1
|
Zhaomei G, Rentao L, Qiuxiang J. A New Model of Projection Pursuit Grade Evaluation Model Based on Simulated Annealing Ant Colony Optimization Algorithm. INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2018. [DOI: 10.4018/ijcini.2018100104] [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
Projection pursuit model (PP) is widely used in many fields, especially quality evaluation. One of the biggest shortages of PP was that the projection direction is strongly influenced by relevant parameters. In order to solve this problem, many experts and scholars introduced all kinds of parameters optimization method in PP. Based on the basis of previous studies, the article proposed a new model of projection pursuit grade evaluation model (PPE) integrated with simulated annealing ant colony optimization algorithm (SA-ACO). It provided a new thought and method for quality evaluation research. The case example demonstrated that the accuracy and the effect evaluation of the model was effectively and more objectively and practical in the evaluation of quality.
Collapse
Affiliation(s)
- Gai Zhaomei
- College of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, China
| | - Liu Rentao
- Department of Municipal and Environmental Engineering, Heilongjiang Institute of Construction Technology, China
| | - Jiang Qiuxiang
- College of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, China
| |
Collapse
|
2
|
Sheikhpour R, Sarram MA, Rezaeian M, Sheikhpour E. QSAR modelling using combined simple competitive learning networks and RBF neural networks. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2018; 29:257-276. [PMID: 29372662 DOI: 10.1080/1062936x.2018.1424030] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 01/02/2018] [Indexed: 06/07/2023]
Abstract
The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.
Collapse
Affiliation(s)
- R Sheikhpour
- a Department of Computer Engineering , Yazd University , Yazd , Iran
| | - M A Sarram
- a Department of Computer Engineering , Yazd University , Yazd , Iran
| | - M Rezaeian
- a Department of Computer Engineering , Yazd University , Yazd , Iran
| | - E Sheikhpour
- b Hematology and Oncology Research Center , Shahid Sadoughi University of Medical Sciences , Yazd , Iran
| |
Collapse
|
3
|
Wang FF, Yang W, Shi YH, Cheng XR, Le GW. Structure-based approach for the study of thyroid hormone receptor binding affinity and subtype selectivity. J Biomol Struct Dyn 2015; 34:2251-67. [DOI: 10.1080/07391102.2015.1113384] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Fang-Fang Wang
- The State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Wei Yang
- Faculty of Medicine, Department of Microbiology, Monash University, Melbourne, Victoria 3800, Australia
| | - Yong-Hui Shi
- The State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Xiang-Rong Cheng
- The State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Guo-Wei Le
- The State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| |
Collapse
|
4
|
Molecular determinants of thyroid hormone receptor selectivity in a series of phosphonic acid derivatives: 3D-QSAR analysis and molecular docking. Chem Biol Interact 2015; 240:324-35. [DOI: 10.1016/j.cbi.2015.09.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Revised: 07/16/2015] [Accepted: 09/03/2015] [Indexed: 11/20/2022]
|
5
|
Wang F, Yang W, Shi Y, Le G. Structural analysis of selective agonists of thyroid hormone receptor β using 3D-QSAR and molecular docking. J Taiwan Inst Chem Eng 2015. [DOI: 10.1016/j.jtice.2014.11.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
6
|
Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods. Toxicol Appl Pharmacol 2014; 280:177-89. [PMID: 25058446 DOI: 10.1016/j.taap.2014.07.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2014] [Revised: 07/10/2014] [Accepted: 07/11/2014] [Indexed: 12/12/2022]
Abstract
The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure-Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R(2)=0.55 and CCR=0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R(2)=0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern.
Collapse
|
7
|
Gupta MK, Misra K. Atom-based 3D-QSAR, molecular docking and molecular dynamics simulation assessment of inhibitors for thyroid hormone receptor α and β. J Mol Model 2014; 20:2286. [DOI: 10.1007/s00894-014-2286-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Accepted: 05/01/2014] [Indexed: 12/27/2022]
|
8
|
Li X, Ye L, Wang X, Wang X, Liu H, Zhu Y, Yu H. Combined 3D-QSAR, molecular docking and molecular dynamics study on thyroid hormone activity of hydroxylated polybrominated diphenyl ethers to thyroid receptors β. Toxicol Appl Pharmacol 2012; 265:300-7. [PMID: 22982074 DOI: 10.1016/j.taap.2012.08.030] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2012] [Revised: 08/27/2012] [Accepted: 08/28/2012] [Indexed: 10/27/2022]
Abstract
Several recent reports suggested that hydroxylated polybrominated diphenyl ethers (HO-PBDEs) may disturb thyroid hormone homeostasis. To illuminate the structural features for thyroid hormone activity of HO-PBDEs and the binding mode between HO-PBDEs and thyroid hormone receptor (TR), the hormone activity of a series of HO-PBDEs to thyroid receptors β was studied based on the combination of 3D-QSAR, molecular docking, and molecular dynamics (MD) methods. The ligand- and receptor-based 3D-QSAR models were obtained using Comparative Molecular Similarity Index Analysis (CoMSIA) method. The optimum CoMSIA model with region focusing yielded satisfactory statistical results: leave-one-out cross-validation correlation coefficient (q²) was 0.571 and non-cross-validation correlation coefficient (r²) was 0.951. Furthermore, the results of internal validation such as bootstrapping, leave-many-out cross-validation, and progressive scrambling as well as external validation indicated the rationality and good predictive ability of the best model. In addition, molecular docking elucidated the conformations of compounds and key amino acid residues at the docking pocket, MD simulation further determined the binding process and validated the rationality of docking results.
Collapse
Affiliation(s)
- Xiaolin Li
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210046, PR China
| | | | | | | | | | | | | |
Collapse
|
9
|
Joharapurkar AA, Dhote VV, Jain MR. Selective Thyromimetics Using Receptor and Tissue Selectivity Approaches: Prospects for Dyslipidemia. J Med Chem 2012; 55:5649-75. [DOI: 10.1021/jm2004706] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Amit A. Joharapurkar
- Department of Pharmacology and Toxicology, Zydus Research Centre, Sarkhej Bavla NH 8A, Moraiya,
Ahmedabad 382210, India
| | - Vipin V. Dhote
- Department of Pharmacology and Toxicology, Zydus Research Centre, Sarkhej Bavla NH 8A, Moraiya,
Ahmedabad 382210, India
| | - Mukul R. Jain
- Department of Pharmacology and Toxicology, Zydus Research Centre, Sarkhej Bavla NH 8A, Moraiya,
Ahmedabad 382210, India
| |
Collapse
|
10
|
Fernandez M, Caballero J, Fernandez L, Sarai A. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM). Mol Divers 2010; 15:269-89. [PMID: 20306130 DOI: 10.1007/s11030-010-9234-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2009] [Accepted: 01/25/2010] [Indexed: 10/19/2022]
Abstract
Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.
Collapse
Affiliation(s)
- Michael Fernandez
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, Iizuka, 820-8502, Japan.
| | | | | | | |
Collapse
|
11
|
Teixeira RR, Pinheiro PF, Barbosa LCDA, Carneiro JWDM, Forlani G. QSAR modeling of photosynthesis-inhibiting nostoclide derivatives. PEST MANAGEMENT SCIENCE 2010; 66:196-202. [PMID: 19798697 DOI: 10.1002/ps.1855] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
BACKGROUND A statistical model, built using the CODESSA software package, was developed to describe the relationship between the structure of nostoclide derivatives and their ability to interfere with the electron transport chain in the Hill reaction. RESULTS A QSAR treatment was carried out on a series of compounds designed using the naturally occurring toxin nostoclides to correlate molecular descriptors with their in vitro biological activity (the ability to interfere with light-driven reduction of ferricyanide by isolated spinach chloroplast thylakoid membranes). The treatment using the CODESSA software package resulted in a three-parameter model with n = 19, R(2) = 0.83, F = 23.8 and R(2) (cv) = 0.72. In the proposed model, the Image of Onsager Kirkwood solvation energy, which gives a measure of the polarity of a given compound, is the most important descriptor. The model was internally validated. CONCLUSIONS The results obtained in this study indicate that polarity, as expressed by the dipole moment, is the most relevant molecular property determining efficiency of photosynthetic inhibitory activity.
Collapse
Affiliation(s)
- Róbson Ricardo Teixeira
- Department of Chemistry, Federal University of Viçosa, Avenida P. H. Rolfs, CEP 36570-000, Viçosa, MG, Brazil.
| | | | | | | | | |
Collapse
|
12
|
Ren Y, Qin J, Liu H, Yao X, Liu M. QSPR Study on the Melting Points of a Diverse Set of Potential Ionic Liquids by Projection Pursuit Regression. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200710073] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
13
|
Current mathematical methods used in QSAR/QSPR studies. Int J Mol Sci 2009; 10:1978-1998. [PMID: 19564933 PMCID: PMC2695261 DOI: 10.3390/ijms10051978] [Citation(s) in RCA: 126] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2009] [Accepted: 04/28/2009] [Indexed: 02/07/2023] Open
Abstract
This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.
Collapse
|
14
|
Lather V, Fernandes M. QSAR Models for Prediction of PPARδ Agonistic Activity of Indanylacetic Acid Derivatives. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200810092] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
15
|
Du J, Qin J, Liu H, Yao X. 3D-QSAR and molecular docking studies of selective agonists for the thyroid hormone receptor beta. J Mol Graph Model 2008; 27:95-104. [PMID: 18436460 DOI: 10.1016/j.jmgm.2008.03.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2007] [Revised: 03/10/2008] [Accepted: 03/10/2008] [Indexed: 11/15/2022]
Abstract
Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) on a series of agonists of thyroid hormone receptor beta (TRbeta), which may lead to safe therapies for non-thyroid disorders while avoiding the cardiac side effects. The reasonable q(2) (cross-validated) values 0.600 and 0.616 and non-cross-validated r(2) values of 0.974 and 0.974 were obtained for CoMFA and CoMSIA models for the training set compounds, respectively. The predictive ability of two models was validated using a test set of 12 molecules which gave predictive correlation coefficients (r(pred)(2)) of 0.688 and 0.674, respectively. The Lamarckian Genetic Algorithm (LGA) of AutoDock 4.0 was employed to explore the binding mode of the compound at the active site of TRbeta. The results not only lead to a better understanding of interactions between these agonists and the thyroid hormone receptor beta but also can provide us some useful information about the influence of structures on the activity which will be very useful for designing some new agonist with desired activity.
Collapse
Affiliation(s)
- Juan Du
- Department of Chemistry, Lanzhou University, Lanzhou 730000, China
| | | | | | | |
Collapse
|