1
|
Bunmahotama W, Vijver MG, Peijnenburg W. Development of a Quasi-Quantitative Structure-Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal-Based Nanomaterials. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2022; 41:1439-1450. [PMID: 35234298 PMCID: PMC9325417 DOI: 10.1002/etc.5322] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/17/2021] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
The conventional Hill equation model is suitable to fit dose-response data obtained from performing (eco)toxicity assays. Models based on quasi-quantitative structure-activity relationships (QSARs) to estimate the Hill coefficient ( n H ) ${n}_{{\rm{H}}})$ were developed with the aim of predicting the response of the invertebrate species Daphnia magna to exposure to metal-based nanomaterials. Descriptors representing the pristine properties of nanoparticles and media conditions were coded to a quasi-simplified molecular input line entry system and correlated to experimentally derived values of n H ${n}_{{\rm{H}}}$ . Monte Carlo optimization was used to model the set of n H ${n}_{{\rm{H}}}$ values, and the model was trained on the basis of reported dose-response relationships of 60 data sets (n = 367 individual response observations) of 11 metal-based nanomaterials as obtained from 20 literature reports. The model simulates the training data well, with only 2.3% deviation between experimental and modeled response data. The technique was employed to predict the dose-response relationships of 15 additional data sets (n = 72 individual observations) not included in model development of seven metal-based nanomaterials from 10 literature reports, with an average error of 3.5%. Combining the model output with either the median effective concentration value or any other known effect level as obtained from experimental data allows the prediction of full dose-response curves of D. magna immobilization. This model is an accurate screening tool that allows the determination of the shape and slope of dose-response curves, thereby greatly reducing experimental effort in case of novel advanced metal-based nanomaterials or the prediction of responses in altered exposure media. This screening model is compliant with the 3Rs (replacement, reduction, and refinement) principle, which is embraced by the scientific and regulatory communities dealing with nano-safety. Environ Toxicol Chem 2022;41:1439-1450. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Collapse
Affiliation(s)
- Warisa Bunmahotama
- Institute of Environmental SciencesLeiden UniversityLeidenThe Netherlands
| | - Martina G. Vijver
- Institute of Environmental SciencesLeiden UniversityLeidenThe Netherlands
| | - Willie Peijnenburg
- Institute of Environmental SciencesLeiden UniversityLeidenThe Netherlands
- Center for Safety of Substances and ProductsNational Institute of Public Health and the EnvironmentBilthovenThe Netherlands
| |
Collapse
|
2
|
Sayyadikord Abadi R, Shojaei AF, Tatafei FE, Alizadeh O. Theoretical Study of Octreotide Derivatives as Anti-Cancer Drugs using QSAR, Monte Carlo Method and formation of Complexes. RUSSIAN JOURNAL OF PHYSICAL CHEMISTRY B 2022. [DOI: 10.1134/s199079312201002x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
3
|
Patnode K, Rasulev B, Voronov A. Synergistic Behavior of Plant Proteins and Biobased Latexes in Bioplastic Food Packaging Materials: Experimental and Machine Learning Study. ACS APPLIED MATERIALS & INTERFACES 2022; 14:8384-8393. [PMID: 35119263 DOI: 10.1021/acsami.1c21650] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Plant-based proteins are attractive components which may serve as sustainable alternatives to current petrochemical products. Both soy protein and major corn protein, zein, are of interest in food packaging applications due to their sustainability, biodegradation properties, and inherent physicochemical properties. This study discusses the development of bioplastic materials, where it explores the effects of combining zein, soy protein, and plasticizing latexes derived from plant oil-based monomers (POBMs) on properties of resulting bioplastic films. By looking for synergistic effects of soy protein's inherent film formation ability and zein's higher strength, we prepare strong yet flexible soy-zein films as materials, called proteoposites. Incorporation of natural additive POBM-latexes helps to plasticize and hydrophobize the bioplastic films and thus to improve mechanical and barrier properties. Variation of the POBM-latexes' particle size further aims to enhance the performance of resulting bioplastic films. As a result, modified soy-zein proteoposite films with improved moisture resistance, enhanced mechanical behavior, and greater barrier properties were developed. Machine learning-based computational models were utilized in order to find main structural factors affecting the bioplastic's properties and develop a quantitative structure-property relationship model between the physicochemical properties of the film components and the resulted bioplastics' properties and performance. The developed model effectively predicts experimental outcomes with >85% (R2: 0.85) accuracy. The newly synthesized proteoposites confirmed the machine learning model predictions. As a result, proteoposite films made of two plant proteins and modified with POBM-latexes can be considered as an attractive and viable replacement for petrochemical food packaging products.
Collapse
Affiliation(s)
- Kristen Patnode
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102-6050, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102-6050, United States
| | - Andriy Voronov
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102-6050, United States
| |
Collapse
|
4
|
Maria Nikkar, Robabeh SayyadikordAbadi, Alizadehdakhel A, Ghasemi G. Monte Carlo Method and a Novel Modelling-Optimization Approach on QSAR Study of Doxazolidine Drugs and DNA-Binding. RUSSIAN JOURNAL OF PHYSICAL CHEMISTRY B 2021. [DOI: 10.1134/s199079312109013x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
5
|
Toropov AA, Toropova AP. Correlation intensity index: Building up models for mutagenicity of silver nanoparticles. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 737:139720. [PMID: 32554036 DOI: 10.1016/j.scitotenv.2020.139720] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/21/2020] [Accepted: 05/24/2020] [Indexed: 06/11/2023]
Abstract
Nanomaterials become significant component of economics. Consequently, nanomaterials become object of environmental sciences. There is a traditional list of endpoints which are indicators of the ecological risk. Mutagenicity is one of important component in this list. The quasi-SMILES approach, that in contrast to majority of work dedicated to modelling behaviour of nanomaterials gives possibility to consider experimental conditions as well as other circumstances which can impact the behaviour of nanomaterials is suggested. This is carried out via so-called quasi-SMILES. The quasi-SMILES is a line on of codes that contains all the above available eclectic data. Modelling process aimed to build up a model involves Correlation Intensity Index (CII) that is a new criterion of predictive potential of models. The scheme of calculation of CII is described in this work in the first time. The applying of CII together with Index of Ideality Correlation (IIC) in modelling of mutagenicity of silver nanoparticles by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral) indicates that application of the CII improves the predictive potential of these models for three random splits into the training set (75%) and validation set (25%).
Collapse
Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| |
Collapse
|
6
|
Gadaleta D, Marzo M, Toropov A, Toropova A, Lavado GJ, Escher SE, Dorne JLCM, Benfenati E. Integrated In Silico Models for the Prediction of No-Observed-(Adverse)-Effect Levels and Lowest-Observed-(Adverse)-Effect Levels in Rats for Sub-chronic Repeated-Dose Toxicity. Chem Res Toxicol 2020; 34:247-257. [PMID: 32664725 DOI: 10.1021/acs.chemrestox.0c00176] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Repeated-dose toxicity (RDT) is a critical endpoint for hazard characterization of chemicals and is assessed to derive safe levels of exposure for human health. Here we present the first attempt to model simultaneously no-observed-(adverse)-effect level (NO(A)EL) and lowest-observed-(adverse)-effect level (LO(A)EL). Classification and regression models were derived based on rat sub-chronic repeated dose toxicity data for 327 compounds from the Fraunhofer RepDose database. Multi-category classification models were built for both NO(A)EL and LO(A)EL though a consensus of statistics- and fragment-based algorithms, while regression models were based on quantitative relationships between the endpoints and SMILES-based attributes. NO(A)EL and LO(A)EL models were integrated, and predictions were compared to exclude inconsistent values. This strategy improved the performance of single models, leading to R2 greater than 0.70, root-mean-square error (RMSE) lower than 0.60 (for regression models), and accuracy of 0.61-0.73 (for classification models) on the validation set, based on the endpoint and the threshold applied for selecting predictions. This study confirms the effectiveness of the modeling strategy presented here for assessing RDT of chemicals using in silico models.
Collapse
Affiliation(s)
- Domenico Gadaleta
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milan, Italy
| | - Marco Marzo
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milan, Italy
| | - Andrey Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milan, Italy
| | - Alla Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milan, Italy
| | - Giovanna J Lavado
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milan, Italy
| | - Sylvia E Escher
- Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM), 30625 Hannover, Germany
| | - Jean Lou C M Dorne
- Scientific Committee and Emerging Risks Unit, European Food Safety Authority, 43126 Parma, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milan, Italy
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Khan K, Baderna D, Cappelli C, Toma C, Lombardo A, Roy K, Benfenati E. Ecotoxicological QSAR modeling of organic compounds against fish: Application of fragment based descriptors in feature analysis. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2019; 212:162-174. [PMID: 31128417 DOI: 10.1016/j.aquatox.2019.05.011] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 05/16/2019] [Accepted: 05/16/2019] [Indexed: 06/09/2023]
Abstract
Organic compounds (OCs) constitute an enormously large class of highly persistent and toxic chemicals widely used for various purposes throughout the world. Their increased detection in water bodies, mainly sewage treatment plants via industrial discharge, has rendered them to become a cause for ecological concern. The limited availability of experimental toxicological data has necessitated development of models that can help us identify the most hazardous and potentially toxic compounds thus prioritizing the experiments on the selected chemicals. Computational tools such as quantitative structure-activity relationship (QSAR) can be used to predict the missing data and classify the chemicals based on their acute predicted responses for existing as well as not yet synthesized chemicals. In the current study, novel, externally validated, highly robust local QSAR models for different chemical classes and moderately robust global QSAR models were developed using partial least squares (PLS) regression technique using a large dataset of 1121 OCs for the fish mortality endpoint. For feature selection, genetic algorithm along with stepwise regression was used while model validation was performed using various stringent validation criteria following the strict rules of OECD guidelines of QSAR validation. The variables included in the models were obtained from simplex representation of molecular structures (SiRMS) (Version 4.1.2.270), Dragon (Version 7.0) and PaDEL-descriptor software (Version 2.20). The final developed models were robust, externally predictive and characterized by a large chemical as well as biological domain. The predictive efficiency of the developed models was then compared with the ECOSAR tool in order to justify the applicability of the developed models in ecotoxicological predictions for organic chemicals. Better predictive efficiency of the developed QSAR models compared to the ECOSAR derived predictions signifies their applicability in early risk assessment of known as well as untested chemicals in order to design safer alternatives to the environment.
Collapse
Affiliation(s)
- Kabiruddin Khan
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India
| | - Diego Baderna
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy
| | - Claudia Cappelli
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy
| | - Cosimo Toma
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy
| | - Anna Lombardo
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India; Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy.
| | - Emilio Benfenati
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India.
| |
Collapse
|
9
|
“Ideal correlations” for biological activity of peptides. Biosystems 2019; 181:51-57. [DOI: 10.1016/j.biosystems.2019.04.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 02/18/2019] [Accepted: 04/12/2019] [Indexed: 02/08/2023]
|
10
|
Toma C, Gadaleta D, Roncaglioni A, Toropov A, Toropova A, Marzo M, Benfenati E. QSAR Development for Plasma Protein Binding: Influence of the Ionization State. Pharm Res 2018; 36:28. [PMID: 30591975 PMCID: PMC6308215 DOI: 10.1007/s11095-018-2561-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 12/17/2018] [Indexed: 01/05/2023]
Abstract
Purpose This study explored several strategies to improve the performance of literature QSAR models for plasma protein binding (PPB), such as a suitable endpoint transformation, a correct representation of chemicals, more consistency in the dataset, and a reliable definition of the applicability domain. Methods We retrieved human fraction unbound (Fu) data for 670 compounds from the literature and carefully checked them for consistency. Descriptors were calculated taking account of the ionization state of molecules at physiological pH (7.4), in order to better estimate the affinity of molecules to blood proteins. We used different algorithms and chemical descriptors to explore the most suitable strategy for modeling the endpoint. SMILES (simplified molecular input line entry system)-based string descriptors were also tested with the CORAL software (CORelation And Logic). We did an outlier analysis to establish the models to use (or not to use) in case of well recognized families. Results Internal validation of the selected models returned Q2 values close to 0.60. External validation also gave r2 values always greater than 0.60. The CORAL descriptor based model for √fu was the best, with r2 0.74 in external validation. Conclusions Performance in prediction confirmed the robustness of all the derived models and their suitability for real-life purposes, i.e. screening chemicals for their ADMET profiling. Optimization of descriptors can be useful in order to obtain the correct results with a ionized molecule. Electronic supplementary material The online version of this article (10.1007/s11095-018-2561-8) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Cosimo Toma
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy.
| | - Domenico Gadaleta
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| | - Andrey Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| | - Alla Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| | - Marco Marzo
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156, Milano, Italy
| |
Collapse
|
11
|
Halder AK. Finding the structural requirements of diverse HIV-1 protease inhibitors using multiple QSAR modelling for lead identification. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2018; 29:911-933. [PMID: 30332922 DOI: 10.1080/1062936x.2018.1529702] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 09/25/2018] [Indexed: 06/08/2023]
Abstract
Multiple Quantitative Structure-Activity Relationship (QSAR) analysis is widely used in drug discovery for lead identification. Human Immunodeficiency Virus (HIV) protease is one of the key targets for the treatment of Acquired Immunodeficiency Syndrome (AIDS). One of the major challenges for the design of HIV-1 protease inhibitors (HIV PRIs) is to increase the inhibitory activities against the enzyme to a level where the problem associated to drug resistance may be considerably delayed. Herein, chemometric analyses were performed with 346 structurally diverse HIV PRIs with experimental bioactivities against a sub-type B mutant to develop highly predictable QSAR models and also to identify the effective structural determinants for higher affinity against HIV PR. The QSAR models were developed using OCHEM-based machine learning tools (ASNN, FSMLR, KNN, RF, MANN and XGBoost), with descriptors calculated by eight different software packages. Simultaneously, a Monte Carlo optimization-based QSAR modelling was performed using SMILES and graph-based descriptors to understand fragment and topochemical contributions. To validate the actual predictability of all these models, an additional set of 104 compounds (also with known experimental activities) with slightly different chemical space were employed. This ligand-based study serves as a crucial benchmark for further development of the HIV protease inhibitors with improved activities.
Collapse
Affiliation(s)
- A K Halder
- a School of Health Sciences, University of KwaZulu-Natal , Durban , South Africa
| |
Collapse
|
12
|
Use of Simplified Molecular Input Line Entry System and molecular graph based descriptors in prediction and design of pancreatic lipase inhibitors. Future Med Chem 2018; 10:1603-1622. [DOI: 10.4155/fmc-2018-0024] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Aim: The inhibition of pancreatic lipase (PL) enzyme is the most explored strategy for the treatment of obesity. The present study describes the development of quantitative structure–activity relationship (QSAR) models for a diverse set of 293 PL inhibitors by means of the Monte Carlo optimization technique. Methodology & results: The hybrid optimal descriptors were used to build QSAR models with three subsets of three splits. The developed QSAR models were further validated with corresponding external sets. The best QSAR model has the following statistical particulars: R2 = 0.752, Q LOO 2 = 0 . 736 for the test set and R2 = 0.768, Q F 1 2 = 0 . 628 , Q F 2 2 = 0 . 621 for the validation set. Conclusion: The developed QSAR models were robust, stable and predictive and led to the design of novel PL inhibitors.
Collapse
|
13
|
Toropov AA, Toropova AP, Raitano G, Benfenati E. CORAL: Building up QSAR models for the chromosome aberration test. Saudi J Biol Sci 2018; 26:1101-1106. [PMID: 31516335 PMCID: PMC6734133 DOI: 10.1016/j.sjbs.2018.05.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 04/23/2018] [Accepted: 05/06/2018] [Indexed: 12/13/2022] Open
Abstract
A high level of chromosomal aberrations in peripheral blood lymphocytes may be an early marker of cancer risk, but data on risk of specific cancers and types of chromosomal aberrations are limited. Consequently, the development of predictive models for chromosomal aberrations test is important task. Majority of models for chromosomal aberrations test are so-called knowledge-based rules system. The CORAL software (http://www.insilico.eu/coral, abbreviation of “CORrelation And Logic”) is an alternative for knowledge-based rules system. In contrast to knowledge-based rules system, the CORAL software gives possibility to estimate the influence upon the predictive potential of a model of different molecular alerts as well as different splits into the training set and validation set. This possibility is not available for the approaches based on the knowledge-based rules system. Quantitative Structure–Activity Relationships (QSAR) for chromosome aberration test are established for five random splits into the training, calibration, and validation sets. The QSAR approach is based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) without data on physicochemical and/or biochemical parameters. In spite of this limitation, the statistical quality of these models is quite good.
Collapse
Affiliation(s)
| | - Alla P. Toropova
- Corresponding author at: Laboratory of Environmental Chemistry and Toxicology, IRCCS – Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy.
| | | | | |
Collapse
|
14
|
Toropova AP, Toropov AA. CORAL: QSAR models for carcinogenicity of organic compounds for male and female rats. Comput Biol Chem 2018; 72:26-32. [PMID: 29310001 DOI: 10.1016/j.compbiolchem.2017.12.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 12/04/2017] [Accepted: 12/30/2017] [Indexed: 01/17/2023]
Abstract
Quantitative structure - activity relationships (QSARs) for carcinogenicity (rats, TD50) have been built up using the CORAL software. Different molecular features, which are extracted from simplified molecular input-line entry system (SMILES) serve as the basis for building up a model. Correlation weights for the molecular features are calculated by means of the Monte Carlo optimization. Using the numerical data on the correlation weights, one can calculate a model of carcinogenicity as a mathematical function of descriptors, which are sum of the corresponding correlation weights. In other words, the correlation weights provide the maximal correlation coefficient between the descriptor and carcinogenicity, for the training set. This correlation was assessed via external validation set. Finally, lists of molecular alerts in aspects of carcinogenicity for male rats and for female rats were compared and their differences were discussed.
Collapse
Affiliation(s)
- Alla P Toropova
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milano, Italy.
| | - Andrey A Toropov
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milano, Italy
| |
Collapse
|
15
|
Improved Model for Biodegradability of Organic Compounds: The Correlation Contributions of Rings. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2018. [DOI: 10.1007/978-1-4939-7425-2_8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
|
16
|
Toropov AA, Toropova AP, Cappellini L, Benfenati E, Davoli E. QSPR analysis of threshold of odor for the large number of heterogenic chemicals. Mol Divers 2017; 22:397-403. [DOI: 10.1007/s11030-017-9800-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 11/23/2017] [Indexed: 11/24/2022]
|
17
|
Toropov AA, Toropova AP, Beeg M, Gobbi M, Salmona M. QSAR model for blood-brain barrier permeation. J Pharmacol Toxicol Methods 2017; 88:7-18. [DOI: 10.1016/j.vascn.2017.04.014] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 04/20/2017] [Accepted: 04/30/2017] [Indexed: 12/14/2022]
|
18
|
|
19
|
Toropova AP, Toropov AA. CORAL: Binary classifications (active/inactive) for drug-induced liver injury. Toxicol Lett 2017; 268:51-57. [PMID: 28111161 DOI: 10.1016/j.toxlet.2017.01.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 12/16/2016] [Accepted: 01/16/2017] [Indexed: 12/16/2022]
Abstract
INTRODUCTION The data on human hepatotoxcity (drug-induced liver injury) is extremely important information from point of view of drug discovery. Experimental clinical data on this endpoint is scarce. Experimental way to extend databases on this endpoint is extremely difficult. Quantitative structure - activity relationships (QSAR) is attractive alternative of the experimental approach. METHODS Predictive models for human hepatotoxicity (drug-induced liver injury) have been built up by the Monte Carlo method with using of the CORAL software (http://www.insilico.eu/coral). These models are the binary classifications into active class and inactive class. These models are calculated with so-called "semi correlations" described in this work. The Mattews correlation coefficient of these models for external validation sets ranged from 0.52 to 0.62. RESULTS DISCUSSION The approach has been checked up with a group of random splits into the training and validation sets. These stochastic experiments have shown the stability of results: predictability of the models for various splits. Thus, the attempt to build up the classification QSAR model by means of the Monte Carlo technique, based on representation of the molecular structure via simplified molecular input line entry systems (SMILES) and hydrogen suppressed graph (HSG) using the CORAL software (http://www.insilico.eu/coral) has shown ability of this approach to provide quite good prediction of the examined endpoint (drug-induced liver injury).
Collapse
Affiliation(s)
- Alla P Toropova
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milano, Italy.
| | - Andrey A Toropov
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milano, Italy
| |
Collapse
|
20
|
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.
Collapse
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
| | | | | | | |
Collapse
|
21
|
Toropova AP, Toropov AA, Veselinović AM, Veselinović JB, Leszczynska D, Leszczynski J. Monte Carlo-based quantitative structure-activity relationship models for toxicity of organic chemicals to Daphnia magna. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2016; 35:2691-2697. [PMID: 27110865 DOI: 10.1002/etc.3466] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 04/18/2016] [Accepted: 04/21/2016] [Indexed: 06/05/2023]
Abstract
Quantitative structure-activity relationships (QSARs) for toxicity of a large set of 758 organic compounds to Daphnia magna were built up. The simplified molecular input-line entry system (SMILES) was used to represent the molecular structure. The Correlation and Logic (CORAL) software was utilized as a tool to develop the QSAR models. These models are built up using the Monte Carlo method and according to the principle "QSAR is a random event" if one checks a group of random distributions in the visible training set and the invisible validation set. Three distributions of the data into the visible training, calibration, and invisible validation sets are examined. The predictive potentials (i.e., statistical characteristics for the invisible validation set of the best model) are as follows: n = 87, r2 = 0.8377, root mean square error = 0.564. The mechanistic interpretations and the domain of applicability of built models are suggested and discussed. Environ Toxicol Chem 2016;35:2691-2697. © 2016 SETAC.
Collapse
Affiliation(s)
- Alla P Toropova
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.
| | - Andrey A Toropov
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | | | | | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, Jackson, Mississippi, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
| |
Collapse
|
22
|
Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis. Struct Chem 2016. [DOI: 10.1007/s11224-016-0776-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
|
23
|
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]
|
24
|
Assessment of nano-QSPR models of organic contaminant absorption by carbon nanotubes for ecological impact studies. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.md.2016.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
|
25
|
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]
|
26
|
Toropov AA, Toropova AP, Begum S, Achary PGR. Towards predicting the solubility of CO2 and N2 in different polymers using a quasi-SMILES based QSPR approach. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:293-301. [PMID: 27097272 DOI: 10.1080/1062936x.2016.1172666] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The solubility of gases in various polymers plays an important role for the design of new polymeric materials. Quantitative structure-property relationship (QSPR) models were designed to predict the solubility of gases such as CO2 and N2 in polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl acetate (PVA) and poly (butylene succinate) (PBS) at different temperatures and pressures by using quasi-SMILES codes. The dataset of 315 systems was split randomly into training, calibration and validation sets; random split 1 led to 214 training (r(2) = 0.870 and RMSE = 0.019), 51 calibration (r(2) = 0.858 and RMSE = 0.020) and 50 validation (r(2) = 0.869 and RMSE = 0.017) sets. The suggested approach based on the quasi-SMILES, which are analogues of the traditional SMILES gives reasonable good predictions for solubility of CO2 and N2 in different polymers. The described methodology is universal for situations where the aim is to predict the response of an eclectic system upon a variety of physicochemical and/or biochemical conditions.
Collapse
Affiliation(s)
- A A Toropov
- a IRCCS, Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - A P Toropova
- a IRCCS, Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - S Begum
- b Department of Chemistry , Siksha 'O' Anusandhan University , Bhubaneswar , Odisha , India
| | - P G R Achary
- b Department of Chemistry , Siksha 'O' Anusandhan University , Bhubaneswar , Odisha , India
| |
Collapse
|
27
|
Toropova AP, Schultz TW, Toropov AA. Building up a QSAR model for toxicity toward Tetrahymena pyriformis by the Monte Carlo method: A case of benzene derivatives. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2016; 42:135-145. [PMID: 26851376 DOI: 10.1016/j.etap.2016.01.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 01/12/2016] [Accepted: 01/14/2016] [Indexed: 06/05/2023]
Abstract
Data on toxicity toward Tetrahymena pyriformis is indicator of applicability of a substance in ecologic and pharmaceutical aspects. Quantitative structure-activity relationships (QSARs) between the molecular structure of benzene derivatives and toxicity toward T. pyriformis (expressed as the negative logarithms of the population growth inhibition dose, mmol/L) are established. The available data were randomly distributed three times into the visible training and calibration sets, and invisible validation sets. The statistical characteristics for the validation set are the following: r(2)=0.8179 and s=0.338 (first distribution); r(2)=0.8682 and s=0.341 (second distribution); r(2)=0.8435 and s=0.323 (third distribution). These models are built up using only information on the molecular structure: no data on physicochemical parameters, 3D features of the molecular structure and quantum mechanics descriptors are involved in the modeling process.
Collapse
Affiliation(s)
- Alla P Toropova
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy.
| | - Terry W Schultz
- College of Veterinary Medicine, The University of Tennessee, 2407 River Drive, Knoxville, TN 37996-4543, United States
| | - Andrey A Toropov
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy
| |
Collapse
|
28
|
Toropova AP, Toropov AA, Veselinović AM, Veselinović JB, Benfenati E, Leszczynska D, Leszczynski J. Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2016; 124:32-36. [PMID: 26452192 DOI: 10.1016/j.ecoenv.2015.09.038] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 09/18/2015] [Accepted: 09/25/2015] [Indexed: 06/05/2023]
Abstract
The experimental data on the bacterial reverse mutation test (under various conditions) on C60 nanoparticles for the cases (i) TA100, and (ii) WP2uvrA/pkM101 are examined as endpoints. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of these endpoints has been built up. The models are a mathematical function of eclectic data such as (i) dose (g/plate); (ii) metabolic activation (i.e. with mix S9 or without mix S9); and (iii) illumination (i.e. darkness or irradiation). The eclectic data on different conditions were represented by so-called quasi-SMILES. In contrast to the traditional SMILES which are representation of molecular structure, the quasi-SMILES are representation of conditions by sequence of symbols. The calculations were carried out with the CORAL software, available on the Internet at http://www.insilico.eu/coral. The main idea of the suggested descriptors is the accumulation of all available eclectic information in the role of logical and digital basis for building up a model. The computational experiments have shown that the described approach can be a tool to build up models of mutagenicity of fullerene under different conditions.
Collapse
Affiliation(s)
- Alla P Toropova
- IRCCS-I stituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy
| | - Andrey A Toropov
- IRCCS-I stituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy.
| | | | | | - Emilio Benfenati
- IRCCS-I stituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch St, Jackson, MS 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA
| |
Collapse
|
29
|
Messina PV, Besada-Porto JM, González-Díaz H, Ruso JM. Self-Assembled Binary Nanoscale Systems: Multioutput Model with LFER-Covariance Perturbation Theory and an Experimental-Computational Study of NaGDC-DDAB Micelles. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2015; 31:12009-12018. [PMID: 26484726 DOI: 10.1021/acs.langmuir.5b03074] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Studies of the self-aggregation of binary systems are of both theoretical and practical importance. They provide an opportunity to investigate the influence of the molecular structure of the hydrophobe on the nonideality of mixing. On the other hand, linear free energy relationship (LFER) models, such as Hansch's equations, may be used to predict the properties of chemical compounds such as drugs or surfactants. However, the task becomes more difficult once we want to predict simultaneaously the effect over multiple output properties of binary systems of perturbations under multiple input experimental boundary conditions (b(j)). As a consequence, we need computational chemistry or chemoinformatics models that may help us to predict different properties of the autoaggregation process of mixed surfactants under multiple conditions. In this work, we have developed the first model that combines perturbation theory (PT) and LFER ideas. The model uses as input covariance PT operators (CPTOs). CPTOs are calculated as the difference between covariance ΔCov((i)μ(k)) functions before and after multiple perturbations in the binary system. In turn, covariances calculated as the product of two Box-Jenkins operators (BJO) operators. BJOs are used to measure the deviation of the structure of different chemical compounds from a set of molecules measured under a given subset of experimental conditions. The best CPT-LFER model found predicted the effects of 25,000 perturbations over 9 different properties of binary systems. We also reported experimental studies of different experimental properties of the binary system formed by sodium glycodeoxycholate and didodecyldimethylammonium bromide (NaGDC-DDAB). Last, we used our CPT-LFER model to carry out a 1000 data point simulation of the properties of the NaGDC-DDAB system under different conditions not studied experimentally.
Collapse
Affiliation(s)
- Paula V Messina
- Department of Chemistry, INQUISUR-CONICET, Universidad Nacional del Sur , 8000 Bahía Blanca, Argentina
| | - Jose Miguel Besada-Porto
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela , Santiago de Compostela E-15782, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, Faculty of Science and Technology, University of the Basque Country UPV/EHU , 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
| | - Juan M Ruso
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela , Santiago de Compostela E-15782, Spain
| |
Collapse
|
30
|
Cappelli CI, Benfenati E, Cester J. Evaluation of QSAR models for predicting the partition coefficient (log P) of chemicals under the REACH regulation. ENVIRONMENTAL RESEARCH 2015; 143:26-32. [PMID: 26432472 DOI: 10.1016/j.envres.2015.09.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Revised: 09/18/2015] [Accepted: 09/21/2015] [Indexed: 06/05/2023]
Abstract
The partition coefficient (log P) is a physicochemical parameter widely used in environmental and health sciences and is important in REACH and CLP regulations. In this regulatory context, the number of existing experimental data on log P is negligible compared to the number of chemicals for which it is necessary. There are many models to predict log P and we have selected a number of free programs to examine how they predict the log P of chemicals registered for REACH and to evaluate wheter they can be used in place of experimental data. Some results are good, especially if the information on the applicability domain of the models is considered, with R(2) values from 0.7 to 0.8 and root mean square error (RMSE) from 0.8 to 1.5.
Collapse
Affiliation(s)
- Claudia Ileana Cappelli
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Milan, Italy.
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Milan, Italy.
| | - Josep Cester
- Departament d'Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, Catalunya, Tarragona 43007, Spain.
| |
Collapse
|
31
|
Prediction of retention characteristics of heterocyclic compounds. Anal Bioanal Chem 2015; 407:9185-9. [DOI: 10.1007/s00216-015-9067-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 09/18/2015] [Indexed: 01/30/2023]
|
32
|
Živković JV, Trutić NV, Veselinović JB, Nikolić GM, Veselinović AM. Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors. Comput Biol Med 2015; 64:276-82. [DOI: 10.1016/j.compbiomed.2015.07.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Revised: 06/28/2015] [Accepted: 07/07/2015] [Indexed: 12/23/2022]
|
33
|
Toropova A, Toropov A, Benfenati E. CORAL: Prediction of binding affinity and efficacy of thyroid hormone receptor ligands. Eur J Med Chem 2015; 101:452-61. [DOI: 10.1016/j.ejmech.2015.07.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 07/01/2015] [Accepted: 07/06/2015] [Indexed: 12/19/2022]
|
34
|
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]
|
35
|
Toropov AA, Toropova AP, Pizzo F, Lombardo A, Gadaleta D, Benfenati E. CORAL: model for no observed adverse effect level (NOAEL). Mol Divers 2015; 19:563-75. [PMID: 25850638 PMCID: PMC4487358 DOI: 10.1007/s11030-015-9587-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Accepted: 03/21/2015] [Indexed: 02/07/2023]
Abstract
The in vivo repeated dose toxicity (RDT) test is intended to provide information on the possible risk caused by repeated exposure to a substance over a limited period of time. The measure of the RDT is the no observed adverse effect level (NOAEL) that is the dose at which no effects are observed, i.e., this endpoint indicates the safety level for a substance. The need to replace in vivo tests, as required by some European Regulations (registration, evaluation authorization and restriction of chemicals) is leading to the searching for reliable alternative methods such as quantitative structure-activity relationships (QSAR). Considering the complexity of the RDT endpoint, for which data quality is limited and depends anyway on the study design, the development of QSAR for this endpoint is an attractive task. Starting from a dataset of 140 organic compounds with NOAEL values related to oral short term toxicity in rats, we developed a QSAR model based on optimal descriptors calculated with simplified molecular input-line entry systems and the graph of atomic orbitals by the Monte Carlo method, using CORAL software. Three different splits into the training, calibration, and validation sets are studied. The mechanistic interpretation of these models in terms of molecular fragment with positive or negative contributions to the endpoint is discussed. The probabilistic definition for the domain of applicability is suggested.
Collapse
Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20159, Milan, Italy,
| | | | | | | | | | | |
Collapse
|
36
|
QSPR models for estimating retention in HPLC with the p solute polarity parameter based on the Monte Carlo method. Struct Chem 2015. [DOI: 10.1007/s11224-015-0636-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
|
37
|
Worachartcheewan A, Prachayasittikul V, Toropova AP, Toropov AA, Nantasenamat C. Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors. Mol Divers 2015; 19:955-64. [DOI: 10.1007/s11030-015-9614-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
|
38
|
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.
Collapse
Affiliation(s)
- Alla P Toropova
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milano, Italy
| | | | | | | |
Collapse
|
39
|
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.
Collapse
Affiliation(s)
- S Begum
- a Department of Chemistry , Institute of Technical Education and Research (ITER), Siksha 'O' Anusandhan University , Bhubaneswar , Odisha - 751030 , India
| | | |
Collapse
|
40
|
Toropova AP, Toropov AA, Benfenati E, Leszczynska D, Leszczynski J. QSAR model as a random event: A case of rat toxicity. Bioorg Med Chem 2015; 23:1223-30. [DOI: 10.1016/j.bmc.2015.01.055] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Revised: 01/29/2015] [Accepted: 01/30/2015] [Indexed: 01/12/2023]
|
41
|
Toropova AP, Toropov AA, Rallo R, Leszczynska D, Leszczynski J. Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2015; 112:39-45. [PMID: 25463851 DOI: 10.1016/j.ecoenv.2014.10.003] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Revised: 10/01/2014] [Accepted: 10/03/2014] [Indexed: 06/04/2023]
Abstract
The Monte Carlo technique has been used to build up quantitative structure-activity relationships (QSARs) for prediction of dark cytotoxicity and photo-induced cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (minus logarithm of lethal concentration for 50% bacteria pLC50, LC50 in mol/L). The representation of nanoparticles include (i) in the case of the dark cytotoxicity a simplified molecular input-line entry system (SMILES), and (ii) in the case of photo-induced cytotoxicity a SMILES plus symbol '^'. The predictability of the approach is checked up with six random distributions of available data into the visible training and calibration sets, and invisible validation set. The statistical characteristics of these models are correlation coefficient 0.90-0.94 (training set) and 0.73-0.98 (validation set).
Collapse
Affiliation(s)
- Alla P Toropova
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy
| | - Andrey A Toropov
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy.
| | - Robert Rallo
- Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Av. Països Catalans, 26, 43007 Tarragona, Catalunya, Spain
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 JR Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA
| |
Collapse
|
42
|
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.
Collapse
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
| | | | | | | |
Collapse
|
43
|
Toropova AP, Toropov AA, Benfenati E, Korenstein R, Leszczynska D, Leszczynski J. Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2015; 22:745-757. [PMID: 25223357 DOI: 10.1007/s11356-014-3566-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Accepted: 09/03/2014] [Indexed: 06/03/2023]
Abstract
Systematization of knowledge on nanomaterials has become a necessity with the fast growth of applications of these species. Building up predictive models that describe properties (both beneficial and hazardous) of nanomaterials is vital for computational sciences. Classic quantitative structure-property/activity relationships (QSPR/QSAR) are not suitable for investigating nanomaterials because of the complexity of their molecular architecture. However, some characteristics such as size, concentration, and exposure time can influence endpoints (beneficial or hazardous) related to nanoparticles and they can therefore be involved in building a model. Application of the optimal descriptors calculated with the so-called correlation weights of various concentrations and different exposure times are suggested in order to build up a predictive model for cell membrane damage caused by a series of nano metal-oxides. The numerical data on correlation weights are calculated by the Monte Carlo method. The obtained results are in good agreement with the experimental data.
Collapse
Affiliation(s)
- Alla P Toropova
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milan, Italy
| | | | | | | | | | | |
Collapse
|
44
|
Toropova AP, Toropov AA, Benfenati E, Puzyn T, Leszczynska D, Leszczynski J. Optimal descriptor as a translator of eclectic information into the prediction of membrane damage: the case of a group of ZnO and TiO2 nanoparticles. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2014; 108:203-209. [PMID: 25086232 DOI: 10.1016/j.ecoenv.2014.07.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Revised: 07/03/2014] [Accepted: 07/04/2014] [Indexed: 06/03/2023]
Abstract
The development of quantitative structure-activity relationships for nanomaterials needs representation of molecular structure of extremely complex molecular systems. Obviously, various characteristics of nanomaterial could impact associated biochemical endpoints. Following features of TiO2 and ZnO nanoparticles (n=42) are considered here: (i) engineered size (nm); (ii) size in water suspension (nm); (iii) size in phosphate buffered saline (PBS, nm); (iv) concentration (mg/L); and (v) zeta potential (mV). The damage to cellular membranes (units/L) is selected as an endpoint. Quantitative features-activity relationships (QFARs) are calculated by the Monte Carlo technique for three distributions of data representing values associated with membrane damage into the training and validation sets. The obtained models are characterized by the following average statistics: 0.78<r(2)training<0.92 and 0.67<r(2)validation<0.83.
Collapse
Affiliation(s)
- Alla P Toropova
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milano, Italy
| | - Andrey A Toropov
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milano, Italy.
| | - Emilio Benfenati
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milano, Italy
| | - Tomasz Puzyn
- Faculty of Chemistry, Laboratory of Environmental Chemometrics, University of Gdansk, ul. Sobieskiego 18/19, Gdansk 80-952, Poland
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch St, Jackson, MS 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, PO Box 17910, Jackson, MS 39217, USA
| |
Collapse
|
45
|
Karthick V, Toropova AP, Toropov AA, Ramanathan K. Discovery of Potential, Non-Toxic Influenza Virus Inhibitor by Computational Techniques. Mol Inform 2014; 33:559-65. [PMID: 27486041 DOI: 10.1002/minf.201400041] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2014] [Accepted: 06/19/2014] [Indexed: 11/09/2022]
Abstract
Influenza infection continues to be a major problem in many parts of the world. Rimantadine is a first-line drug used to treat the influenza infection by targeting M2 proton channel. However, S31N mutation in M2 proton channel diminishes the efficiency of rimantadine and creates resistance. To address this issue, the present study was aimed to screen the effective lead candidate against drug resistance strain of influenza from DrugBank database. Initially, the lead molecules were filtered using Lipinski rule of five and the drug likeliness property. Subsequently, the data reduction was carried out by employing molecular docking study. Finally, molecular dynamics simulations techniques were performed to validate the lead compound. Most importantly, the -p LD50 of the screened lead molecule was calculated using CORAL software to estimate the Rat oral toxicity. Accordingly, memantine may possibly become a promising lead compound of rimantadine-resistant influenza virus strain.
Collapse
Affiliation(s)
- V Karthick
- Industrial Biotechnology Division, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India tel: +91 9486667687; fax: +91 4162243092
| | - Alla P Toropova
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milano, Italy
| | - Andrey A Toropov
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milano, Italy
| | - K Ramanathan
- Industrial Biotechnology Division, School of Bio Sciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India tel: +91 9486667687; fax: +91 4162243092.
| |
Collapse
|
46
|
Deng F, Xie M, Zhang X, Li P, Tian Y, Zhai H, Li Y. Combined molecular docking, molecular dynamics simulation and quantitative structure–activity relationship study of pyrimido[1,2-c][1,3]benzothiazin-6-imine derivatives as potent anti-HIV drugs. J Mol Struct 2014. [DOI: 10.1016/j.molstruc.2014.03.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
|
47
|
Toropov AA, Toropova AP. Optimal descriptor as a translator of eclectic data into endpoint prediction: mutagenicity of fullerene as a mathematical function of conditions. CHEMOSPHERE 2014; 104:262-264. [PMID: 24246220 DOI: 10.1016/j.chemosphere.2013.10.079] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Revised: 10/21/2013] [Accepted: 10/26/2013] [Indexed: 06/02/2023]
Abstract
The experimental data on the bacterial reverse mutation test on C60 nanoparticles (TA100) is examined as an endpoint. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of the endpoint has been built up. The model is the mathematical function of (i) dose (g/plate); (ii) metabolic activation (i.e. with S9 mix or without S9 mix); and (iii) illumination (i.e. dark or irradiation). The statistical quality of the model is the following: n=10, r(2)=0.7549, q(2)=0.5709, s=7.67, F=25 (Training set); n=5, r(2)=0.8987, s=18.4 (Calibration set); and n=5, r(2)=0.6968, s=10.9 (Validation set).
Collapse
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
| |
Collapse
|
48
|
Achary PGR. QSPR modelling of dielectric constants of π-conjugated organic compounds by means of the CORAL software. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2014; 25:507-526. [PMID: 24716837 DOI: 10.1080/1062936x.2014.899267] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Simplified molecular input line entry system (SMILES) notations of 116 π-conjugated organic compounds have been used in three random splits to develop single optimal descriptor based quantitative structure-property relationships (QSPR) models for the prediction of dielectric constants by CORAL (CORrelation And Logic). Four kinds of optimal descriptors were obtained based on SMILES, hydrogen suppressed graph (HSG), graph of atomic orbitals (GAO) and hybrid descriptors. The Monte Carlo optimization was carried out for each random split by three different methods: (i) classic scheme; (ii) balance of correlations; and (iii) balance of correlations with ideal slopes. The QSPR models gave reliable and accurate values of dielectric constant for all the π-conjugated organic compounds. SMILES and the hybrid-based QSPR model provided the best accuracy for the prediction of dielectric constants. Statistical characteristics of the QSPR model-1 based on classic scheme method are n = 110, r(2) = 0.860, Q(2) = 0.860, s = 1.84, MAE = 1.30 and F = 696 (training set), n = 6, r(2) = 0.947, Q(2) = 0.876, s = 0.955, MAE = 0.647 and F = 71 (test set). These QSPR models are further validated by an external validation set of 25 molecules and the robustness is checked by parameters like k, kk, rm(2), r(*)m(2), average rm(2), ∆rm(2)and randomization technique ([Formula: see text]).
Collapse
Affiliation(s)
- P G R Achary
- a Department of Chemistry , Institute of Technical Education and Research (ITER), Siksha 'O' Anusandhan University , Bhubaneswar , Odisha , India
| |
Collapse
|
49
|
Toropova AP, Toropov AA. CORAL software: Prediction of carcinogenicity of drugs by means of the Monte Carlo method. Eur J Pharm Sci 2014; 52:21-5. [DOI: 10.1016/j.ejps.2013.10.005] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2013] [Revised: 10/01/2013] [Accepted: 10/12/2013] [Indexed: 12/13/2022]
|
50
|
Vassiliev PM, Spasov AA, Kosolapov VA, Kucheryavenko AF, Gurova NA, Anisimova VA. Consensus Drug Design Using IT Microcosm. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2014. [DOI: 10.1007/978-94-017-9257-8_12] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
|