1
|
A partial least squares and artificial neural network study for a series of arylpiperazines as antidepressant agents. J Mol Model 2021; 27:297. [PMID: 34558019 DOI: 10.1007/s00894-021-04906-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/08/2021] [Indexed: 10/20/2022]
Abstract
Depression affects more than 300 million people around the world and can lead to suicide. About 30% of patients on treatment for depression drop out of therapy due to side effects or to latency time associated to therapeutic effects. 5-HT receptor, known as serotonin, is considered the key in depression treatment. Arylpiperazine compounds are responsible for several pharmacological effects and are considered as ligands in serotonin receptors, such as the subtype 5-HT2a. Here, in silico studies were developed using partial least squares (PLSs) and artificial neural networks (ANNs) to design new arylpiperazine compounds that could interact with the 5-HT2a receptor. First, molecular and electronic descriptors were calculated and posteriorly selected from correlation matrixes and genetic algorithm (GA). Then, the selected descriptors were used to construct PLS and ANN models that showed to be robust and predictive. Lastly, new arylpiperazine compounds were designed and their biological activity values were predicted by both PLS and ANN models. It is worth to highlight compounds G5 and G7 (predicted by the PLS model) and G3 and G15 (predicted by the ANN model), whose predicted pIC50 values were as high as the three highest values from the arylpiperazine original set studied here. Therefore, it can be asserted that the two models (PLS and ANN) proposed in this work are promising for the prediction of the biological activity of new arylpiperazine compounds and may significantly contribute to the design of new drugs for the treatment of depression.
Collapse
|
2
|
Jiao L, Liu H, Qu L, Xue Z, Wang Y, Wang Y, Lei B, Zang Y, Xu R, Zhang Z, Li H, Alyemeni OAA. QSPR Studies on the Octane Number of Toluene Primary Reference Fuel Based on the Electrotopological State Index. ACS OMEGA 2020; 5:3878-3888. [PMID: 32149214 PMCID: PMC7057327 DOI: 10.1021/acsomega.9b03139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 02/07/2020] [Indexed: 05/10/2023]
Abstract
The quantitative structure-property relationship (QSPR) models for predicting the octane number (ON) of toluene primary reference fuel (TPRF; blends of n-heptane, isooctane, and toluene) was investigated. The electrotopological state (E-state) index of TPRF components was computed and weight-summed to generate the quantitative descriptor of TPRF samples. The partial least squares (PLS) technique was used to build up the regression model between the ON and weight-summed E-state index of the investigated samples. The QSPR models for the research octane number (RON) and motor octane number (MON) of TPRF were built. The prediction performance of the obtained PLS models was assessed by the external test set validation and leave-one-out cross-validation. The validation results demonstrate that the proposed PLS models are feasible for predicting the ON, both RON and MON, of TPRF. In addition, several other QSPR models for the ON of TPRF were developed by employing the stepwise regression and Scheffé polynomials methods, and the prediction performance of these models were compared with that of the PLS models. The comparison result shows that the proposed PLS models are slightly better than multiple linear regression models and Scheffé models. It is demonstrated that the combination of the E-state index and PLS is an easy-to-use and promising method for studying and forecasting the ON of TPRF.
Collapse
Affiliation(s)
- Long Jiao
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
- E-mail:
| | - Huanhuan Liu
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Le Qu
- State
Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chendu University of Technology, Chendu 610059, P. R. China
- Shaanxi
Cooperative Innovation Center of Unconventional Oil and Gas Exploration
and Development, Xi’an Shiyou University, Xi’an 710065, P. R. China
| | - Zhiwei Xue
- No.
203 Research Institute of Nuclear Industry, Xianyang 712000, P. R. China
| | - Yuan Wang
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Yanzhao Wang
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Bin Lei
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Yunlei Zang
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Rui Xu
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Zhen Zhang
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | - Hua Li
- College
of Chemistry and Chemical Engineering, Xi’an
Shiyou University, Xi’an 710065, P. R. China
| | | |
Collapse
|
3
|
Prediction of stenosis behaviour in artery by neural network and multiple linear regressions. Biomech Model Mechanobiol 2020; 19:1697-1711. [DOI: 10.1007/s10237-020-01300-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 01/22/2020] [Indexed: 11/26/2022]
|
4
|
Khan PM, Roy K, Benfenati E. Chemometric modeling of Daphnia magna toxicity of agrochemicals. CHEMOSPHERE 2019; 224:470-479. [PMID: 30831498 DOI: 10.1016/j.chemosphere.2019.02.147] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/21/2019] [Accepted: 02/22/2019] [Indexed: 06/09/2023]
Abstract
Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.
Collapse
Affiliation(s)
- Pathan Mohsin Khan
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054, Kolkata, India
| | - 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, 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
|
5
|
Tugcu G, Sipahi H. QSPR modelling of in vitro degradation half-life of acyl glucuronides. Xenobiotica 2018; 49:1007-1014. [DOI: 10.1080/00498254.2018.1527049] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Gulcin Tugcu
- Department of Toxicology, Yeditepe University, Istanbul, Turkey
| | - Hande Sipahi
- Department of Toxicology, Yeditepe University, Istanbul, Turkey
| |
Collapse
|
6
|
Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine. Biochim Biophys Acta Gen Subj 2016; 1860:2664-71. [PMID: 27217074 DOI: 10.1016/j.bbagen.2016.05.019] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 05/03/2016] [Accepted: 05/08/2016] [Indexed: 11/23/2022]
Abstract
BACKGROUND Elimination half-life is an important pharmacokinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The experimental evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life. METHODS In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold. RESULTS Among seven individual models, GBM showed the best performance (R(2)=0.820 and RMSE=0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R(2)=0.738 and RMSE=0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed. CONCLUSIONS An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R(2)=0.723 and RMSE=0.698). GENERAL SIGNIFICANCE Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
Collapse
|
7
|
Dong MH, Chen HF, Ren YJ, Shao FM. Molecular modeling studies, synthesis and biological evaluation of dabigatran analogues as thrombin inhibitors. Bioorg Med Chem 2015; 24:73-84. [PMID: 26690913 DOI: 10.1016/j.bmc.2015.11.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2015] [Revised: 11/17/2015] [Accepted: 11/20/2015] [Indexed: 11/16/2022]
Abstract
In this work, 48 thrombin inhibitors based on the structural scaffold of dabigatran were analyzed using a combination of molecular modeling techniques. We generated three-dimensional quantitative structure-activity relationship (3D-QSAR) models based on three alignments for both comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) to highlight the structural requirements for thrombin protein inhibition. In addition to the 3D-QSAR study, Topomer CoMFA model also was established with a higher leave-one-out cross-validation q(2) and a non-cross-validation r(2), which suggest that the three models have good predictive ability. The results indicated that the steric, hydrophobic and electrostatic fields play key roles in QSAR model. Furthermore, we employed molecular docking and re-docking simulation explored the binding relationship of the ligand and the receptor protein in detail. Molecular docking simulations identified several key interactions that were also indicated through 3D-QSAR analysis. On the basis of the obtained results, two compounds were designed and predicted by three models, the biological evaluation in vitro (IC50) demonstrated that these molecular models were effective for the development of novel potent thrombin inhibitors.
Collapse
Affiliation(s)
- Ming-Hui Dong
- College of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai 201418, China
| | - Hai-Feng Chen
- College of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai 201418, China
| | - Yu-Jie Ren
- College of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai 201418, China.
| | - Fang-Ming Shao
- School of Science, East China University of Science and Technology, Shanghai 200237, China
| |
Collapse
|
8
|
Shibi I, Aswathy L, Jisha R, Masand V, Divyachandran A, Gajbhiye J. Molecular docking and QSAR analyses for understanding the antimalarial activity of some 7-substituted-4-aminoquinoline derivatives. Eur J Pharm Sci 2015; 77:9-23. [DOI: 10.1016/j.ejps.2015.05.025] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Revised: 05/02/2015] [Accepted: 05/21/2015] [Indexed: 12/28/2022]
|
9
|
Aalizadeh R, Pourbasheer E, Ganjali MR. Analysis of B-Raf $$^{\mathrm{V600E}}$$ V 600 E inhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies. Mol Divers 2015; 19:915-30. [DOI: 10.1007/s11030-015-9626-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2015] [Accepted: 07/27/2015] [Indexed: 12/14/2022]
|
10
|
Development of models for prediction of the antioxidant activity of derivatives of natural compounds. Anal Chim Acta 2015; 868:23-35. [DOI: 10.1016/j.aca.2015.01.050] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Revised: 01/23/2015] [Accepted: 01/27/2015] [Indexed: 12/23/2022]
|
11
|
Pourbasheer E, Aalizadeh R, Ardabili JS, Ganjali MR. QSPR study on solubility of some fullerenes derivatives using the genetic algorithms — Multiple linear regression. J Mol Liq 2015. [DOI: 10.1016/j.molliq.2015.01.028] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
12
|
Pourbasheer E, Banaei A, Aalizadeh R, Ganjali MR, Norouzi P, Shadmanesh J, Methenitis C. Prediction of PCE of fullerene (C 60 ) derivatives as polymer solar cell acceptors by genetic algorithm–multiple linear regression. J IND ENG CHEM 2015. [DOI: 10.1016/j.jiec.2014.05.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
13
|
QSAR study of ACK1 inhibitors by genetic algorithm–multiple linear regression (GA–MLR). JOURNAL OF SAUDI CHEMICAL SOCIETY 2014. [DOI: 10.1016/j.jscs.2014.01.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
14
|
Pourbasheer E, Aalizadeh R, Shokouhi Tabar S, Ganjali MR, Norouzi P, Shadmanesh J. 2D and 3D Quantitative Structure–Activity Relationship Study of Hepatitis C Virus NS5B Polymerase Inhibitors by Comparative Molecular Field Analysis and Comparative Molecular Similarity Indices Analysis Methods. J Chem Inf Model 2014; 54:2902-14. [DOI: 10.1021/ci500216c] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Eslam Pourbasheer
- Department
of Chemistry, Payame Noor University (PNU), P. O. Box 19395-3697, Tehran, Iran
| | | | - Samira Shokouhi Tabar
- Department
of Chemistry, Payame Noor University (PNU), P. O. Box 19395-3697, Tehran, Iran
| | - Mohammad Reza Ganjali
- Center
of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, P.O. Box 143981-7435, Tehran, Iran
- Biosensor
Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences
Institute, Tehran University of Medical Sciences, P. O. Box,
14114-13137, Tehran, Iran
| | - Parviz Norouzi
- Center
of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, P.O. Box 143981-7435, Tehran, Iran
- Biosensor
Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences
Institute, Tehran University of Medical Sciences, P. O. Box,
14114-13137, Tehran, Iran
| | | |
Collapse
|
15
|
Zhang X, Zhang X, Li Q, Sun Z, Song L, Sun T. Support Vector Machine Applied to Study on Quantitative Structure–Retention Relationships of Polybrominated Diphenyl Ether Congeners. Chromatographia 2014. [DOI: 10.1007/s10337-014-2735-4] [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]
|
16
|
Structure-activity relationship for Fe(III)-salen-like complexes as potent anticancer agents. ScientificWorldJournal 2014; 2014:745649. [PMID: 24955417 PMCID: PMC3997896 DOI: 10.1155/2014/745649] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2014] [Accepted: 02/19/2014] [Indexed: 11/17/2022] Open
Abstract
Quantitative structure activity relationship (QSAR) for the anticancer activity of Fe(III)-salen and salen-like complexes was studied. The methods of density function theory (B3LYP/LANL2DZ) were used to optimize the structures. A pool of descriptors was calculated: 1497 theoretical descriptors and quantum-chemical parameters, shielding NMR, and electronic descriptors. The study of structure and activity relationship was performed with multiple linear regression (MLR) and artificial neural network (ANN). In nonlinear method, the adaptive neuro-fuzzy inference system (ANFIS) was applied in order to choose the most effective descriptors. The ANN-ANFIS model with high statistical significance (R2train = 0.99, RMSE = 0.138, and Q2LOO = 0.82) has better capability to predict the anticancer activity of the new compounds series of this family. Based on this study, anticancer activity of this compound is mainly dependent on the geometrical parameters, position, and the nature of the substituent of salen ligand.
Collapse
|
17
|
A QSAR classification study on inhibitory activities of 2-arylbenzoxazoles against cholesteryl ester transfer protein. Med Chem Res 2014. [DOI: 10.1007/s00044-013-0789-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
18
|
QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA–MLR). Med Chem Res 2013. [DOI: 10.1007/s00044-013-0821-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
|
19
|
Pourbasheer E, Aalizadeh R, Ganjali MR, Norouzi P. QSAR study of α1β4 integrin inhibitors by GA-MLR and GA-SVM methods. Struct Chem 2013. [DOI: 10.1007/s11224-013-0300-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
20
|
Wang Z, He Z, Zhang L, Zhang H, Zhang M, Wen X, Quan G, Huang X, Pan X, Wu C. Optimization of a doxycycline hydroxypropyl-β-cyclodextrin inclusion complex based on computational modeling. Acta Pharm Sin B 2013. [DOI: 10.1016/j.apsb.2013.01.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
|
21
|
QSAR study on hERG inhibitory effect of kappa opioid receptor antagonists by linear and non-linear methods. Med Chem Res 2012. [DOI: 10.1007/s00044-012-0412-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
22
|
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]
|
23
|
Hao M, Li Y, Wang Y, Zhang S. Prediction of P2Y12 antagonists using a novel genetic algorithm-support vector machine coupled approach. Anal Chim Acta 2011; 690:53-63. [DOI: 10.1016/j.aca.2011.02.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2010] [Revised: 01/26/2011] [Accepted: 02/01/2011] [Indexed: 12/15/2022]
|
24
|
Hao M, Li Y, Wang Y, Zhang S. A classification study of respiratory Syncytial Virus (RSV) inhibitors by variable selection with random forest. Int J Mol Sci 2011; 12:1259-80. [PMID: 21541057 PMCID: PMC3083704 DOI: 10.3390/ijms12021259] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2010] [Revised: 02/10/2011] [Accepted: 02/11/2011] [Indexed: 12/29/2022] Open
Abstract
Experimental pEC50s for 216 selective respiratory syncytial virus (RSV) inhibitors are used to develop classification models as a potential screening tool for a large library of target compounds. Variable selection algorithm coupled with random forests (VS-RF) is used to extract the physicochemical features most relevant to the RSV inhibition. Based on the selected small set of descriptors, four other widely used approaches, i.e., support vector machine (SVM), Gaussian process (GP), linear discriminant analysis (LDA) and k nearest neighbors (kNN) routines are also employed and compared with the VS-RF method in terms of several of rigorous evaluation criteria. The obtained results indicate that the VS-RF model is a powerful tool for classification of RSV inhibitors, producing the highest overall accuracy of 94.34% for the external prediction set, which significantly outperforms the other four methods with the average accuracy of 80.66%. The proposed model with excellent prediction capacity from internal to external quality should be important for screening and optimization of potential RSV inhibitors prior to chemical synthesis in drug development.
Collapse
Affiliation(s)
- Ming Hao
- School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116012, China; E-Mails: (M.H.); (S.Z.)
| | - Yan Li
- School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116012, China; E-Mails: (M.H.); (S.Z.)
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +86-411-84986062; Fax: +86-411-84986063
| | - Yonghua Wang
- Center of Bioinformatics, Northwest A&F University, Yangling, Shaanxi 712100, China; E-Mail:
| | - Shuwei Zhang
- School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116012, China; E-Mails: (M.H.); (S.Z.)
| |
Collapse
|
25
|
Pourbasheer E, Riahi S, Ganjali MR, Norouzi P. QSAR study of C allosteric binding site of HCV NS5B polymerase inhibitors by support vector machine. Mol Divers 2010; 15:645-53. [DOI: 10.1007/s11030-010-9283-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2010] [Accepted: 09/20/2010] [Indexed: 11/29/2022]
|
26
|
Hao M, Li Y, Wang Y, Zhang S. Prediction of PKCθ inhibitory activity using the Random Forest Algorithm. Int J Mol Sci 2010; 11:3413-33. [PMID: 20957104 PMCID: PMC2956104 DOI: 10.3390/ijms11093413] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2010] [Revised: 08/24/2010] [Accepted: 09/03/2010] [Indexed: 12/14/2022] Open
Abstract
This work is devoted to the prediction of a series of 208 structurally diverse PKCθ inhibitors using the Random Forest (RF) based on the Mold(2) molecular descriptors. The RF model was established and identified as a robust predictor of the experimental pIC(50) values, producing good external R(2) (pred) of 0.72, a standard error of prediction (SEP) of 0.45, for an external prediction set of 51 inhibitors which were not used in the development of QSAR models. By using the RF built-in measure of the relative importance of the descriptors, an important predictor-the number of group donor atoms for H-bonds (with N and O)-has been identified to play a crucial role in PKCθ inhibitory activity. We hope that the developed RF model will be helpful in the screening and prediction of novel unknown PKCθ inhibitory activity.
Collapse
Affiliation(s)
- Ming Hao
- School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116012, China; E-Mails: (M.H.); (S.Z.)
| | - Yan Li
- School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116012, China; E-Mails: (M.H.); (S.Z.)
| | - Yonghua Wang
- Center of Bioinformatics, Northwest A&F University, Yangling, Shaanxi 712100, China; E-Mail: (Y.W.)
| | - Shuwei Zhang
- School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116012, China; E-Mails: (M.H.); (S.Z.)
| |
Collapse
|
27
|
Pourbasheer E, Riahi S, Ganjali MR, Norouzi P. Quantitative structure-retention relationship (QSRR) models for predicting the GC retention times of essential oil components. ACTA CHROMATOGR 2010. [DOI: 10.1556/achrom.22.2010.3.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|