, Prezzavento O, Nastasi G, Dichiara M, Amata E. Development of a Sigma-2 Receptor affinity filter through a Monte Carlo based QSAR analysis. Eur J Pharm Sci 2017; 106:94-101. [DOI: 10.1016/j.ejps.2017.05.061] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 05/26/2017] [Accepted: 05/27/2017] [Indexed: 10/19/2022]
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Toropov AA, Toropova AP, Benfenati E, Nicolotti O, Carotti A, Nesmerak K, Veselinović AM, Veselinović JB, Duchowicz PR, Bacelo D, Castro EA, Rasulev BF, Leszczynska D, Leszczynski J. QSPR/QSAR Analyses by Means of the CORAL Software. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Pablo R. Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Argentina
| | | | - Eduardo A. Castro
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Argentina
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Veselinović JB, Veselinović AM, Toropova AP, Toropov AA. The Monte Carlo technique as a tool to predict LOAEL. Eur J Med Chem 2016; 116:71-75. [DOI: 10.1016/j.ejmech.2016.03.075] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Revised: 03/24/2016] [Accepted: 03/25/2016] [Indexed: 12/17/2022]
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12
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Nesměrák K, Toropov AA, Toropova AP. Model for electrochemical parameters for 4-(benzylsulfanyl)pyridines calculated from the molecular structure. J Electroanal Chem (Lausanne) 2016. [DOI: 10.1016/j.jelechem.2016.01.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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13
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Vrontaki E, Melagraki G, Mavromoustakos T, Afantitis A. Searching for anthranilic acid-based thumb pocket 2 HCV NS5B polymerase inhibitors through a combination of molecular docking, 3D-QSAR and virtual screening. J Enzyme Inhib Med Chem 2015; 31:38-52. [PMID: 26060939 DOI: 10.3109/14756366.2014.1003925] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A combination of the following computational methods: (i) molecular docking, (ii) 3-D Quantitative Structure Activity Relationship Comparative Molecular Field Analysis (3D-QSAR CoMFA), (iii) similarity search and (iv) virtual screening using PubChem database was applied to identify new anthranilic acid-based inhibitors of hepatitis C virus (HCV) replication. A number of known inhibitors were initially docked into the "Thumb Pocket 2" allosteric site of the crystal structure of the enzyme HCV RNA-dependent RNA polymerase (NS5B GT1b). Then, the CoMFA fields were generated through a receptor-based alignment of docking poses to build a validated and stable 3D-QSAR CoMFA model. The proposed model can be first utilized to get insight into the molecular features that promote bioactivity, and then within a virtual screening procedure, it can be used to estimate the activity of novel potential bioactive compounds prior to their synthesis and biological tests.
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Affiliation(s)
- Eleni Vrontaki
- a Department of Chemoinformatics , NovaMechanics Ltd. , Nicosia , Cyprus and.,b Department of Chemistry, Laboratory of Organic Chemistry , University of Athens , Athens , Greece
| | - Georgia Melagraki
- a Department of Chemoinformatics , NovaMechanics Ltd. , Nicosia , Cyprus and
| | - Thomas Mavromoustakos
- b Department of Chemistry, Laboratory of Organic Chemistry , University of Athens , Athens , Greece
| | - Antreas Afantitis
- a Department of Chemoinformatics , NovaMechanics Ltd. , Nicosia , Cyprus and
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Toropova AP, Toropov AA, Veselinović JB, Veselinović AM. QSAR as a random event: a case of NOAEL. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2015; 22:8264-8271. [PMID: 25520208 DOI: 10.1007/s11356-014-3977-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Accepted: 12/09/2014] [Indexed: 06/04/2023]
Abstract
Quantitative structure-activity relationships (QSAR) for no observed adverse effect levels (NOAEL, mmol/kg/day, in logarithmic units) are suggested. Simplified molecular input line entry systems (SMILES) were used for molecular structure representation. Monte Carlo method was used for one-variable models building up for three different splits into the "visible" training set and "invisible" validation. The statistical quality of the models for three random splits are the following: split 1 n = 180, r (2) = 0.718, q (2) = 0.712, s = 0.403, F = 454 (training set); n = 17, r (2) = 0.544, s = 0.367 (calibration set); n = 21, r (2) = 0.61, s = 0.44, r m (2) = 0.61 (validation set); split 2 n = 169, r (2) = 0.711, q (2) = 0.705, s = 0.409, F = 411 (training set); n = 27, r (2) = 0.512, s = 0.461 (calibration set); n = 22, r (2) = 0.669, s = 0.360, r m (2) = 0.63 (validation set); split 3 n = 172, r (2) = 0.679, q (2) = 0.672, s = 0.420, F = 360 (training set); n = 19, r (2) = 0.617, s = 0.582 (calibration set); n = 21, r (2) = 0.627, s = 0.367, r m (2) = 0.54 (validation set). All models are built according to OCED principles.
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Affiliation(s)
- Alla P Toropova
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milano, Italy
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Begum S, Achary PGR. Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1). SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:343-361. [PMID: 25967103 DOI: 10.1080/1062936x.2015.1039577] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Quantitative structure-activity relationship (QSAR) models were built for the prediction of inhibition (pIC50, i.e. negative logarithm of the 50% effective concentration) of MAP kinase-interacting protein kinase (MNK1) by 43 potent inhibitors. The pIC50 values were modelled with five random splits, with the representations of the molecular structures by simplified molecular input line entry system (SMILES). QSAR model building was performed by the Monte Carlo optimisation using three methods: classic scheme; balance of correlations; and balance correlation with ideal slopes. The robustness of these models were checked by parameters as rm(2), r(*)m(2), [Formula: see text] and randomisation technique. The best QSAR model based on single optimal descriptors was applied to study in vitro structure-activity relationships of 6-(4-(2-(piperidin-1-yl) ethoxy) phenyl)-3-(pyridin-4-yl) pyrazolo [1,5-a] pyrimidine derivatives as a screening tool for the development of novel potent MNK1 inhibitors. The effects of alkyl group, -OH, -NO2, F, Cl, Br, I, etc. on the IC50 values towards the inhibition of MNK1 were also reported.
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Affiliation(s)
- S Begum
- a Department of Chemistry , Institute of Technical Education and Research (ITER), Siksha 'O' Anusandhan University , Bhubaneswar , Odisha - 751030 , India
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Toropov AA, Toropova AP. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. CHEMOSPHERE 2015; 124:40-46. [PMID: 25465947 DOI: 10.1016/j.chemosphere.2014.10.067] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 10/15/2014] [Accepted: 10/18/2014] [Indexed: 06/04/2023]
Abstract
Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set).
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Affiliation(s)
- Andrey A Toropov
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy.
| | - Alla P Toropova
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy
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Toropov AA, Toropova AP, Benfenati E, Nicolotti O, Carotti A, Nesmerak K, Veselinović AM, Veselinović JB, Duchowicz PR, Bacelo D, Castro EA, Rasulev BF, Leszczynska D, Leszczynski J. QSPR/QSAR Analyses by Means of the CORAL Software. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS IN DRUG DESIGN, PREDICTIVE TOXICOLOGY, AND RISK ASSESSMENT 2015. [DOI: 10.4018/978-1-4666-8136-1.ch015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Pablo R. Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Argentina
| | | | - Eduardo A. Castro
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Argentina
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Vrontaki E, Melagraki G, Mavromoustakos T, Afantitis A. Exploiting ChEMBL database to identify indole analogs as HCV replication inhibitors. Methods 2015; 71:4-13. [DOI: 10.1016/j.ymeth.2014.03.021] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2014] [Revised: 03/11/2014] [Accepted: 03/13/2014] [Indexed: 12/16/2022] Open
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