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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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Tourneix F, Alépée N, Detroyer A, Eilstein J, Martinozzi Teissier S, Nardelli L, Noçairi H, Pauloin T, Piroird C, Del Bufalo A. Assessment of a defined approach based on a stacking prediction model to identify skin sensitization hazard. Toxicol In Vitro 2019; 60:134-143. [DOI: 10.1016/j.tiv.2019.05.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 05/10/2019] [Accepted: 05/13/2019] [Indexed: 10/26/2022]
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3
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Toropov AA, Toropova AP. The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models? MUTATION RESEARCH-GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2017. [PMID: 28622828 DOI: 10.1016/j.mrgentox.2017.05.008] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
The index of ideality of correlation (IIC) is a new criterion of the predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs). This IIC is calculated with using of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The mutagenicity is well-known important characteristic of substances from ecological point of view. Consequently, the estimation of the IIC for mutagenicity is well motivated. It is confirmed that the utilization of this criterion significantly improves the predictive potential of QSAR models of mutagenicity. The new criterion can be used for other endpoints.
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Affiliation(s)
- Andrey A Toropov
- IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, 20156, Italy
| | - Alla P Toropova
- IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, 20156, Italy.
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Ren YY, Zhou LC, Yang L, Liu PY, Zhao BW, Liu HX. Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:721-746. [PMID: 27653817 DOI: 10.1080/1062936x.2016.1229691] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 08/22/2016] [Indexed: 06/06/2023]
Abstract
The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.
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Affiliation(s)
- Y Y Ren
- a School of Environmental and Municipal Engineering, Lanzhou Jiaotong University , Lanzhou , P.R. China
| | - L C Zhou
- b College of Chemistry and Chemical Engineering, Lanzhou University , Lanzhou , P.R. China
| | - L Yang
- a School of Environmental and Municipal Engineering, Lanzhou Jiaotong University , Lanzhou , P.R. China
| | - P Y Liu
- a School of Environmental and Municipal Engineering, Lanzhou Jiaotong University , Lanzhou , P.R. China
| | - B W Zhao
- a School of Environmental and Municipal Engineering, Lanzhou Jiaotong University , Lanzhou , P.R. China
| | - H X Liu
- c School of Pharmacy, Lanzhou University , Lanzhou , P.R. China
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5
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Prediction of glucagon receptor antagonist activities of some substituted imidazoles using combined radial basis function neural network and density functional theory. Med Chem Res 2013. [DOI: 10.1007/s00044-013-0869-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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6
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Leong MK, Lin SW, Chen HB, Tsai FY. Predicting Mutagenicity of Aromatic Amines by Various Machine Learning Approaches. Toxicol Sci 2010; 116:498-513. [DOI: 10.1093/toxsci/kfq159] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
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7
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Minovski N, Vračko M, Šolmajer T. Quantitative structure–activity relationship study of antitubercular fluoroquinolones. Mol Divers 2010; 15:417-26. [DOI: 10.1007/s11030-010-9238-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2009] [Accepted: 02/22/2010] [Indexed: 11/29/2022]
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8
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AFIUNI-ZADEH S, AZIMI G. A QSAR Study for Modeling of 8-Azaadenine Analogues Proposed as A1 Adenosine Receptor Antagonists Using Genetic Algorithm Coupling Adaptive Neuro-Fuzzy Inference System (ANFIS). ANAL SCI 2010; 26:897-902. [DOI: 10.2116/analsci.26.897] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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9
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Lather V, Fernandes M. QSAR Models for Prediction of PPARδ Agonistic Activity of Indanylacetic Acid Derivatives. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200810092] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Nandi S, Vracko M, Bagchi MC. Anticancer activity of selected phenolic compounds: QSAR studies using ridge regression and neural networks. Chem Biol Drug Des 2008; 70:424-36. [PMID: 17949360 DOI: 10.1111/j.1747-0285.2007.00575.x] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Phenol and its congeners are known to induce caspase-mediated apoptosis activity and cytotoxicity on various cancer cell lines. Apoptosis, scavenging of radicals, antioxidant, and pro-oxidant characteristics are primarily responsible for the antitumor activities of phenolic compounds. Quantitative structure-activity relationship studies on the cellular apoptosis and cytotoxicity of phenolic compounds have been investigated recently by Selassie and colleagues (J Med Chem; 48:7234, 2005) wherein models were developed for various carcinogenic cell lines. These quantitative structure-activity relationship models are based on few experimentally obtained physicochemical parameters such as Verloop's sterimol descriptor, hydrophobicity, Hammett electronic parameter, and octanol/water partition coefficient. The paper deals with structure-activity relationships of phenols and its derivatives for the development of predictive models from the standpoint of theoretical structural parameters and ridge regression methodology. The quantitative structure-activity relationship studies developed here for the caspase-mediated apoptosis activity and cytotoxicity on murine leukemia cell line (L1210), human promylolytic cell line (HL-60), human breast cancer cell line (MCF-7), parenteral human acute lymphoblastic cells (CCRF-CEM), and multidrug-resistant subline of CCRF-resistant to vinblastine (CEM/VLB) cells utilize physicochemical molecular descriptors calculated solely from the structure of phenolic compounds under investigation along with the descriptors used by Selassie and group. It is seen that such quantitative structure-activity relationships can provide a better quality predictive model for the phenolic compounds. The biological activities of the nine sets of phenolic compounds have been calculated based on ridge regression analysis that clearly gives a better significant correlation compared to the activities predicted by Selassie and co-workers. Counter-propagation artificial neural network studies have been introduced in the present investigation for a better understanding of multidimensional rational patterns in more complex data sets. The counter-propagation artificial neural network studies were performed on the same data set and with the same descriptors as have been carried out in developing ridge regression models and the result of counter-propagation neural network models produces very interesting findings in terms of leave-one-out test. Finally, an attempt has been made for a comparative study of the relative effectiveness of linear statistical methods versus nonlinear techniques, such as counter-propagation neural networks in modeling structure-activity studies of the phenolic compounds.
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Affiliation(s)
- Sisir Nandi
- Structural Biology and Bioinformatics Division, Indian Institute of Chemical Biology, 4 Raja S.C. Mullick Road, Jadavpur, Calcutta, India
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Prediction Partial Molar Heat Capacity at Infinite Dilution for Aqueous Solutions of Various Polar Aromatic Compounds over a Wide Range of Conditions Using Artificial Neural Networks. B KOREAN CHEM SOC 2007. [DOI: 10.5012/bkcs.2007.28.9.1477] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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12
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Artificial Neural Network Prediction of Normalized Polarity Parameter for Various Solvents with Diverse Chemical Structures. B KOREAN CHEM SOC 2007. [DOI: 10.5012/bkcs.2007.28.9.1472] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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13
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Buyukbingol E, Sisman A, Akyildiz M, Alparslan FN, Adejare A. Adaptive neuro-fuzzy inference system (ANFIS): A new approach to predictive modeling in QSAR applications: A study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists. Bioorg Med Chem 2007; 15:4265-82. [PMID: 17434739 DOI: 10.1016/j.bmc.2007.03.065] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2006] [Accepted: 03/20/2007] [Indexed: 11/18/2022]
Abstract
This paper proposes a new method, Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate physicochemical descriptors of certain chemical compounds for their appropriate biological activities in terms of QSAR models with the aid of artificial neural network (ANN) approach combined with the principle of fuzzy logic. The ANFIS was utilized to predict NMDA (N-methyl-d-Aspartate) receptor binding activities of phencyclidine (PCP) derivatives. A data set of 38 drug-like compounds was coded with 1244 calculated molecular structure descriptors (clustered in 20 data sets) which were obtained from several sources, mainly from Dragon software. Prior to the progress to the ANFIS system, descriptors from the best subsets were selected using unsupervised forward selection (UFS) to eliminate redundancy and multicollinearity followed by fuzzy linear regression algorithm (FLR) which was used for variable selection. ANFIS was applied to train the final descriptors (Mor22m, E3s, R3v+, and R1e+) using a hybrid algorithm consisting of back-propagation and least-square estimation while the optimum number and shape of related functions were obtained through the subtractive clustering algorithm. Comparison of the proposed method with traditional methods, that is, multiple linear regression (MLR) and partial least-square (PLS) was also studied and the results indicated that the ANFIS model obtained from data sets achieved satisfactory accuracy.
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Affiliation(s)
- Erdem Buyukbingol
- Ankara University, Faculty of Pharmacy (ECZACILIK), Department of Pharmaceutical Chemistry, Tandogan 06100, Ankara, Turkey.
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Torres-Cartas S, Martín-Biosca Y, Villanueva-Camañas RM, Sagrado S, Medina-Hernández MJ. Biopartitioning micellar chromatography to predict mutagenicity of aromatic amines. Eur J Med Chem 2007; 42:1396-402. [PMID: 17482318 DOI: 10.1016/j.ejmech.2007.02.022] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2007] [Revised: 02/26/2007] [Accepted: 02/27/2007] [Indexed: 12/01/2022]
Abstract
Mutagenicity is a toxicity endpoint associated with the chronic exposure to chemicals. Aromatic amines have considerable industrial and environmental importance due to their widespread use in industry and their mutagenic capacity. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases of Brij35 in adequate experimental conditions, has demonstrated to be useful in mimicking the drug partitioning process into biological systems. In this paper, the usefulness of BMC for predicting mutagenicity of aromatic amines is demonstrated. A multiple linear regression (MLR) model based on BMC retention data is proposed and compared with other ones reported in bibliography. The proposed model present better or similar descriptive and predictive capability.
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Affiliation(s)
- S Torres-Cartas
- Departamento de Química Analítica, Universidad de Valencia, C/Vicente Andrés Estellés s/n, 46100 Burjassot, Valencia, Spain
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15
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Ghafourian T, Cronin M. The Effect of Variable Selection on the Non-linear Modelling of Oestrogen Receptor Binding. ACTA ACUST UNITED AC 2006. [DOI: 10.1002/qsar.200510153] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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16
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Vracko M, Bandelj V, Barbieri P, Benfenati E, Chaudhry Q, Cronin M, Devillers J, Gallegos A, Gini G, Gramatica P, Helma C, Mazzatorta P, Neagu D, Netzeva T, Pavan M, Patlewicz G, Randić M, Tsakovska I, Worth A. Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2006; 17:265-84. [PMID: 16815767 DOI: 10.1080/10659360600787650] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.
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Affiliation(s)
- M Vracko
- European Chemical Beaureau, Institute for Health and Consumer Protection, European Commission Joint Research Centre, 21020 Ispra, Italy.
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Prediction Acidity Constant of Various Benzoic Acids and Phenols in Water Using Linear and Nonlinear QSPR Models. B KOREAN CHEM SOC 2005. [DOI: 10.5012/bkcs.2005.26.12.2007] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Dohnal V, Kuca K, Jun D. WHAT ARE ARTIFICIAL NEURAL NETWORKS AND WHAT THEY CAN DO? Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2005; 149:221-4. [PMID: 16601760 DOI: 10.5507/bp.2005.030] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
The artificial neural networks (ANN) are very often applied in many areas of toxicology for the solving of complex problems, such as the prediction of chemical compound properties and quantitative structure-activity relationship. The aim of this contribution is to give the basic knowledge about conception of ANN, theirs division and finally, the typical application of ANN will be discussed. Due to the diversity of architectures and adaptation algorithms, the ANNs are used in the broad spectrum of applications from the environmental processes modeling, through the optimization to quantitative structure-activity relationship (QSAR) methods. In addition, especially ANNs with Kohonen learning are very effective classification tool. The ANNs are mostly applied in cases, where the commonly used methods does not work.
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Affiliation(s)
- Vlastimil Dohnal
- Department of Food Technology, Mendel University of Agriculture and Forestry, Brno, Czech Republic.
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Prediction of Solvent Effects on Rate Constant of [2+2] Cycloaddition Reaction of Diethyl Azodicarboxylate with Ethyl Vinyl Ether Using Artificial Neural Networks. B KOREAN CHEM SOC 2005. [DOI: 10.5012/bkcs.2005.26.1.139] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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