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Sahin K, Saripinar E. A novel hybrid method named electron conformational genetic algorithm as a 4D QSAR investigation to calculate the biological activity of the tetrahydrodibenzazosines. J Comput Chem 2020; 41:1091-1104. [PMID: 32058616 DOI: 10.1002/jcc.26154] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 12/18/2019] [Accepted: 12/18/2019] [Indexed: 11/11/2022]
Abstract
To understand the structure-activity correlation of a group of tetrahydrodibenzazocines as inhibitors of 17β-hydroxysteroid dehydrogenase type 3, we have performed a combined genetic algorithm (GA) and four-dimensional quantitative structure-activity relationship (4D-QSAR) modeling study. The computed electronic and geometry structure descriptors were regulated as a matrix and named as electron-conformational matrix of contiguity (ECMC). A chemical property-based pharmacophore model was developed for series of tetrahydrodibenzazocines by EMRE software package. GA was employed to choose an optimal combination of parameters. A model has been developed for estimating anticancer activity quantitatively. All QSAR models were established with 40 compounds (training set), then they were considered for selective capability with additional nine compounds (test set). A statistically valid 4D-QSAR ( R training 2 = 0.856 , R test 2 = 0.851 and q2 = 0.650) with good external set prediction was obtained.
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Affiliation(s)
- Kader Sahin
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey
| | - Emin Saripinar
- Science Faculty, Department of Chemistry, Erciyes University, Kayseri, Turkey
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Chang H, Zhu L, Lou X, Meng X, Guo Y, Wang Z. Local Strategy Combined with a Wavelength Selection Method for Multivariate Calibration. SENSORS 2016; 16:s16060827. [PMID: 27271636 PMCID: PMC4934253 DOI: 10.3390/s16060827] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 05/30/2016] [Accepted: 05/31/2016] [Indexed: 11/16/2022]
Abstract
One of the essential factors influencing the prediction accuracy of multivariate calibration models is the quality of the calibration data. A local regression strategy, together with a wavelength selection approach, is proposed to build the multivariate calibration models based on partial least squares regression. The local algorithm is applied to create a calibration set of spectra similar to the spectrum of an unknown sample; the synthetic degree of grey relation coefficient is used to evaluate the similarity. A wavelength selection method based on simple-to-use interactive self-modeling mixture analysis minimizes the influence of noisy variables, and the most informative variables of the most similar samples are selected to build the multivariate calibration model based on partial least squares regression. To validate the performance of the proposed method, ultraviolet-visible absorbance spectra of mixed solutions of food coloring analytes in a concentration range of 20-200 µg/mL is measured. Experimental results show that the proposed method can not only enhance the prediction accuracy of the calibration model, but also greatly reduce its complexity.
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Affiliation(s)
- Haitao Chang
- School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, China.
| | - Lianqing Zhu
- Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, China.
| | - Xiaoping Lou
- Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, China.
| | - Xiaochen Meng
- Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, China.
| | - Yangkuan Guo
- Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science & Technology University, Beijing 100192, China.
| | - Zhongyu Wang
- School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, China.
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4
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Kruggel-Emden H, Scherer V. Heat and Mass Flow Control in an Interconnected Multiphase CFD Model for Chemical Looping Combustion. Chem Eng Technol 2011. [DOI: 10.1002/ceat.201100068] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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5
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Riahi S, Mousavi MF, Ganjali MR, Norouzi P. Application of Correlation Ranking Procedure and Artificial Neural Networks in the Modeling of Liquid Chromatographic Retention Times (tR) of Various Pesticides. ANAL LETT 2008. [DOI: 10.1080/00032710802514881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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6
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Rayens W, Liu Y, Andersen A, Smith C. Using OrPLS to Identify Asymptomatic Women at Risk For Alzheimer's Disease. J Chemother 2008; 22:522-527. [PMID: 21340044 PMCID: PMC3039878 DOI: 10.1002/cem.1172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Persons at risk for Alzheimer's disease (AD) demonstrate altered cortical activation measured by functional MRI (fMRI) years before symptoms of disease are expected. We used fMRI to study the differences in cortical activation between 13 women with a family history of AD and at least one apolipoprotein E4 allele, a risk factor for AD, and a control group of 11 women lacking both factors. Our primary goal was to assess how well the two groups are able to be statistically separated, a task which directly affects the performance of post hoc classification. The dimension of the dataset, however, precludes the use of ordinary classification methods. In this paper we show the superiority of using oriented PLS (OrPLS) to accomplish the classification in the presence of this dimensionality problem. We are able to reduce the misclassification rates on the standardized fMRI data from an average of about 48% for PCA, to an average of 27% for PLS, and then to perfect classification for OrPLS.
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Shen Q, Shi WM, Yang XP, Ye BX. Particle swarm algorithm trained neural network for QSAR studies of inhibitors of platelet-derived growth factor receptor phosphorylation. Eur J Pharm Sci 2006; 28:369-76. [PMID: 16713200 DOI: 10.1016/j.ejps.2006.04.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2005] [Revised: 03/29/2006] [Accepted: 04/02/2006] [Indexed: 11/20/2022]
Abstract
The multilayer feed-forward artificial neural network (ANN) has been widely used in QSAR studying. Back-propagation algorithm (BP) and the use of evolutionary search as an ANN training method has some limitations associated with overfitting, local optimum problems and slow convergence rate. In this paper, particle swarm optimization (PSO) as a relatively new optimization technique has been used in ANN training. Compared to ANN trained by BP algorithm and evolutionary search, ANN training by PSO algorithm (PSONN) show satisfactory performance, converges quickly towards the optimal position and can avoid overfitting in some extent. The PSONN has been testified by using in QSAR modeling for inhibitory activity of 4-[4-(N-substituted (thio) carbamoyl)-1-piperazinyl]-6,7-dimethoxyquinazoline derivatives.
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Affiliation(s)
- Qi Shen
- Chemistry Department, Zhengzhou University, Zhengzhou 450052, China.
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8
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Paszkowicz W. Properties of a genetic algorithm extended by a random self-learning operator and asymmetric mutations: A convergence study for a task of powder-pattern indexing. Anal Chim Acta 2006. [DOI: 10.1016/j.aca.2006.02.055] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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9
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Kasprzyk G, Jaskuła M. Application of the hybrid genetic-simplex algorithm for deconvolution of electrochemical responses in SDLSV method. J Electroanal Chem (Lausanne) 2004. [DOI: 10.1016/j.jelechem.2003.11.060] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Shen Q, Jiang JH, Jiao CX, Lin WQ, Shen GL, Yu RQ. Hybridized particle swarm algorithm for adaptive structure training of multilayer feed-forward neural network: QSAR studies of bioactivity of organic compounds. J Comput Chem 2004; 25:1726-35. [PMID: 15362129 DOI: 10.1002/jcc.20094] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The multilayer feed-forward ANN is an important modeling technique used in QSAR studying. The training of ANN is usually carried out only to optimize the weights of the neural network and without paying attention to the network topology. Some other strategies used to train ANN are, first, to discover an optimum structure of the network, and then to find weights for an already defined structure. These methods tend to converge to local optima, and may also lead to overfitting. In this article, a hybridized particle swarm optimization (PSO) approach was applied to the neural network structure training (HPSONN). The continuous version of PSO was used for the weight training of ANN, and the modified discrete PSO was applied to find appropriate the network architecture. The network structure and connectivity are trained simultaneously. The two versions of PSO can jointly search the global optimal ANN architecture and weights. A new objective function is formulated to determine the appropriate network architecture and optimum value of the weights. The proposed HPSONN algorithm was used to predict carcinogenic potency of aromatic amines and biological activity of a series of distamycin and distamycin-like derivatives. The results were compared to those obtained by PSO and GA training in which the network architecture was kept fixed. The comparison demonstrated that the HPSONN is a useful tool for training ANN, which converges quickly towards the optimal position, and can avoid overfitting in some extent.
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Affiliation(s)
- Qi Shen
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China
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Gributs CE, Burns DH. Multiresolution analysis for quantification of optical properties in scattering media using pulsed photon time-of-flight measurements. Anal Chim Acta 2003. [DOI: 10.1016/s0003-2670(03)00534-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Lü Q, Shen G, Yu R. A chaotic approach to maintain the population diversity of genetic algorithm in network training. Comput Biol Chem 2003; 27:363-71. [PMID: 12927110 DOI: 10.1016/s1476-9271(02)00083-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The concept of chaos being radically different from statistical randomness is introduced into chemometrics research. The chaotic system that is deterministic with underlying patterns and inherent ability in searching the space of interest has been employed to improve the performance of chemometric algorithms. In this paper, a chaotic mutation is introduced into the genetic algorithm (GA) which is used for artificial neural network (ANN) training. The chaotic algorithm is very efficient in maintaining the population diversity during the evolution process of GA. The proposed algorithm CGANN has been testified by prediction of vibrational frequencies of octahedral hexahalides from some selected molecular parameters.
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Affiliation(s)
- Qingzhang Lü
- State Key laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, 410082, Changsha, People's Republic of China.
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Gributs CEW, Burns DH. Haar transform analysis of photon time-of-flight measurements for quantification of optical properties in scattering media. APPLIED OPTICS 2003; 42:2923-2930. [PMID: 12790441 DOI: 10.1364/ao.42.002923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
A method to independently quantify the absorption and the scattering properties of samples based on the analysis of the Haar transform (HT) of photon time-of-flight (TOF) distributions is described. A series of reflectance photon TOF measurements were acquired from absorbing/scattering milk samples of known composition (0 < mu(a) < 0.025 mm(-1); 100 < mu(s) < 250 mm(-1)). The HT of the profiles was calculated, and the regression based on the most parsimonious subset of wavelets was determined by the genetic algorithm (GA). In addition, the utility of computing the logarithm of the profiles or of the absolute value of the wavelet coefficients before the GA was studied. Results show that the absorption coefficient could be estimated with a coefficient of variation (C.V.) of 6.7% and an r2 of 0.99 by use of the log of selected wavelets of frequency less than 800 MHz. Scattering coefficients were estimated with a C.V. of 2.3% and an r2 of 0.99 with the log of wavelets of frequency less than 400 MHz. The above results suggest that a simplified instrument based on low-frequency switches could be developed to quantify the optical properties of highly scattering media.
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Affiliation(s)
- Claudia E W Gributs
- Department of Chemistry, McGill University, 801 Sherbrooke Street West, Montréal, Quebec, Canada H3A 2K6
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Lu Q, Shen G, Yu R. Genetic training of network using chaos concept: application to QSAR studies of vibration modes of tetrahedral halides. J Comput Chem 2002; 23:1357-65. [PMID: 12214318 DOI: 10.1002/jcc.10149] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The chaotic dynamical system is introduced in genetic algorithm to train ANN to formulate the CGANN algorithm. Logistic mapping as one of the most important chaotic dynamic mappings provides each new generation a high chance to hold GA's population diversity. This enhances the ability to overcome overfitting in training an ANN. The proposed CGANN has been used for QSAR studies to predict the tetrahedral modes (nu(1)(A1) and nu(2)(E)) of halides [MX(4)](epsilon). The frequencies predicted by QSAR were compared with those calculated by quantum chemistry methods including PM3, AM1, and MNDO/d. The possibility of improving the predictive ability of QSAR by including quantum chemistry parameters as feature variables has been investigated using tetrahedral tetrahalide examples.
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Affiliation(s)
- Qingzhang Lu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China
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