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Lu H, Liu W, Yang F, Zhou H, Liu F, Yuan H, Chen G, Jiao Y. Thermal Conductivity Estimation of Diverse Liquid Aliphatic Oxygen-Containing Organic Compounds Using the Quantitative Structure-Property Relationship Method. ACS OMEGA 2020; 5:8534-8542. [PMID: 32337414 PMCID: PMC7178330 DOI: 10.1021/acsomega.9b04190] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 03/31/2020] [Indexed: 06/11/2023]
Abstract
Thermal conductivity is an essential thermodynamic data in chemical engineering applications. Liquid aliphatic oxygen-containing organic compounds are important organic intermediates and raw materials. As a result, estimating thermal conductivity of liquid aliphatic oxygen-containing organic compounds is of significance in industry production. In this study, the genetic function approximation method was applied to screen descriptors and develop a 6-descriptor linear quantitative structure-property relationship model. The entire data set of these compounds covering 1064 thermal conductivity values was divided into 694-member training set, 298-member test set, and 72-member prediction set. The average absolute relative deviation of the training set, test set, and prediction set were 4.14, 4.41, and 4.16%, respectively. Model validation and Y-randomization test proved that the developed model has goodness-of-fit, predictive power, and robustness. In addition, the applicability domain of the developed model was visualized by the Williams plot. This study can provide a convenient method to estimate the thermal conductivity for researchers in chemical engineering production.
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Affiliation(s)
- Haixia Lu
- School of Chemistry and Chemical Engineering,
Key Laboratory of Theoretical Organic Chemistry and Function Molecule
of Ministry of Education, Hunan Province College Key Laboratory of
QSAR/QSPR, Hunan Provincial Key Laboratory of Controllable Preparation
and Functional Application of Fine Polymers, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Wanqiang Liu
- School of Chemistry and Chemical Engineering,
Key Laboratory of Theoretical Organic Chemistry and Function Molecule
of Ministry of Education, Hunan Province College Key Laboratory of
QSAR/QSPR, Hunan Provincial Key Laboratory of Controllable Preparation
and Functional Application of Fine Polymers, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Fan Yang
- School of Chemistry and Chemical Engineering,
Key Laboratory of Theoretical Organic Chemistry and Function Molecule
of Ministry of Education, Hunan Province College Key Laboratory of
QSAR/QSPR, Hunan Provincial Key Laboratory of Controllable Preparation
and Functional Application of Fine Polymers, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Hu Zhou
- School of Chemistry and Chemical Engineering,
Key Laboratory of Theoretical Organic Chemistry and Function Molecule
of Ministry of Education, Hunan Province College Key Laboratory of
QSAR/QSPR, Hunan Provincial Key Laboratory of Controllable Preparation
and Functional Application of Fine Polymers, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Fengping Liu
- School of Chemistry and Chemical Engineering,
Key Laboratory of Theoretical Organic Chemistry and Function Molecule
of Ministry of Education, Hunan Province College Key Laboratory of
QSAR/QSPR, Hunan Provincial Key Laboratory of Controllable Preparation
and Functional Application of Fine Polymers, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Hua Yuan
- School of Chemistry and Chemical Engineering,
Key Laboratory of Theoretical Organic Chemistry and Function Molecule
of Ministry of Education, Hunan Province College Key Laboratory of
QSAR/QSPR, Hunan Provincial Key Laboratory of Controllable Preparation
and Functional Application of Fine Polymers, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Guanfan Chen
- School of Chemistry and Chemical Engineering,
Key Laboratory of Theoretical Organic Chemistry and Function Molecule
of Ministry of Education, Hunan Province College Key Laboratory of
QSAR/QSPR, Hunan Provincial Key Laboratory of Controllable Preparation
and Functional Application of Fine Polymers, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Yinchun Jiao
- School of Chemistry and Chemical Engineering,
Key Laboratory of Theoretical Organic Chemistry and Function Molecule
of Ministry of Education, Hunan Province College Key Laboratory of
QSAR/QSPR, Hunan Provincial Key Laboratory of Controllable Preparation
and Functional Application of Fine Polymers, Hunan University of Science and Technology, Xiangtan 411201, China
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Hu S, Chen P, Gu P, Wang B. A Deep Learning-Based Chemical System for QSAR Prediction. IEEE J Biomed Health Inform 2020; 24:3020-3028. [PMID: 32142459 DOI: 10.1109/jbhi.2020.2977009] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Research on quantitative structure-activity relationships (QSAR) provides an effective approach to determine new hits and promising lead compounds during drug discovery. In the past decades, various works have gained good performance for QSAR with the development of machine learning. The rise of deep learning, along with massive accessible chemical databases, made improvement on the QSAR performance. This article proposes a novel deep-learning-based method to implement QSAR prediction by the concatenation of end-to-end encoder-decoder model and convolutional neural network (CNN) architecture. The encoder-decoder model is mainly used to generate fixed-size latent features to represent chemical molecules; while these features are then input into CNN framework to train a robust and stable model and finally to predict active chemicals. Two models with different schemes are investigated to evaluate the validity of our proposed model on the same data sets. Experimental results showed that our proposed method outperforms other state-of-the-art methods in successful identification of chemical molecule whether it is active.
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Measurement and correlation of isobaric binary vapor-liquid equilibria for water and 2-propanol each with 1-butyl-1-methypyrrolidinium chloride and 1-butyl-1-methylpyrrolidinium trifluoromethanesulfonate. J Mol Liq 2018. [DOI: 10.1016/j.molliq.2018.05.089] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8071121] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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6
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Dadmohammadi Y, Gebreyohannes S, Neely BJ, Gasem KAM. Application of Modified NRTL Models for Binary LLE Phase Characterization. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b00683] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Younas Dadmohammadi
- Oklahoma State University School of Chemical Engineering, Stillwater, Oklahoma 74078-0537, United States
| | - Solomon Gebreyohannes
- Oklahoma State University School of Chemical Engineering, Stillwater, Oklahoma 74078-0537, United States
| | - Brian J. Neely
- Oklahoma State University School of Chemical Engineering, Stillwater, Oklahoma 74078-0537, United States
| | - Khaled A. M. Gasem
- University of Wyoming Departments of Chemical & Petroleum Engineering, Laramie, Wyoming 82071, United States
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Li M, Zhang H, Chen B, Wu Y, Guan L. Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods. Sci Rep 2018; 8:3991. [PMID: 29507318 PMCID: PMC5838250 DOI: 10.1038/s41598-018-22332-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 02/21/2018] [Indexed: 11/23/2022] Open
Abstract
The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.
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Affiliation(s)
- Mengshan Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China.
| | - Huaijing Zhang
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Bingsheng Chen
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Yan Wu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Lixin Guan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
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