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Wei Y, Sun Y, Jia S, Yan P, Xiong C, Qi M, Wang C, Du Z, Jiang H. Identification of endogenous carbonyl steroids in human serum by chemical derivatization, hydrogen/deuterium exchange mass spectrometry and the quantitative structure-retention relationship. J Chromatogr B Analyt Technol Biomed Life Sci 2023; 1226:123776. [PMID: 37311272 DOI: 10.1016/j.jchromb.2023.123776] [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: 04/06/2023] [Revised: 05/09/2023] [Accepted: 05/30/2023] [Indexed: 06/15/2023]
Abstract
Steroids are tetracyclic aliphatic compounds, and most of them contain carbonyl groups. The disordered homeostasis of steroids is closely related to the occurrence and progression of various diseases. Due to high structural similarity, low concentrations in vivo, poor ionization efficiency, and interference from endogenous substances, it is very challenging to comprehensively and unambiguously identify endogenous steroids in biological matrix. Herein, an integrated strategy was developed for the characterization of endogenous steroids in serum based on chemical derivatization, ultra-performance liquid chromatography quadrupole Exactive mass spectrometry (UPLC-Q-Exactive-MS/MS), hydrogen/deuterium (H/D) exchange, and a quantitative structure-retention relationship (QSRR) model. To enhance the mass spectrometry (MS) response of carbonyl steroids, the ketonic carbonyl group was derivatized by Girard T (GT). Firstly, the fragmentation rules of derivatized carbonyl steroid standards by GT were summarized. Then, carbonyl steroids in serum were derivatized by GT and identified based on the fragmentation rules or by comparing retention time and MS/MS spectra with those of standards. H/D exchange MS was utilized to distinguish derivatized steroid isomers for the first time. Finally, a QSRR model was constructed to predict the retention time of the unknown steroid derivatives. With this strategy, 93 carbonyl steroids were identified from human serum, and 30 of them were determined to be dicarbonyl steroids by the charge number of characteristic ions and the number of exchangeable hrdrogen or comparing with standards. The QSRR model built by the machine learning algorithms has an excellent regression correlation, thus the accurate structures of 14 carbonyl steroids were determined, among which three steroids were reported for the first time in human serum. This study provides a new analytical method for the comprehensive and reliable identification of carbonyl steroids in biological matrix.
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Affiliation(s)
- Yinyu Wei
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yi Sun
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shuailong Jia
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030 Wuhan, China
| | - Pan Yan
- Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha 410028, China
| | - Chaomei Xiong
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Meiling Qi
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Chenxi Wang
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhifeng Du
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Hongliang Jiang
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.
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2
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Fouad MA, Serag A, Tolba EH, El-Shal MA, El Kerdawy AM. QSRR modeling of the chromatographic retention behavior of some quinolone and sulfonamide antibacterial agents using firefly algorithm coupled to support vector machine. BMC Chem 2022; 16:85. [PMID: 36329493 PMCID: PMC9635186 DOI: 10.1186/s13065-022-00874-2] [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: 05/03/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022] Open
Abstract
Quinolone and sulfonamide are two classes of antibacterial agents with an opulent history of medicinal chemistry features that contribute to their bacterial spectrum, efficacy, pharmacokinetics, and adverse effect profiles. The urgent need for their use, combined with the escalating rate of their resistance, necessitates the development of suitable analytical methods that accelerate and facilitate their analysis. In this study, the advanced firefly algorithm (FFA) coupled with support vector regression (SVR) was used to select the most significant descriptors and to construct two quantitative structure-retention relationship (QSRR) models using a series of 11 selected quinolone and 13 sulfonamide drugs, respectively, to predict their retention behavior in HPLC. Precisely, the effect of the pH value and acetonitrile composition in the mobile phase on the retention behavior of quinolones and sulfonamides, respectively, were studied. The obtained QSRR models performed well in both internal and external validations, demonstrating their robustness and predictive ability. Y-randomization validation demonstrated that the obtained models did not result by statistical chance. Moreover, the obtained results shed the light on the molecular features that influence the retention behavior of these two classes under the current chromatographic conditions.
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Affiliation(s)
- Marwa A. Fouad
- grid.7776.10000 0004 0639 9286Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St, P.O. Box 11562, Cairo, Egypt ,Department of Pharmaceutical Chemistry, School of Pharmacy, Newgiza University (NGU), Newgiza, km 22 Cairo–Alexandria Desert Road, Cairo, Egypt
| | - Ahmed Serag
- grid.411303.40000 0001 2155 6022Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Cairo, Egypt
| | - Enas H. Tolba
- grid.419698.bEgyptian Drug Authority (Former National Organization for Drug Control and Research), Cairo, Egypt
| | - Manal A. El-Shal
- grid.419698.bEgyptian Drug Authority (Former National Organization for Drug Control and Research), Cairo, Egypt
| | - Ahmed M. El Kerdawy
- grid.7776.10000 0004 0639 9286Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St, P.O. Box 11562, Cairo, Egypt
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3
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Ewees AA, Al-qaness MAA, Abualigah L, Algamal ZY, Oliva D, Yousri D, Elaziz MA. Enhanced feature selection technique using slime mould algorithm: a case study on chemical data. Neural Comput Appl 2022; 35:3307-3324. [PMID: 36245794 PMCID: PMC9547998 DOI: 10.1007/s00521-022-07852-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 09/16/2022] [Indexed: 01/31/2023]
Abstract
Feature selection techniques are considered one of the most important preprocessing steps, which has the most significant influence on the performance of data analysis and decision making. These FS techniques aim to achieve several objectives (such as reducing classification error and minimizing the number of features) at the same time to increase the classification rate. FS based on Metaheuristic (MH) is considered one of the most promising techniques to improve the classification process. This paper presents a modified method of the Slime mould algorithm depending on the Marine Predators Algorithm (MPA) operators as a local search strategy, which leads to increasing the convergence rate of the developed method, named SMAMPA and avoiding the attraction to local optima. The efficiency of SMAMPA is evaluated using twenty datasets and compared its results with the state-of-the-art FS methods. In addition, the applicability of SMAMPA to work with real-world problems is evaluated by using it as a quantitative structure-activity relationship (QSAR) model. The obtained results show the high ability of the developed SMAMPA method to reduce the dimension of the tested datasets by increasing the prediction rate. In addition, it provides results better than other FS techniques in terms of performance metrics.
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Affiliation(s)
- Ahmed A. Ewees
- Department of Information Systems, College of Computing and Information Technology, University of Bisha, Bisha, 61922 Saudi Arabia
- Department of Computer, Damietta University, Damietta, 34517 Egypt
| | - Mohammed A. A. Al-qaness
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004 China
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
- Faculty of Information Technology, Middle East University, Amman, 11831 Jordan
| | - Zakariya Yahya Algamal
- Department of Statistics and Informatics, University of Mosul, Mosul, Iraq
- College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq
| | - Diego Oliva
- Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal Mexico
| | - Dalia Yousri
- Department of Electrical Engineering, Faculty of Engineering, Fayoum University, Fayoum, Egypt
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519 Egypt
- Faculty of Computer Science and Engineering, Galala University, Suez, Egypt
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, UAE
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
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4
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Azadifar S, Rostami M, Berahmand K, Moradi P, Oussalah M. Graph-based relevancy-redundancy gene selection method for cancer diagnosis. Comput Biol Med 2022; 147:105766. [PMID: 35779479 DOI: 10.1016/j.compbiomed.2022.105766] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 06/12/2022] [Accepted: 06/18/2022] [Indexed: 11/26/2022]
Abstract
Nowadays, microarray data processing is one of the most important applications in molecular biology for cancer diagnosis. A major task in microarray data processing is gene selection, which aims to find a subset of genes with the least inner similarity and most relevant to the target class. Removing unnecessary, redundant, or noisy data reduces the data dimensionality. This research advocates a graph theoretic-based gene selection method for cancer diagnosis. Both unsupervised and supervised modes use well-known and successful social network approaches such as the maximum weighted clique criterion and edge centrality to rank genes. The suggested technique has two goals: (i) to maximize the relevancy of the chosen genes with the target class and (ii) to reduce their inner redundancy. A maximum weighted clique is chosen in a repetitive way in each iteration of this procedure. The appropriate genes are then chosen from among the existing features in this maximum clique using edge centrality and gene relevance. In the experiment, several datasets consisting of Colon, Leukemia, SRBCT, Prostate Tumor, and Lung Cancer, with different properties, are used to demonstrate the efficacy of the developed model. Our performance is compared to that of renowned filter-based gene selection approaches for cancer diagnosis whose results demonstrate a clear superiority.
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Affiliation(s)
- Saeid Azadifar
- Department of Computer Engineering, University of Khajeh Nasir Toosi, Tehran, Iran
| | - Mehrdad Rostami
- Centre for Machine Vision and Signal Processing, University of Oulu, Oulu, Finland.
| | - Kamal Berahmand
- School of Computer Science, Faculty of Science, Queensland University of Technology (QUT), Brisbane, Australia
| | - Parham Moradi
- Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
| | - Mourad Oussalah
- Centre for Machine Vision and Signal Processing, University of Oulu, Oulu, Finland; Research Unit of Medical Imaging, Physics, and Technology, Faculty of Medicine, University of Oulu, Finland
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5
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Al-Fakih AM, Algamal ZY, Qasim MK. An improved opposition-based crow search algorithm for biodegradable material classification. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:403-415. [PMID: 35469528 DOI: 10.1080/1062936x.2022.2064546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/05/2022] [Indexed: 06/14/2023]
Abstract
The development of a reliable quantitative structure-activity relationship (QSAR) classification model with a small number of molecular descriptors is a crucial step in chemometrics. In this study, an improvement of crow search algorithm (CSA) is proposed by adapting the opposite-based learning (OBL) approach, which is named as OBL-CSA, to improve the exploration and exploitation capability of the CSA in quantitative structure-biodegradation relationship (QSBR) modelling of classifying the biodegradable materials. The results reveal that the performance of OBL-CSA not only manifest in improving the classification performance, but also in reduced computational time required to complete the process when compared to the standard CSA and other four optimization algorithms tested, which are the particle swarm algorithm (PSO), black hole algorithm (BHA), grey wolf algorithm (GWA), and whale optimization algorithm (WOA). In conclusion, the OBL-CSA could be a valuable resource in the classification of biodegradable materials.
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Affiliation(s)
- A M Al-Fakih
- Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia and Department of Chemistry, Faculty of Science, Sana'a University, Sana'a, Yemen
| | - Z Y Algamal
- Department of Statistics and Informatics, University of Mosul, Mosul, Iraq
| | - M K Qasim
- Department of General Science, University of Mosul, Mosul, Iraq
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6
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Abd Elaziz M, Abu-Donia HM, Hosny RA, Hazae SL, Ibrahim RA. Improved evolutionary-based feature selection technique using extension of knowledge based on the rough approximations. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.01.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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7
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Sun J, Liu Y, Yi B, Shu M, Zhang Z, Lin Z. Discovery of Multi‐Targets Neuraminidase Inhibitor Lead Compound Against Influenza H1N1 Virus A/WSN/33 Based on QSAR, Docking, Dynamics Simulation and Network Pharmacology. ChemistrySelect 2022. [DOI: 10.1002/slct.202103962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Jiaying Sun
- School of Pharmacy and Bioengineering Chongqing University of Technology Chongqing 400054 China
| | - Yaru Liu
- School of Pharmacy and Bioengineering Chongqing University of Technology Chongqing 400054 China
| | - Bingxiang Yi
- School of Pharmacy and Bioengineering Chongqing University of Technology Chongqing 400054 China
| | - Mao Shu
- School of Pharmacy and Bioengineering Chongqing University of Technology Chongqing 400054 China
| | - Zhiping Zhang
- ENG. Zhiping Zhang Chongqing Ruepeak Pharmaceutical Co., Ltd Chongqing 400054 China
| | - Zhihua Lin
- School of Pharmacy and Bioengineering Chongqing University of Technology Chongqing 400054 China
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8
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A multi-leader Harris hawk optimization based on differential evolution for feature selection and prediction influenza viruses H1N1. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10075-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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9
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Automatic selection of heavy-tailed distributions-based synergy Henry gas solubility and Harris hawk optimizer for feature selection: case study drug design and discovery. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10009-z] [Citation(s) in RCA: 3] [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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10
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Algamal ZY, Qasim MK, Lee MH, Ali HTM. QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:803-814. [PMID: 32938208 DOI: 10.1080/1062936x.2020.1818616] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 08/31/2020] [Indexed: 06/11/2023]
Abstract
High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.
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Affiliation(s)
- Z Y Algamal
- Department of Statistics and Informatics, University of Mosul , Mosul, Iraq
| | - M K Qasim
- Department of General Science, University of Mosul , Mosul, Iraq
| | - M H Lee
- Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia , Johor, Malaysia
| | - H T M Ali
- College of Computers and Information Technology, Nawroz University , Dahuk, Iraq
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11
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Basile AO, Yahi A, Tatonetti NP. Artificial Intelligence for Drug Toxicity and Safety. Trends Pharmacol Sci 2019; 40:624-635. [PMID: 31383376 PMCID: PMC6710127 DOI: 10.1016/j.tips.2019.07.005] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 07/10/2019] [Accepted: 07/10/2019] [Indexed: 12/13/2022]
Abstract
Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and prevents adverse drug reactions (ADRs). Safety efforts begin during the development process, using in vivo and in vitro studies, continue through clinical trials, and extend to postmarketing surveillance of ADRs in real-world populations. Future toxicity and safety challenges, including increased polypharmacy and patient diversity, stress the limits of these traditional tools. Massive amounts of newly available data present an opportunity for using artificial intelligence (AI) and machine learning to improve drug safety science. Here, we explore recent advances as applied to preclinical drug safety and postmarketing surveillance with a specific focus on machine and deep learning (DL) approaches.
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Affiliation(s)
- Anna O Basile
- Columbia University Medical Center, New York, NY, USA
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12
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Xia Y, Zhang H. 13C NMR chemical shift prediction of diverse chemical compounds. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:477-490. [PMID: 31155931 DOI: 10.1080/1062936x.2019.1619621] [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: 02/14/2019] [Accepted: 05/13/2019] [Indexed: 06/09/2023]
Abstract
Selection of key descriptors is very important in QSPR analysis. Presence of noise in the subset of descriptors reduces the quality of predictions. A complete set is considered as perfect when it does not include irrelevant or redundant elements. This paper reports complete sets of descriptors used to develop QSPR models for 1786 13C NMR chemical shifts (δC parameters) of carbon atoms in 125 diverse chemical compounds. PBE1PBE/6-311G(2d,2p) and B3LYP/6-31G(d) basis sets were used for quantum chemistry calculations after the molecular structures were optimized with semi-empirical AM1 and B3LYP/6-31G(d). The two complete sets consisting of magnetic shielding elements (σXX, σYY, σZZ) and the chemical shift principal values (σ11, σ22, σ33) were used as the inputs for support vector machine (SVM) models of δC parameters. The four SVM models obtained have the mean root mean square (rms) errors of about 4.5-4.6 ppm. The results suggest that SVM models are accurate and acceptable compared with previous models, although our models are based on a relatively large set of compounds. Our approach is valuable in the selection of important descriptors for QSPR studies of δC parameters.
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Affiliation(s)
- Y Xia
- a China Key Laboratory of Advanced Packaging Materials and Technology of Hunan Province, School of Packaging and Materials Engineering , Hunan University of Technology , Zhuzhou , China
| | - H Zhang
- b Chinese Mechanical Engineering Society , Beijing , China
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Al-Fakih AM, Algamal ZY, Lee MH, Aziz M, Ali HTM. A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:403-416. [PMID: 31122062 DOI: 10.1080/1062936x.2019.1607899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 04/11/2019] [Indexed: 06/09/2023]
Abstract
Time-varying binary gravitational search algorithm (TVBGSA) is proposed for predicting antidiabetic activity of 134 dipeptidyl peptidase-IV (DPP-IV) inhibitors. To improve the performance of the binary gravitational search algorithm (BGSA) method, we propose a dynamic time-varying transfer function. A new control parameter, μ , is added in the original transfer function as a time-varying variable. The TVBGSA-based model was internally and externally validated based on Qint2 , QLGO2 , QBoot2 , MSEtrain , Qext2 , MSEtest , Y-randomization test, and applicability domain evaluation. The validation results indicate that the proposed TVBGSA model is robust and not due to chance correlation. The descriptor selection and prediction performance of TVBGSA outperform BGSA method. TVBGSA shows higher Qint2 of 0.957, QLGO2 of 0.951, QBoot2 of 0.954, Qext2 of 0.938, and lower MSEtrain and MSEtest compared to obtained results by BGSA, indicating the best prediction performance of the proposed TVBGSA model. The results clearly reveal that the proposed TVBGSA method is useful for constructing reliable and robust QSARs for predicting antidiabetic activity of DPP-IV inhibitors prior to designing and experimental synthesizing of new DPP-IV inhibitors.
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Affiliation(s)
- A M Al-Fakih
- a Department of Chemistry, Faculty of Science , Universiti Teknologi Malaysia , Johor , Malaysia
- b Department of Chemistry, Faculty of Science , Sana'a University , Sana'a , Yemen
| | - Z Y Algamal
- c Department of Statistics and Informatics , University of Mosul , Mosul , Iraq
| | - M H Lee
- d Department of Mathematical Sciences, Faculty of Science , Universiti Teknologi Malaysia , Johor , Malaysia
| | - M Aziz
- a Department of Chemistry, Faculty of Science , Universiti Teknologi Malaysia , Johor , Malaysia
- e Advanced Membrane Technology Centre , Universiti Teknologi Malaysia , Johor , Malaysia
| | - H T M Ali
- f College of Computers and Information Technology , Nawroz University , Kurdistan region , Iraq
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