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Garro LA, Andrada MF, Vega-Hissi EG, Barberis S, Garro Martinez JC. Development of QSARs for cysteine-containing di- and tripeptides with antioxidant activity:influence of the cysteine position. J Comput Aided Mol Des 2024; 38:27. [PMID: 39093524 DOI: 10.1007/s10822-024-00567-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 07/22/2024] [Indexed: 08/04/2024]
Abstract
Antioxidants agents play an essential role in the food industry for improving the oxidative stability of food products. In the last years, the search for new natural antioxidants has increased due to the potential high toxicity of chemical additives. Therefore, the synthesis and evaluation of the antioxidant activity in peptides is a field of current research. In this study, we performed a Quantitative Structure Activity Relationship analysis (QSAR) of cysteine-containing 19 dipeptides and 19 tripeptides. The main objective is to bring information on the relationship between the structure of peptides and their antioxidant activity. For this purpose, 1D and 2D molecular descriptors were calculated using the PaDEL software, which provides information about the structure, shape, size, charge, polarity, solubility and other aspects of the compounds. Different QSAR model for di- and tripeptides were developed. The statistic parameters for di-peptides model (R2train = 0.947 and R2test = 0.804) and for tripeptide models (R2train = 0.923 and R2test = 0.847) indicate that the generated models have high predictive capacity. Then, the influence of the cysteine position was analyzed predicting the antioxidant activity for new di- and tripeptides, and comparing them with glutathione. In dipeptides, excepting SC, TC and VC, the activity increases when cysteine is at the N-terminal position. For tripeptides, we observed a notable increase in activity when cysteine is placed in the N-terminal position.
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Affiliation(s)
- Lucas A Garro
- Instituto de Física Aplicada (INFAP), CCT - San Luis, CONICET, Universidad Nacional de San Luis, San Luis, 5700, Argentina
| | - Matias F Andrada
- Instituto de Física Aplicada (INFAP), CCT - San Luis, CONICET, Universidad Nacional de San Luis, San Luis, 5700, Argentina
| | - Esteban G Vega-Hissi
- Facultad de Química, Bioquímica y Farmacia, IMIBIO-SL-CONICET, Universidad Nacional de San Luis, San Luis, 5700, Argentina
| | - Sonia Barberis
- Instituto de Física Aplicada (INFAP), CCT - San Luis, CONICET, Universidad Nacional de San Luis, San Luis, 5700, Argentina
| | - Juan C Garro Martinez
- Facultad de Química, Bioquímica y Farmacia, IMIBIO-SL-CONICET, Universidad Nacional de San Luis, San Luis, 5700, Argentina.
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2
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Chemometrics-assisted inductively coupled plasma-optical emission spectrometry method for determination of natural zinc isotopes. J Radioanal Nucl Chem 2023. [DOI: 10.1007/s10967-022-08756-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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3
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In Silico Antiprotozoal Evaluation of 1,4-Naphthoquinone Derivatives against Chagas and Leishmaniasis Diseases Using QSAR, Molecular Docking, and ADME Approaches. Pharmaceuticals (Basel) 2022; 15:ph15060687. [PMID: 35745607 PMCID: PMC9228275 DOI: 10.3390/ph15060687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/24/2022] [Accepted: 05/27/2022] [Indexed: 12/04/2022] Open
Abstract
Chagas and leishmaniasis are two neglected diseases considered as public health problems worldwide, for which there is no effective, low-cost, and low-toxicity treatment for the host. Naphthoquinones are ligands with redox properties involved in oxidative biological processes with a wide variety of activities, including antiparasitic. In this work, in silico methods of quantitative structure–activity relationship (QSAR), molecular docking, and calculation of ADME (absorption, distribution, metabolism, and excretion) properties were used to evaluate naphthoquinone derivatives with unknown antiprotozoal activity. QSAR models were developed for predicting antiparasitic activity against Trypanosoma cruzi, Leishmania amazonensis, and Leishmania infatum, as well as the QSAR model for toxicity activity. Most of the evaluated ligands presented high antiparasitic activity. According to the docking results, the family of triazole derivatives presented the best affinity with the different macromolecular targets. The ADME results showed that most of the evaluated compounds present adequate conditions to be administered orally. Naphthoquinone derivatives show good biological activity results, depending on the substituents attached to the quinone ring, and perhaps the potential to be converted into drugs or starting molecules.
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Hajihosseinloo A, Salahinejad M, Rofouei MK, Ghasemi JB. Exploratory and machine learning analysis of the stability constants of HgII- triazene ligands complexes. MAIN GROUP CHEMISTRY 2021. [DOI: 10.3233/mgc-210130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Knowing stability constants for the complexes HgII with extracting ligands is very important from environmental and therapeutic standpoints. Since the selectivity of ligands can be stated by the stability constants of cation–ligand complexes, quantitative structure–property relationship (QSPR) investigations on binding constant of HgII complexes were done. Experimental data of the stability constants in ML2 complexation of HgII and synthesized triazene ligands were used to construct and develop QSPR models. Support vector machine (SVM) and multiple linear regression (MLR) have been employed to create the QSPR models. The final model showed squared correlation coefficient of 0.917 and the standard error of calibration (SEC) value of 0.141 log K units. The proposed model presented accurate prediction with the Leave-One-Out cross validation ( Q LOO 2 = 0.756) and validated using Y-randomization and external test set. Statistical results demonstrated that the proposed models had suitable goodness of fit, predictive ability, and robustness. The results revealed the importance of charge effects and topological properties of ligand in HgII - triazene complexation.
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Affiliation(s)
| | - Maryam Salahinejad
- Maryam Salahinejad, Nuclear Science and Technology Research Institute, Tehran, Iran
| | | | - Jahan B. Ghasemi
- Department of Chemistry, Faculty of Science, University of Tehran, Tehran, Iran
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Saavedra LM, Duchowicz PR. Predicting zebrafish (Danio rerio) embryo developmental toxicity through a non-conformational QSAR approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 796:148820. [PMID: 34328907 DOI: 10.1016/j.scitotenv.2021.148820] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 06/11/2021] [Accepted: 06/29/2021] [Indexed: 06/13/2023]
Abstract
For many years, the frequent use of synthetic chemicals in the manufacture of veterinary drugs and plague control products has raised negative effects on human health and other non-target organisms, promoting the need to employ a practical and suitable methodology for early risk identification of several thousand commercial compounds. The zebrafish (Danio rerio) embryo has been emerged as one sustainable animal model for measuring developmental toxicity, an endpoint that is included in the regulatory procedures to approve chemicals, avoiding conventional and costly toxicity assays based on animal testing. In this context, the Quantitative Structure-Activity Relationships (QSAR) theory is applied to develop a predictive model based on a well-defined zebrafish embryo developmental toxicity database reported by the ToxCast™ Phase I chemical library of the Environmental Protection Agency (U.S. EPA). By means of four freely available softwares, a set with 28,038 non-conformational descriptors that encode the largest amount of permanent structural features are readily calculated. The Replacement Method (RM) variable subset selection technique provided the best regression models. Thereby, a linear QSAR model with proper statistical quality (Rtrain2 = 0.64, RMSEtrain = 0.49) is established in agreement with the Organization for Economic Co-operation and Development principles, accomplishing each internal (loo, l15 % o, VIF and Y-randomization) and external (Rtest2,Rm2, QF12, QF22, QF32 and CCC) validation criterion. The present QSAR approach provides a useful computational tool to estimate zebrafish developmental toxicity of new, untasted or hypothetical compounds, and it can contribute to the general lack of QSAR models in the literature to predict this endpoint.
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Affiliation(s)
- Laura M Saavedra
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113 y 64, C.C. 16, Sucursal 4, 1900 La Plata, Argentina.
| | - Pablo R Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113 y 64, C.C. 16, Sucursal 4, 1900 La Plata, Argentina.
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6
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Salahinejad M, Sadjadi S, Abdouss M. Investigating fluorescence quenching of cysteine-functionalized carbon quantum dots by heavy metal ions: Experimental and QSPR studies. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116067] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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7
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Nossa González DL, Gómez Castaño JA, Rozo Núñez WE, Duchowicz PR. Antiprotozoal QSAR modelling for trypanosomiasis (Chagas disease) based on thiosemicarbazone and thiazole derivatives. J Mol Graph Model 2020; 103:107821. [PMID: 33333422 DOI: 10.1016/j.jmgm.2020.107821] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 10/09/2020] [Accepted: 12/03/2020] [Indexed: 01/19/2023]
Abstract
Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, remains a neglected endemic infection that affects around 8 million people worldwide and causes 12,000 premature deaths per year. Traditional chemotherapy is limited to the nitro-antiparasitic drugs Benznidazole and Nifurtimox, which present serious side effects and low long-term efficacy. Several research efforts have been made over the last decade to find new chemical structures with better effectiveness and tolerance than standard anti-Chagas drugs. Among these, new sets of thiosemicarbazone and thiazole derivatives have exhibited potent in vitro activity against T. cruzi, especially for its extracellular forms (epimastigote and trypomastigote). In this work, we have developed three antiprotozoal quantitative structure-relationship (QSAR) models for Chagas disease based on the in vitro activity data reported as IC50 (μM) and CC50 (μM) over the last decade, particularly by Lima-Leite's group in Brazil. The models were developed using the replacement method (RM), a technique based on Multivariable Linear Regression (MLR), and external and internal validation methodologies, like the use of a test set, Leave-one-Out (LOO) cross-validation and Y-Randomization. Two of these QSAR models were developed for trypomastigotes form of the parasite Trypanosoma cruzi, one based on IC50 and the other on CC50 data; while the third QSAR model was developed for its epimastigotes form based on CC50 activity. Our models presented sound statistical parameters that endorses their prediction capability. Such capability was tested for a set of 13 hitherto-unknown structurally related aromatic cyclohexanone derivatives.
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Affiliation(s)
- Diana L Nossa González
- Grupo Química-Física Molecular y Modelamiento Computacional (QUIMOL), Facultad de Ciencias, Universidad Pedagógica y Tecnológica de Colombia, Avenida Central Del Norte, Tunja, Boyacá, Colombia.
| | - Jovanny A Gómez Castaño
- Grupo Química-Física Molecular y Modelamiento Computacional (QUIMOL), Facultad de Ciencias, Universidad Pedagógica y Tecnológica de Colombia, Avenida Central Del Norte, Tunja, Boyacá, Colombia.
| | - Wilson E Rozo Núñez
- Grupo Química-Física Molecular y Modelamiento Computacional (QUIMOL), Facultad de Ciencias, Universidad Pedagógica y Tecnológica de Colombia, Avenida Central Del Norte, Tunja, Boyacá, Colombia
| | - Pablo R Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (CONICET- Universidad Nacional de La Plata), Diagonal 113 y calle 64, C.C. 16, Sucursal 4, 1900, La Plata, Provincia de Buenos Aires, Argentina.
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8
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Peng D, Picchioni F. Prediction of toxicity of Ionic Liquids based on GC-COSMO method. JOURNAL OF HAZARDOUS MATERIALS 2020; 398:122964. [PMID: 32768829 DOI: 10.1016/j.jhazmat.2020.122964] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 04/22/2020] [Accepted: 05/14/2020] [Indexed: 06/11/2023]
Abstract
In order to evaluate the toxicity of several different ionic liquids (ILs) towards the leukemia rat cell line (ICP-81), an efficient and reliable quantitative structure-activity relationships (QSAR) model is developed based on descriptors from COSMO-SAC (conductor-like screening model for segment activity coefficient) model. The distribution of screen charge density (σ-profile) of 127 ILs is calculated by GC-COSMO (group contribution based COSMO) method. Two segmentation methods toward σ-profile are used to find out the appropriate descriptors for the QSAR model. The optimal subset of descriptors is obtained by enhanced replacement method (ERM). A multiple linear regression (MLR) and multilayer perceptron technique (MLP) are used to build the linear and nonlinear models, respectively, and the applicability domain of the models is assessed by the Williams plot. It turns out that the nonlinear model based the second segmentation method (MLP-2) is the best QSAR model with an R2=0.975, MSE=0.019 for the training set and R2=0.938, MSE=0.037 for the test set. The reliability and robustness of the presented QSAR models are confirmed by Leave-One-Out (LOO) cross and external validations.
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Affiliation(s)
- Daili Peng
- University of Groningen, Faculty of Science and Engineering, Product Technology - Engineering and Technology Institute Groningen, Nijenborgh 4, 9747 AG Groningen, the Netherlands
| | - Francesco Picchioni
- University of Groningen, Faculty of Science and Engineering, Product Technology - Engineering and Technology Institute Groningen, Nijenborgh 4, 9747 AG Groningen, the Netherlands.
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Duan W, Pan Y, He H, Zhao S, Zhao X, Jiang J, Shu CM. Prediction of the thermal decomposition temperatures of imidazolium ILs based on norm indexes. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2020.113780] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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10
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Yu S, Jia S, Wang D, Lv Z, Chen Y, Wang N, Yao W, Yuan J. Predicting pungency and understanding the pungency mechanism of capsaicinoids using TOPS-MODE approach. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:527-545. [PMID: 32573260 DOI: 10.1080/1062936x.2020.1777583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 05/31/2020] [Indexed: 06/11/2023]
Abstract
Quantitative structure-property relationship (QSPR) models were developed for predicting the pungency of a set of capsaicinoids. Multiple linear regression (MLR) coupled with topological substructural molecular descriptor (TOPS-MODE) approach was used. The best MLR model based on only five orthogonalized TOPS-MODE variables allowed us to obtain a coefficient of determination of 0.954 on the training set. The predictive power of the model was validated through a test set and several external validation parameters. This showed that the TOPS-MODE descriptors weighted by bond dipole moments, van der Waals atomic radii, and the total solute hydrogen bond basicity affected pungency. The contributions of certain bonds and fragments to pungency were used to understand the pungency mechanism of capsaicinoids. The selected model can more accurately predict pungency of capsaicinoids compared than those found in the literature, and especially bring insights into the structural features and chemical factors related to pungency.
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Affiliation(s)
- S Yu
- Key Laboratory of Natural Medicine and Immune-Engineering of Henan Province, Henan University , Kaifeng, China
| | - S Jia
- Key Laboratory of Natural Medicine and Immune-Engineering of Henan Province, Henan University , Kaifeng, China
| | - D Wang
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
| | - Z Lv
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
| | - Y Chen
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
| | - N Wang
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
| | - W Yao
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
| | - J Yuan
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
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11
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Ligand Design for Asymmetric Catalysis: Combining Mechanistic and Chemoinformatics Approaches. TOP ORGANOMETAL CHEM 2020. [DOI: 10.1007/3418_2020_47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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12
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QSAR studies of the antioxidant activity of anthocyanins. Journal of Food Science and Technology 2019; 56:5518-5530. [PMID: 31749500 DOI: 10.1007/s13197-019-04024-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 08/08/2019] [Accepted: 08/12/2019] [Indexed: 12/22/2022]
Abstract
Through experimental information available from antioxidant assays of seventeen anthocyanins, and six common anthocyanidins, quantitative structure-activity relationships (QSAR) have been established in the present work. The antioxidant bioactivity has been predicted in three different lipid environments: emulsified and bulk oil (methyl linoleate) (in vitro tests) at concentrations of 50 and 250 μM, and 50 μM of the inhibitor, respectively, and in human LDL (low-density lipoprotein; "bad cholesterol") (ex vivo test) at concentrations of 2.5, 10, and 25 μM of the inhibitor. Radical scavenging activity was predicted in the assay with the 1,1-diphenyl-2-picrylhydrazyl radical (DPPH·). The QSAR models developed for each test and concentration used allowed to obtain prospective information on the constitutional and topological molecular characteristics for anthocyanin/anthocyanidin compounds. Therefore, the antioxidant activity was predicted for twenty-one compounds with unknown experimental values, leading for some of them to a favorable predicted bioactivity.
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Salahinejad M, Zolfonoun E. An exploratory study using QICAR models for prediction of adsorption capacity of multi-walled carbon nanotubes for heavy metal ions. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2018; 29:997-1009. [PMID: 30411640 DOI: 10.1080/1062936x.2018.1538059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2018] [Indexed: 06/08/2023]
Abstract
The Quantitative Ion Character-Activity Relationship (QICAR) method was used for correlating metal ionic characteristics with the maximum adsorption capacity (qmax) of multi-walled carbon for heavy metals. The experimental values of qmax for 25 heavy metal ions, estimated by the Langmuir isotherm model, were used to construct a QICAR model. The genetic algorithm, enhanced replacement method and successive projection algorithm procedures were applied as variable selection algorithms to choose the optimal subsets of descriptors. The selected variables were correlated with qmax values by using partial least squares (PLS) regression. Orthogonal signal correction was applied as a pre-processing technique. Among of different variable selection methods, the enhanced replacement method displayed noticeable statistical parameters of the final model. The results of the enhancement replacement method-orthogonal correction signal-PLS model, with RMSEC = 0.733, r2c = 0.999 and r2p = 0.946, were excellent and dramatically better than those of other models. The developed QICAR model satisfied the internal and external validation criteria. The importance of electronegativity, ionic radius and atomic number of the heavy metal ions indicated the impact of the tendency to accept electrons and the size of ions in adsorption on carbon nanotubes.
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Affiliation(s)
- M Salahinejad
- a Materials and Nuclear Fuel Research School , Nuclear Science and Technology Research Institute , Tehran , Iran
| | - E Zolfonoun
- b Applied Radiation Research School , Nuclear Science and Technology Research Institute , Tehran , Iran
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Yépes AF, Bahsas A, Escobar P, Cobo J, Palma A, Garro Martinez JC, Enriz R. Synthesis, anti-parasitic activity and QSAR study of a new library of polysubstituted tetrahydronaphtho[1,2-b]azepines. Med Chem Res 2018. [DOI: 10.1007/s00044-018-2232-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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15
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Shiri F, Pirhadi S, Rahmani A. Identification of new potential HIV-1 reverse transcriptase inhibitors by QSAR modeling and structure-based virtual screening. J Recept Signal Transduct Res 2017; 38:37-47. [PMID: 29254400 DOI: 10.1080/10799893.2017.1414844] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity and low toxicity in antiretroviral combination therapies which are used to treat HIV. To design more specific HIV-1 inhibitors, 218 diverse non-nucleoside reverse transcriptase inhibitors with their EC50 values were collected. Then, different types of molecular descriptors were calculated. Also, genetic algorithm (GA) and enhanced replacement methods (ERM) were used as the variable selection approaches to choose more relevant features. Based on selected descriptors, a classification support vector machine (SVM) model was constructed to categorize compounds into two groups of active and inactive ones. The most active compound in the set was docked and was used as the input to the Pharmit server to screen the Molport and PubChem libraries by constructing a structure-based pharmacophore model. Shape filters for the protein and ligand as well as Lipinski's rule of five have been applied to filter out the output of virtual screening from pharmacophore search. Three hundred and thirty-four compounds were finally retrieved from the virtual screening and were fed to the previously constructed SVM model. Among them, the SVM model rendered seven active compounds and they were also analyzed by docking calculations and ADME/Tox parameters.
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Affiliation(s)
- Fereshteh Shiri
- a Department of Chemistry , University of Zabol , Zabol , Iran
| | - Somayeh Pirhadi
- b Medicinal and Natural Products Chemistry Research Center , Shiraz University of Medical Sciences , Shiraz , Iran
| | - Azita Rahmani
- a Department of Chemistry , University of Zabol , Zabol , Iran
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Andrada MF, Vega-Hissi EG, Estrada MR, Garro Martinez JC. Impact assessment of the rational selection of training and test sets on the predictive ability of QSAR models. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2017; 28:1011-1023. [PMID: 29135323 DOI: 10.1080/1062936x.2017.1397056] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Accepted: 10/22/2017] [Indexed: 06/07/2023]
Abstract
This study performed an analysis of the influence of the training and test set rational selection on the quality and predictively of the quantitative structure-activity relationship (QSAR) model. The study was carried out on three different datasets of Influenza Neuraminidase (H1N1) inhibitors. The three datasets were divided into training and test sets using three rational selection methods: based on k-means, Kennard-Stone algorithm and Activity and the results were compared with Random selection. Then, a total of 31,490 mathematical models were developed and those models that presented a determination coefficient higher than: r2train > 0.8, r2loo > 0.7, r2test > 0.5 and minimum standard deviation (SD) and minimum root-mean square error (RMS) were selected. The selected models were validated using the internal leave-one-out method and the predictive capacity was evaluated by the external test set. The results indicate that random selection could lead to erroneous results. In return, a rational selection allows for obtaining more reliable conclusions. The QSAR models with major predictive power were found using the k-means algorithm and selection by activity.
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Affiliation(s)
- M F Andrada
- a Facultad de Química, Bioquímica y Farmacia , Universidad Nacional de San Luis , San Luis , Argentina
| | - E G Vega-Hissi
- b IMIBIO, CONICET. Facultad de Química, Bioquímica y Farmacia , Universidad Nacional de San Luis , San Luis , Argentina
| | - M R Estrada
- a Facultad de Química, Bioquímica y Farmacia , Universidad Nacional de San Luis , San Luis , Argentina
| | - J C Garro Martinez
- b IMIBIO, CONICET. Facultad de Química, Bioquímica y Farmacia , Universidad Nacional de San Luis , San Luis , Argentina
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17
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Paneth A, Płonka W, Paneth P. What do docking and QSAR tell us about the design of HIV-1 reverse transcriptase nonnucleoside inhibitors? J Mol Model 2017; 23:317. [PMID: 29046967 PMCID: PMC5655543 DOI: 10.1007/s00894-017-3489-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 09/25/2017] [Indexed: 11/30/2022]
Abstract
Despite vigorous studies, effective nonnucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) are still in demand, not only due to toxicity and detrimental side effects of currently used drugs but also because of the emergence of multidrug-resistant viral strains. In this contribution, we present results of docking of 47 inhibitors to 107 allosteric centers of HIV-1 reverse transcriptase. Based on the average binding scores, we have constructed QSAR equations to elucidate directions of further developments in the inhibitor design that come from this structural data.
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Affiliation(s)
- Agata Paneth
- Institute of Applied Radiation Chemistry, Lodz University of Technology, Żeromskiego 116, 90-924, Łódź, Poland
- Faculty of Pharmacy, Medical University of Lublin, Chodźki 4a, 20-093, Lublin, Poland
| | | | - Piotr Paneth
- Institute of Applied Radiation Chemistry, Lodz University of Technology, Żeromskiego 116, 90-924, Łódź, Poland.
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18
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Kittelmann J, Lang KM, Ottens M, Hubbuch J. Orientation of monoclonal antibodies in ion-exchange chromatography: A predictive quantitative structure–activity relationship modeling approach. J Chromatogr A 2017; 1510:33-39. [DOI: 10.1016/j.chroma.2017.06.047] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Revised: 06/05/2017] [Accepted: 06/15/2017] [Indexed: 11/16/2022]
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19
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Mercader AG, Bacelo DE, Duchowicz PR. Different encoding alternatives for the prediction of halogenated polymers glass transition temperature by quantitative structure–property relationships. INTERNATIONAL JOURNAL OF POLYMER ANALYSIS AND CHARACTERIZATION 2017. [DOI: 10.1080/1023666x.2017.1358847] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Andrew G. Mercader
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas, CCT La Plata-CONICET, UNLP, La Plata, Argentina
| | - Daniel E. Bacelo
- Departamento de Química, Facultad de Ciencias Exactas y Naturales, Universidad de Belgrano, Buenos Aires, Argentina
| | - Pablo R. Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas, CCT La Plata-CONICET, UNLP, La Plata, Argentina
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20
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Kittelmann J, Lang KM, Ottens M, Hubbuch J. An orientation sensitive approach in biomolecule interaction quantitative structure–activity relationship modeling and its application in ion-exchange chromatography. J Chromatogr A 2017; 1482:48-56. [DOI: 10.1016/j.chroma.2016.12.065] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2016] [Revised: 12/11/2016] [Accepted: 12/15/2016] [Indexed: 11/16/2022]
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21
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
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Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
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22
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Dihydrofolate reductase inhibitors: a quantitative structure–activity relationship study using 2D-QSAR and 3D-QSAR methods. Med Chem Res 2016. [DOI: 10.1007/s00044-016-1742-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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23
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Golmohammadi H, Dashtbozorgi Z. Prediction of solvation enthalpy of gaseous organic compounds in propanol. RUSSIAN JOURNAL OF PHYSICAL CHEMISTRY A 2016. [DOI: 10.1134/s0036024416090119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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24
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Yuan J, Xie C, Zhang T, Sun J, Yuan X, Yu S, Zhang Y, Cao Y, Yu X, Yang X, Yao W. Linear and nonlinear models for predicting fish bioconcentration factors for pesticides. CHEMOSPHERE 2016; 156:334-340. [PMID: 27183335 DOI: 10.1016/j.chemosphere.2016.05.002] [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] [Received: 09/06/2015] [Revised: 04/27/2016] [Accepted: 05/02/2016] [Indexed: 06/05/2023]
Abstract
This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation.
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Affiliation(s)
- Jintao Yuan
- School of Public Health, Zhengzhou University, Zhengzhou, 450001, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China
| | - Chun Xie
- Shangqiu Medical College, Shangqiu, Henan Province 476100, China
| | - Ting Zhang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China
| | - Jinfang Sun
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China
| | - Xuejie Yuan
- Shangqiu Medical College, Shangqiu, Henan Province 476100, China
| | - Shuling Yu
- Key Laboratory of Natural Medicine and Immune-Engineering of Henan Province, Henan University, Kaifeng, Henan 475004, China
| | - Yingbiao Zhang
- Shenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen, Guangdong 518001, China
| | - Yunyuan Cao
- School of Public Health, Zhengzhou University, Zhengzhou, 450001, China
| | - Xingchen Yu
- School of Public Health, Zhengzhou University, Zhengzhou, 450001, China
| | - Xuan Yang
- School of Public Health, Zhengzhou University, Zhengzhou, 450001, China
| | - Wu Yao
- School of Public Health, Zhengzhou University, Zhengzhou, 450001, China.
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25
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Goodarzi M, Coelho LDS, Honarparvar B, Ortiz EV, Duchowicz PR. Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2016; 128:52-60. [PMID: 26890190 DOI: 10.1016/j.ecoenv.2016.01.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 01/23/2016] [Accepted: 01/25/2016] [Indexed: 06/05/2023]
Abstract
The application of molecular descriptors in describing Quantitative Structure Property Relationships (QSPR) for the estimation of vapor pressure (VP) of pesticides is of ongoing interest. In this study, QSPR models were developed using multiple linear regression (MLR) methods to predict the vapor pressure values of 162 pesticides. Several feature selection methods, namely the replacement method (RM), genetic algorithms (GA), stepwise regression (SR) and forward selection (FS), were used to select the most relevant molecular descriptors from a pool of variables. The optimum subset of molecular descriptors was used to build a QSPR model to estimate the vapor pressures of the selected pesticides. The Replacement Method improved the predictive ability of vapor pressures and was more reliable for the feature selection of these selected pesticides. The results provided satisfactory MLR models that had a satisfactory predictive ability, and will be important for predicting vapor pressure values for compounds with unknown values. This study may open new opportunities for designing and developing new pesticide.
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Affiliation(s)
- Mohammad Goodarzi
- Department of Biosystems, Faculty of Bioscience Engineering, Katholieke Universiteit Leuven - KULeuven, Kasteelpark Arenberg 30, B-3001 Heverlee, Belgium
| | - Leandro dos Santos Coelho
- Department of Electrical Engineering, Federal University of Parana (UFPR), Rua Cel. Francisco Heraclito dos Santos, 100, 81531-980 Curitiba, PR, Brazil; Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Parana (PUCPR), Imaculada Conceição, 1155, 80215-901 Curitiba, PR, Brazil
| | - Bahareh Honarparvar
- School of Pharmacy and Pharmacology, University of KwaZulu-Natal, Durban 4001, South Africa
| | - Erlinda V Ortiz
- IMCoDeG (CONICET), Fac. de Tecnología y Cs. Aplicadas, Universidad Nacional de Catamarca, Maximio Victoria 55, Catamarca, Argentina
| | - Pablo R Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (CCT La Plata-CONICET, UNLP), Diag. 113 y 64, Sucursal 4, C.C. 16, 1900 La Plata, Argentina.
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Yu S, Gao S, Gan Y, Zhang Y, Ruan X, Wang Y, Yang L, Shi J. QSAR models for predicting octanol/water and organic carbon/water partition coefficients of polychlorinated biphenyls. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:249-63. [PMID: 26998720 DOI: 10.1080/1062936x.2016.1158734] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Quantitative structure-property relationship modelling can be a valuable alternative method to replace or reduce experimental testing. In particular, some endpoints such as octanol-water (KOW) and organic carbon-water (KOC) partition coefficients of polychlorinated biphenyls (PCBs) are easier to predict and various models have been already developed. In this paper, two different methods, which are multiple linear regression based on the descriptors generated using Dragon software and hologram quantitative structure-activity relationships, were employed to predict suspended particulate matter (SPM) derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of 209 PCBs. The predictive ability of the derived models was validated using a test set. The performances of all these models were compared with EPI Suite™ software. The results indicated that the proposed models were robust and satisfactory, and could provide feasible and promising tools for the rapid assessment of the SPM derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of PCBs.
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Affiliation(s)
- S Yu
- a Key Laboratory of Natural Medicine and Immune Engineering of Henan Province , Henan University , Kaifeng , China
| | - S Gao
- a Key Laboratory of Natural Medicine and Immune Engineering of Henan Province , Henan University , Kaifeng , China
| | - Y Gan
- a Key Laboratory of Natural Medicine and Immune Engineering of Henan Province , Henan University , Kaifeng , China
| | - Y Zhang
- a Key Laboratory of Natural Medicine and Immune Engineering of Henan Province , Henan University , Kaifeng , China
| | - X Ruan
- b Zhengjiang Fangyuan Test Group Co., Ltd , Zhejiang , China
| | - Y Wang
- a Key Laboratory of Natural Medicine and Immune Engineering of Henan Province , Henan University , Kaifeng , China
| | - L Yang
- a Key Laboratory of Natural Medicine and Immune Engineering of Henan Province , Henan University , Kaifeng , China
- c Pharmaceutical College of Henan University , Kaifeng , China
| | - J Shi
- a Key Laboratory of Natural Medicine and Immune Engineering of Henan Province , Henan University , Kaifeng , China
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27
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Shiri F, Pirhadi S, Ghasemi JB. Alignment independent 3D-QSAR, quantum calculations and molecular docking of Mer specific tyrosine kinase inhibitors as anticancer drugs. Saudi Pharm J 2016; 24:197-212. [PMID: 27013913 PMCID: PMC4792907 DOI: 10.1016/j.jsps.2015.03.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2015] [Accepted: 03/13/2015] [Indexed: 11/29/2022] Open
Abstract
Mer receptor tyrosine kinase is a promising novel cancer therapeutic target in many human cancers, because abnormal activation of Mer has been implicated in survival signaling and chemoresistance. 3D-QSAR analyses based on alignment independent descriptors were performed on a series of 81 Mer specific tyrosine kinase inhibitors. The fractional factorial design (FFD) and the enhanced replacement method (ERM) were applied and tested as variable selection algorithms for the selection of optimal subsets of molecular descriptors from a much greater pool of such regression variables. The data set was split into 65 molecules as the training set and 16 compounds as the test set. All descriptors were generated by using the GRid INdependent descriptors (GRIND) approach. After variable selection, GRIND were correlated with activity values (pIC50) by PLS regression. Of the two applied variable selection methods, ERM had a noticeable improvement on the statistical parameters of PLS model, and yielded a q (2) value of 0.77, an [Formula: see text] of 0.94, and a low RMSEP value of 0.25. The GRIND information contents influencing the affinity on Mer specific tyrosine kinase were also confirmed by docking studies. In a quantum calculation study, the energy difference between HOMO and LUMO (gap) implied the high interaction of the most active molecule in the active site of the protein. In addition, the molecular electrostatic potential energy at DFT level confirmed results obtained from the molecular docking. The identified key features obtained from the molecular modeling, enabled us to design novel kinase inhibitors.
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Affiliation(s)
- Fereshteh Shiri
- Department of Chemistry, University of Zabol, P.O. Box 98615-538, Zabol, Iran
| | - Somayeh Pirhadi
- Drug Design in Silico Laboratory, Chemistry Faculty, K.N. Toosi University of Technology, Tehran, Iran
| | - Jahan B. Ghasemi
- Drug Design in Silico Laboratory, Chemistry Faculty, K.N. Toosi University of Technology, Tehran, Iran
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28
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Saavedra LM, Ruiz D, Romanelli GP, Duchowicz PR. Quantitative Structure-Antifungal Activity Relationships for cinnamate derivatives. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2015; 122:521-527. [PMID: 26410195 DOI: 10.1016/j.ecoenv.2015.09.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Revised: 09/09/2015] [Accepted: 09/14/2015] [Indexed: 06/05/2023]
Abstract
Quantitative Structure-Activity Relationships (QSAR) are established with the aim of analyzing the fungicidal activities of a set of 27 active cinnamate derivatives. The exploration of more than a thousand of constitutional, topological, geometrical and electronic molecular descriptors, which are calculated with Dragon software, leads to predictions of the growth inhibition on Pythium sp and Corticium rolfsii fungi species, in close agreement to the experimental values extracted from the literature. A set containing 21 new structurally related cinnamate compounds is prepared. The developed QSAR models are applied to predict the unknown fungicidal activity of this set, showing that cinnamates like 38, 28 and 42 are expected to be highly active for Pythium sp, while this is also predicted for 28 and 34 in C. rolfsii.
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Affiliation(s)
- Laura M Saavedra
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Diagonal 113 y 64, Sucursal 4, C.C. 16, 1900 La Plata, Argentina
| | - Diego Ruiz
- Curso de Química Orgánica, Facultad de Ciencias Agrarias y Forestales, Universidad Nacional de La Plata (UNLP), 60 y 119, B1904AAN La Plata, Buenos Aires, Argentina
| | - Gustavo P Romanelli
- Curso de Química Orgánica, Facultad de Ciencias Agrarias y Forestales, Universidad Nacional de La Plata (UNLP), 60 y 119, B1904AAN La Plata, Buenos Aires, Argentina; Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. J.J. Ronco" (CINDECA), Departamento de Química, Facultad de Ciencias Exactas, UNLP-CCT-CONICET, Calle 47 No. 257, B1900AJK La Plata, Argentina
| | - Pablo R Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Diagonal 113 y 64, Sucursal 4, C.C. 16, 1900 La Plata, Argentina.
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29
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Maadani H, Salahinejad M, Ghasemi JB. Global and local QSPR models to predict supercooled vapour pressure for organic compounds. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:1033-1045. [PMID: 26649975 DOI: 10.1080/1062936x.2015.1114967] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this study, a quantitative structure-property relationship (QSPR) approach was used for estimation of logarithmic values of supercooled liquid vapour pressure (log PL) of a large set of structurally diverse organic compounds. This set includes 12 local sets of aromatic and aliphatic hydrocarbons, polychlorinated biphenyls, ethers, polychlorinated and brominated diphenylethers, polychlorinated naphthalenes and alcohols. Some simple models based on the linear relationship between log PL and VolSurf descriptors were developed as global models, and a general equation as a simple way to calculate the supercooled liquid vapour pressure of organic chemicals was provided. A descriptor representing the hydrophilic regions (WO1) of organic chemicals showed the highest correlation with log PL and resulted in a one-parameter global model characterized by satisfactory statistical performance; calibration (r2c) and prediction (r2p) correlation coefficient of 0.84 and 0.85, respectively. Moreover, local QSPR models were also developed for each subset of organic compounds and, as expected, the statistical results obtained from these models were better than the global one. From the descriptors involved in the models, it is concluded that the hydrophilic and hydrophobic regions at different energy levels and polarizability usually determine the variation of supercooled liquid vapour pressure of organic compounds.
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Affiliation(s)
- H Maadani
- a Chemistry Faculty , K N Toosi University of Technology , Tehran , Iran
| | - M Salahinejad
- b Nuclear Science and Technology Research Institute , Tehran , Iran
| | - J B Ghasemi
- a Chemistry Faculty , K N Toosi University of Technology , Tehran , Iran
- c Chemistry Faculty , University of Tehran , Tehran , Iran
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30
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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]
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31
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Prediction of gas-to-ionic liquid partition coefficient of organic solutes dissolved in 1-(2-methoxyethyl)-1-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate using QSPR approaches. J Mol Liq 2015. [DOI: 10.1016/j.molliq.2014.11.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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32
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Quantitative structure property relationships on formation constants of radiometals for radiopharmaceuticals applications. J Radioanal Nucl Chem 2015. [DOI: 10.1007/s10967-014-3377-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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33
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS IN DRUG DESIGN, PREDICTIVE TOXICOLOGY, AND RISK ASSESSMENT 2015. [DOI: 10.4018/978-1-4666-8136-1.ch001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
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Affiliation(s)
- Maryam Hamzeh-Mivehroud
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Siavoush Dastmalchi
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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34
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Comelli NC, Duchowicz PR, Castro EA. QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1. Eur J Pharm Sci 2014; 62:171-9. [PMID: 24909730 DOI: 10.1016/j.ejps.2014.05.029] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2014] [Revised: 05/27/2014] [Accepted: 05/28/2014] [Indexed: 02/01/2023]
Abstract
The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (-logIC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure D-optimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (Rtest2). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method.
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Affiliation(s)
- Nieves C Comelli
- Facultad de Ciencias Agrarias, Universidad Nacional de Catamarca, Av. Belgrano y Maestro Quiroga, 4700 Catamarca, Argentina.
| | - Pablo R Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Diag. 113 y 64, C.C. 16, Sucursal 4, 1900 La Plata, Argentina
| | - Eduardo A Castro
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (UNLP, CCT La Plata-CONICET), Diag. 113 y 64, C.C. 16, Sucursal 4, 1900 La Plata, Argentina
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QSAR analysis on tacrine-related acetylcholinesterase inhibitors. J Biomed Sci 2014; 21:84. [PMID: 25239202 PMCID: PMC4177578 DOI: 10.1186/s12929-014-0084-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2014] [Accepted: 08/13/2014] [Indexed: 11/16/2022] Open
Abstract
Background The evaluation of the clinical effects of Tacrine has shown efficacy in delaying the deterioration of the symptoms of Alzheimer’s disease, while confirming the adverse events consisting mainly in the elevated liver transaminase levels. The study of tacrine analogs presents a continuous interest, and for this reason we establish Quantitative Structure-Activity Relationships on their Acetylcholinesterase inhibitory activity. Results Ten groups of new developed Tacrine-related inhibitors are explored, which have been experimentally measured in different biochemical conditions and AChE sources. The number of included descriptors in the structure-activity relationship is characterized by ‘Rule of Thumb’. The 1502 applied molecular descriptors could provide the best linear models for the selected Alzheimer’s data base and the best QSAR model is reported for the considered data sets. Conclusion The QSAR models developed in this work have a satisfactory predictive ability, and are obtained by selecting the most representative molecular descriptors of the chemical structure, represented through more than a thousand of constitutional, topological, geometrical, quantum-mechanical and electronic descriptor types. Electronic supplementary material The online version of this article (doi:10.1186/s12929-014-0084-0) contains supplementary material, which is available to authorized users.
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36
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Khooshechin S, Dashtbozorgi Z, Golmohammadi H, Acree WE. QSPR prediction of gas-to-ionic liquid partition coefficient of organic solutes dissolved in 1-(2-hydroxyethyl)-1-methylimidazolium tris(pentafluoroethyl)trifluorophosphate using the replacement method and support vector regression. J Mol Liq 2014. [DOI: 10.1016/j.molliq.2014.03.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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37
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Srivastav VK, Tiwari M. k-nearest neighbor molecular field analysis based 3D-QSAR and in silico ADME/T studies of cinnamoyl derivatives as HIV-1 integrase inhibitors. Med Chem Res 2014. [DOI: 10.1007/s00044-014-1183-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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38
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Quantitative structure activity relationship and binding investigation of N-alkyl glycine amides as inhibitors of Leukotriene A4 hydrolase. Med Chem Res 2014. [DOI: 10.1007/s00044-014-1121-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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39
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Salahinejad M, Ghasemi JB. 3D-QSAR studies on the toxicity of substituted benzenes to Tetrahymena pyriformis: CoMFA, CoMSIA and VolSurf approaches. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2014; 105:128-134. [PMID: 24636479 DOI: 10.1016/j.ecoenv.2013.11.019] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2013] [Revised: 11/19/2013] [Accepted: 11/21/2013] [Indexed: 06/03/2023]
Abstract
Three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis were performed on the toxicity of a large set of substituted benzenes toward ciliate Tetrahymena pyriformis. The 3D-QSAR studies were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and VolSurf techniques. The optimal CoMFA and CoMSIA models obtained from the training set were all statistically significant with correlation coefficients (R(2)) greater than 0.79 and absolute error less than 0.33 in log units. The predictive ability of the models was externally evaluated through the prediction of a test set (20 percent of the whole data set) that were not included in the training set. A simple and fairly good predictive linear model based on VolSurf descriptors was also developed that showed an adequate prediction power of the toxicity (pIGC50) of substituted benzenes. Validation, reliability and robustness of models were also evaluated by leave-one-out, leave-four-out, bootstrapping and progressive scrambling approaches. The results confirmed that in addition to hydrophobic effects, electrostatic and H-bonding interactions also play important roles in the toxicity of substituted benzenes. The information obtained from CoMFA and CoMSIA 3-D contour maps could be useful to explain the toxicity mechanism of substituted benzenes.
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Affiliation(s)
- M Salahinejad
- Environmental Laboratory, NSTRI, P. O. Box 11365-3486, Tehran, Iran.
| | - J B Ghasemi
- Chemistry Department, Faculty of Sciences, K.N. Toosi University of Technology, Iran
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40
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Duchowicz PR, Bennardi DO, Bacelo DE, Bonifazi EL, Rios-Luci C, Padrón JM, Burton G, Misico RI. QSAR on antiproliferative naphthoquinones based on a conformation-independent approach. Eur J Med Chem 2014; 77:176-84. [DOI: 10.1016/j.ejmech.2014.02.057] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Revised: 02/05/2014] [Accepted: 02/25/2014] [Indexed: 12/26/2022]
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41
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Yuan J, Pu Y, Yin L. QSAR study of liver specificity of carcinogenicity of N-nitroso compounds. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2012; 84:282-292. [PMID: 22910279 DOI: 10.1016/j.ecoenv.2012.07.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2012] [Revised: 07/21/2012] [Accepted: 07/24/2012] [Indexed: 06/01/2023]
Abstract
The quantitative structure-activity relationship (QSAR) of N-nitroso compounds (NOCs) for rat liver was developed by a topological sub-structural molecular-descriptors (TOPS-MODE) approach to predict non-liver-carcinogenic and liver-carcinogenic N-nitroso compounds based on a data set of 108 NOCs. Three descriptors calculated solely from the molecular structures of the compounds were selected by enhanced replacement method (ERM) and were weighted, respectively, with atomic weight, bond dipole moments and Abraham solute descriptor partition between water and aqueous solvent systems to indicate the importance of their roles in liver specificity. A detailed discussion on these three descriptors was carried out, and the contributions of different fragments to rat-liver specificity and the interactions among fragments were analyzed. Such results can offer some useful theoretical references for understanding the chemical structural and biological factors related to the liver-specific biological activity of NOCs.
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Affiliation(s)
- Jintao Yuan
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, 87 Dingjiaqiao, Nanjing 210009, China.
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42
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Simon L, Abdelmalek B. Design of skin penetration enhancers using replacement methods for the selection of the molecular descriptors. Pharmaceutics 2012; 4:343-53. [PMID: 24300295 PMCID: PMC3834920 DOI: 10.3390/pharmaceutics4030343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2012] [Revised: 06/25/2012] [Accepted: 06/28/2012] [Indexed: 11/23/2022] Open
Abstract
Transdermal delivery of certain drugs is challenging because of skin barrier resistance. This study focuses on the implementation of feature-selection algorithms to design chemical penetration enhancers. A database, consisting of 145 polar and nonpolar chemicals, was chosen for the investigation. Replacement, enhanced replacement and stepwise algorithms were applied to identify relevant structural properties of these compounds. The descriptors were calculated using Molecular Modeling Pro™ Plus. Based on the coefficient of determination, the replacement methods outperformed the stepwise approach in selecting the features that best correlated with the flux enhancement ratio. An artificial neural network model was built to map a subset of descriptors from sixty-one nonpolar enhancers onto the output vector. The R2 value improved from 0.68, for a linear model, to 0.74, which shows that the improved framework might be effective in the design of compounds with user-defined properties.
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Affiliation(s)
- Laurent Simon
- Otto H. York Department of Chemical, Biological and Pharmaceutical Engineering, New Jersey Institute of Technology, Newark NJ 07102, USA.
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43
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Zhang H, Zhang L, Peng LJ, Dong XW, Wu D, Wu VCH, Feng FQ. Quantitative structure-activity relationships of antimicrobial fatty acids and derivatives against Staphylococcus aureus. J Zhejiang Univ Sci B 2012; 13:83-93. [PMID: 22302421 DOI: 10.1631/jzus.b1100049] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Fatty acids and derivatives (FADs) are resources for natural antimicrobials. In order to screen for additional potent antimicrobial agents, the antimicrobial activities of FADs against Staphylococcus aureus were examined using a microplate assay. Monoglycerides of fatty acids were the most potent class of fatty acids, among which monotridecanoin possessed the most potent antimicrobial activity. The conventional quantitative structure-activity relationship (QSAR) and comparative molecular field analysis (CoMFA) were performed to establish two statistically reliable models (conventional QSAR: R(2)=0.942, Q(2)(LOO)=0.910; CoMFA: R(2)=0.979, Q(2)=0.588, respectively). Improved forecasting can be achieved by the combination of these two models that provide a good insight into the structure-activity relationships of the FADs and that may be useful to design new FADs as antimicrobial agents.
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Affiliation(s)
- Hui Zhang
- Department of Food Science and Nutrition, Zhejiang University, Hangzhou 310058, China
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44
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Ghasemi JB, Tavakoli H. Improvement of the Prediction Power of the CoMFA and CoMSIA Models on Histamine H3 Antagonists by Different Variable Selection Methods. Sci Pharm 2012; 80:547-66. [PMID: 23008805 PMCID: PMC3447613 DOI: 10.3797/scipharm.1204-19] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2012] [Accepted: 05/24/2012] [Indexed: 11/22/2022] Open
Abstract
The aim of this study is to enhance the predictivity power of CoMFA and CoMSIA models by means of different variable selection algorithms. The genetic algorithm (GA), successive projection algorithm (SPA), stepwise multiple linear regression (SW-MLR), and the enhanced replacement method (ERM) were used and tested as variable selection algorithms. Then, the selected variables were used to generate a simple and predictive model by the multilinear regression algorithm. A set of 74 histamine H3 antagonists were split into 40 compounds as a training set, and 17 compounds as a test set, by the Kennard-Stone algorithm. Before splitting the data, 17 compounds were randomly selected from the pool of the whole data set as an evaluation set without any supervision, pretreatment, or visual inspection. Among applied variable selection algorithms, ERM had noticeable improvement on the statistical parameters. The r2 values of training, test, and evaluation sets for the ERM-MLR model using CoMFA fields were 0.9560, 0.8630, and 0.8460 and using the CoMSIA fields were 0.9800, 0.8521, and 0.9080, respectively. In this study, the principles of organization for economic cooperation and development (OECD) for regulatory acceptability of QSARs are considered.
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Affiliation(s)
- Jahan B Ghasemi
- Department of chemistry, faculty of sciences, K. N. Toosi University of Technology, Tehran, Iran
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45
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Ghasemi JB, Zolfonoun E. A New Variable Selection Method Based on Mutual Information Maximization by Replacing Collinear Variables for Nonlinear Quantitative Structure-Property Relationship Models. B KOREAN CHEM SOC 2012. [DOI: 10.5012/bkcs.2012.33.5.1527] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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46
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Toropov AA, Toropova AP, Raska I, Benfenati E, Gini G. QSAR modeling of endpoints for peptides which is based on representation of the molecular structure by a sequence of amino acids. Struct Chem 2012. [DOI: 10.1007/s11224-012-9995-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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47
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Comelli NC, Duchowicz PR, Lobayan RM, Jubert AH, Castro EA. QSPR Study of Valproic Acid and Its Functionalized Derivatives. Mol Inform 2012; 31:181-8. [DOI: 10.1002/minf.201100119] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2011] [Accepted: 12/19/2011] [Indexed: 11/12/2022]
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48
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Pasquale G, Romanelli GP, Autino JC, García J, Ortiz EV, Duchowicz PR. Quantitative structure-activity relationships of mosquito larvicidal chalcone derivatives. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2012; 60:692-697. [PMID: 22217234 DOI: 10.1021/jf203374r] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The mosquito larvicidal activities of a series of chalcones and some derivatives were subjected to a quantitative structure-activity relationship (QSAR) study, using more than a thousand constitutional, topological, geometrical, and electronic molecular descriptors calculated with Dragon software. The larvicidal activity values for 28 active compounds of the series were predicted, showing in general a good approximation to the experimental values found in the literature. Chalcones having one or both electron-rich rings showed high toxicity. However, the activity of chalcones was reduced by electron-withdrawing groups, and this was roughly diminished by derivatization of the carbonyl group. A set of six chalcones being structurally similar to some of the active ones, with a still unknown larvicidal activity, were prepared. Their activity values were predicted by applying the developed QSAR models, showing that two chalcones of such set, both 32 and 34, were expected to be highly active.
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Affiliation(s)
- Gustavo Pasquale
- Cátedra de Química Orgánica, Facultad de Ciencias Agrarias y Forestales, Universidad Nacional de La Plata (UNLP), La Plata, Buenos Aires, Argentina
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49
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Yuan J, Pu Y, Yin L. Predicting carcinogenicity and understanding the carcinogenic mechanism of N-nitroso compounds using a TOPS-MODE approach. Chem Res Toxicol 2011; 24:2269-79. [PMID: 22084901 DOI: 10.1021/tx2004097] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
A linear discriminant analysis (LDA) coupled with an enhanced replacement method (ERM) was used as an alternative method to predict the carcinogenicity of N-nitroso compounds (NOCs) in rats. This presented LDA based on the topological substructural molecular descriptors (TOPS-MODE) approach was developed to predict the carcinogenic and noncarcinogenic activity on a data set of 111 NOCs with a good classification value of 90.1%. The predictive power of the LDA model was validated through an external validation set (37 compounds) with a prediction accuracy of 94.6% and a leave-one-out cross-validation procedure (LOOCV) with a good prediction of 86.5%. This methodology showed that the TOPS-MODE descriptors weighted, respectively, by bond dipole moment and Abraham solute descriptor dipolarity/polarizability affected the NOC carcinogenicity. The contributions of certain bonds and fragments to carcinogenicity were used to assess biotransformation and carcinogenic mechanisms. The positive contribution of the carbon-nitrogen single bond (between the N-nitroso group and α-carbon to the N-nitroso group) indicated that the α-hydroxylation reaction could occur at the α-carbon or otherwise not occur. Similarly, the contributions from the molecular fragment could be applied to indicate whether the fragments generated an alkylating agent. These results suggested that this approach could discriminate between carcinogenic and noncarcinogenic NOCs, thereby providing insight into the structural features and chemical factors related to NOC carcinogenicity.
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Affiliation(s)
- Jintao Yuan
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
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50
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Mercader AG, Duchowicz PR, Fernández FM, Castro EA. Advances in the Replacement and Enhanced Replacement Method in QSAR and QSPR Theories. J Chem Inf Model 2011; 51:1575-81. [DOI: 10.1021/ci200079b] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Andrew G. Mercader
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA, UNLP, CCT La Plata-CONICET), Diag. 113 y 64, Sucursal 4, C.C. 16, 1900 La Plata, Argentina
- PRALIB (UBA-CONICET), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Junín 956, C1113AAD Buenos Aires, Argentina
| | - Pablo R. Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA, UNLP, CCT La Plata-CONICET), Diag. 113 y 64, Sucursal 4, C.C. 16, 1900 La Plata, Argentina
| | - Francisco M. Fernández
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA, UNLP, CCT La Plata-CONICET), Diag. 113 y 64, Sucursal 4, C.C. 16, 1900 La Plata, Argentina
| | - Eduardo A. Castro
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA, UNLP, CCT La Plata-CONICET), Diag. 113 y 64, Sucursal 4, C.C. 16, 1900 La Plata, Argentina
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