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Alharthi AM, Lee MH, Algamal ZY, Al-Fakih AM. Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:571-583. [PMID: 32628042 DOI: 10.1080/1062936x.2020.1782467] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 06/10/2020] [Indexed: 06/11/2023]
Abstract
One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR classification model estimation. However, penalized methods have drawbacks such as having biases and inconsistencies that make they lack the oracle properties. This paper proposes an adaptive penalized logistic regression (APLR) to overcome these drawbacks. This is done by employing a ratio (BWR) of the descriptors between-groups sum of squares (BSS) to the within-groups sum of squares (WSS) for each descriptor as a weight inside the L1-norm. The proposed method was applied to one dataset that consists of a diverse series of antimicrobial agents with their respective bioactivities against Candida albicans. By experimental study, it has been shown that the proposed method (APLR) was more efficient in the selection of descriptors and classification accuracy than the other competitive methods that could be used in developing QSAR classification models. Another dataset was also successfully experienced. Therefore, it can be concluded that the APLR method had significant impact on QSAR analysis and studies.
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Affiliation(s)
- A M Alharthi
- Department of Mathematical Sciences, Universiti Teknologi Malaysia , Skudai, Malaysia
| | - M H Lee
- Department of Mathematical Sciences, Universiti Teknologi Malaysia , Skudai, Malaysia
| | - Z Y Algamal
- Department of Statistics and Informatics, University of Mosul , Mosul, Iraq
| | - A M Al-Fakih
- Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia , Johor, Malaysia
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Cherdtrakulkiat R, Worachartcheewan A, Tantimavanich S, Lawung R, Sinthupoom N, Prachayasittikul S, Ruchirawat S, Prachayasittikul V. Discovery of novel halogenated 8‐hydroxyquinoline‐based anti‐MRSA agents: In vitro and QSAR studies. Drug Dev Res 2019; 81:127-135. [DOI: 10.1002/ddr.21611] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 09/10/2019] [Accepted: 09/21/2019] [Indexed: 12/18/2022]
Affiliation(s)
- Rungrot Cherdtrakulkiat
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical TechnologyMahidol University Bangkok Thailand
| | - Apilak Worachartcheewan
- Department of Community Medical Technology, Faculty of Medical TechnologyMahidol University Bangkok Thailand
- Department of Clinical Chemistry, Faculty of Medical TechnologyMahidol University Bangkok Thailand
| | - Srisurang Tantimavanich
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical TechnologyMahidol University Bangkok Thailand
| | - Ratana Lawung
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical TechnologyMahidol University Bangkok Thailand
| | - Nujarin Sinthupoom
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical TechnologyMahidol University Bangkok Thailand
| | - Supaluk Prachayasittikul
- Center of Data Mining and Biomedical Informatics, Faculty of Medical TechnologyMahidol University Bangkok Thailand
| | - Somsak Ruchirawat
- Laboratory of Medicinal ChemistryChulabhorn Research Institute Bangkok Thailand
- Program in Chemical BiologyChulabhorn Graduate Institute Bangkok Thailand
- Center of Excellence on Environmental Health and Toxicology, Commission on Higher Education (CHE)Ministry of Education Thailand
| | - Virapong Prachayasittikul
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical TechnologyMahidol University Bangkok Thailand
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Worachartcheewan A, Songtawee N, Siriwong S, Prachayasittikul S, Nantasenamat C, Prachayasittikul V. Rational Design of Colchicine Derivatives as anti-HIV Agents via QSAR and Molecular Docking. Med Chem 2019; 15:328-340. [PMID: 30251609 DOI: 10.2174/1573406414666180924163756] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 08/24/2018] [Accepted: 08/25/2018] [Indexed: 02/08/2023]
Abstract
BACKGROUND Human immunodeficiency virus (HIV) is an infective agent that causes an acquired immunodeficiency syndrome (AIDS). Therefore, the rational design of inhibitors for preventing the progression of the disease is required. OBJECTIVE This study aims to construct quantitative structure-activity relationship (QSAR) models, molecular docking and newly rational design of colchicine and derivatives with anti-HIV activity. METHODS A data set of 24 colchicine and derivatives with anti-HIV activity were employed to develop the QSAR models using machine learning methods (e.g. multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM)), and to study a molecular docking. RESULTS The significant descriptors relating to the anti-HIV activity included JGI2, Mor24u, Gm and R8p+ descriptors. The predictive performance of the models gave acceptable statistical qualities as observed by correlation coefficient (Q2) and root mean square error (RMSE) of leave-one out cross-validation (LOO-CV) and external sets. Particularly, the ANN method outperformed MLR and SVM methods that displayed LOO-CV 2 Q and RMSELOO-CV of 0.7548 and 0.5735 for LOOCV set, and Ext 2 Q of 0.8553 and RMSEExt of 0.6999 for external validation. In addition, the molecular docking of virus-entry molecule (gp120 envelope glycoprotein) revealed the key interacting residues of the protein (cellular receptor, CD4) and the site-moiety preferences of colchicine derivatives as HIV entry inhibitors for binding to HIV structure. Furthermore, newly rational design of colchicine derivatives using informative QSAR and molecular docking was proposed. CONCLUSION These findings serve as a guideline for the rational drug design as well as potential development of novel anti-HIV agents.
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Affiliation(s)
- Apilak Worachartcheewan
- Department of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.,Department of Clinical Chemistry, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.,Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Napat Songtawee
- Department of Clinical Chemistry, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Suphakit Siriwong
- Department of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Supaluk Prachayasittikul
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Virapong Prachayasittikul
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
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Al-Fakih AM, Algamal ZY, Lee MH, Aziz M, Ali HTM. QSAR classification model for diverse series of antifungal agents based on improved binary differential search algorithm. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:131-143. [PMID: 30734580 DOI: 10.1080/1062936x.2019.1568298] [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: 10/31/2018] [Accepted: 01/08/2019] [Indexed: 06/09/2023]
Abstract
An improved binary differential search (improved BDS) algorithm is proposed for QSAR classification of diverse series of antimicrobial compounds against Candida albicans inhibitors. The transfer functions is the most important component of the BDS algorithm, and converts continuous values of the donor into discrete values. In this paper, the eight types of transfer functions are investigated to verify their efficiency in improving BDS algorithm performance in QSAR classification. The performance was evaluated using three metrics: classification accuracy (CA), geometric mean of sensitivity and specificity (G-mean), and area under the curve. The Kruskal-Wallis test was also applied to show the statistical differences between the functions. Two functions, S1 and V4, show the best classification achievement, with a slightly better performance of V4 than S1. The V4 function takes the lowest iterations and selects the fewest descriptors. In addition, the V4 function yields the best CA and G-mean of 98.07% and 0.977%, respectively. The results prove that the V4 transfer function significantly improves the performance of the original BDS.
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Affiliation(s)
- A M Al-Fakih
- a Department of Chemistry , Universiti Teknologi Malaysia , Johor , Malaysia
- b Department of Chemistry , 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 , Universiti Teknologi Malaysia , Johor , Malaysia
| | - M Aziz
- a Department of Chemistry , 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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Prachayasittikul V, Pingaew R, Anuwongcharoen N, Worachartcheewan A, Nantasenamat C, Prachayasittikul S, Ruchirawat S, Prachayasittikul V. Discovery of novel 1,2,3-triazole derivatives as anticancer agents using QSAR and in silico structural modification. SPRINGERPLUS 2015; 4:571. [PMID: 26543706 PMCID: PMC4628044 DOI: 10.1186/s40064-015-1352-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 09/17/2015] [Indexed: 01/17/2023]
Abstract
Considerable attention has been given on the search for novel anticancer drugs with respect to the disease sequelae on human health and well-being. Triazole is considered to be an attractive scaffold possessing diverse biological activities. Structural modification on the privileged structures is noted as an effective strategy towards successful design and development of novel drugs. The quantitative structure–activity relationships (QSAR) is well-known as a powerful computational tool to facilitate the discovery of potential compounds. In this study, a series of thirty-two 1,2,3-triazole derivatives (1–32) together with their experimentally measured cytotoxic activities against four cancer cell lines i.e., HuCCA-1, HepG2, A549 and MOLT-3 were used for
QSAR analysis. Four QSAR models were successfully constructed with acceptable predictive performance affording RCV ranging from 0.5958 to 0.8957 and RMSECV ranging from 0.2070 to 0.4526. An additional set of 64 structurally modified triazole compounds (1A–1R, 2A–2R, 7A–7R and 8A–8R) were constructed in silico and their predicted cytotoxic activities were obtained using the constructed QSAR models. The study suggested crucial moieties and certain properties essential for potent anticancer activity and highlighted a series of promising compounds (21, 28, 32, 1P, 8G, 8N and 8Q) for further development as novel triazole-based anticancer agents.
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Affiliation(s)
- Veda Prachayasittikul
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand ; Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand
| | - Ratchanok Pingaew
- Department of Chemistry, Faculty of Science, Srinakharinwirot University, Bangkok, 10110 Thailand
| | - Nuttapat Anuwongcharoen
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand ; Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand
| | - Apilak Worachartcheewan
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand ; Department of Clinical Chemistry, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand
| | - Supaluk Prachayasittikul
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand
| | - Somsak Ruchirawat
- Laboratory of Medicinal Chemistry, Chulabhorn Research Institute, Bangkok, 10210 Thailand ; Program in Chemical Biology, Chulabhorn Graduate Institute, Bangkok, 10210 Thailand ; Center of Excellence On Environmental Health and Toxicology, Commission On Higher Education (CHE), Ministry of Education, Bangkok, Thailand
| | - Virapong Prachayasittikul
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand
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Nantasenamat C, Prachayasittikul V. Maximizing computational tools for successful drug discovery. Expert Opin Drug Discov 2015; 10:321-9. [PMID: 25693813 DOI: 10.1517/17460441.2015.1016497] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Drug discovery is an iterative cycle of identifying promising hits followed by lead optimization via bioisosteric replacements. In the search for compounds affording good bioactivity, equal importance should also be placed on achieving those with favorable pharmacokinetic properties. Thus, the balance and realization of both key properties is an intricate problem that requires great caution. In this editorial, the authors explore the available computational tools in the context of the extant of big data that has borne out via advents of the Omics revolution. As such, the selection of appropriate computational tools for analyzing the vast number of chemical libraries, target proteins and interactomes is the first step toward maximizing the chance for success. However, in order to realize this, it is also necessary to have a solid foundation on the big concepts of drug discovery as well as knowing which tools are available in order to give drug discovery scientists the best opportunity.
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Affiliation(s)
- Chanin Nantasenamat
- Mahidol University, Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology , 10700 Bangkok , Thailand
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Nantasenamat C, Worachartcheewan A, Jamsak S, Preeyanon L, Shoombuatong W, Simeon S, Mandi P, Isarankura-Na-Ayudhya C, Prachayasittikul V. AutoWeka: toward an automated data mining software for QSAR and QSPR studies. Methods Mol Biol 2015; 1260:119-47. [PMID: 25502379 DOI: 10.1007/978-1-4939-2239-0_8] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
UNLABELLED In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predictive modeling. The latter is based on the concept of quantitative structure-activity/property relationship (QSAR/QSPR) when applied in modeling the biological activity and chemical properties, respectively, of compounds. Data mining is a powerful technology underlying QSAR/QSPR as it harnesses knowledge from large volumes of high-dimensional data via multivariate analysis. Although extremely useful, the technicalities of data mining may overwhelm potential users, especially those in the life sciences. Herein, we aim to lower the barriers to access and utilization of data mining software for QSAR/QSPR studies. AutoWeka is an automated data mining software tool that is powered by the widely used machine learning package Weka. The software provides a user-friendly graphical interface along with an automated parameter search capability. It employs two robust and popular machine learning methods: artificial neural networks and support vector machines. This chapter describes the practical usage of AutoWeka and relevant tools in the development of predictive QSAR/QSPR models. AVAILABILITY The software is freely available at http://www.mt.mahidol.ac.th/autoweka.
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Affiliation(s)
- Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand,
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Nantasenamat C, Monnor T, Worachartcheewan A, Mandi P, Isarankura-Na-Ayudhya C, Prachayasittikul V. Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection. Eur J Med Chem 2014; 76:352-9. [PMID: 24589490 DOI: 10.1016/j.ejmech.2014.02.043] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Revised: 02/12/2014] [Accepted: 02/15/2014] [Indexed: 12/21/2022]
Abstract
This study explores the chemical space and quantitative structure-activity relationship (QSAR) of a set of 60 sulfonylpyridazinones with aldose reductase inhibitory activity. The physicochemical properties of the investigated compounds were described by a total of 3230 descriptors comprising of 6 quantum chemical descriptors and 3224 molecular descriptors. A subset of 5 descriptors was selected from the aforementioned pool by means of Monte Carlo (MC) feature selection coupled to multiple linear regression (MLR). Predictive QSAR models were then constructed by MLR, support vector machine and artificial neural network, which afforded good predictive performance as deduced from internal and external validation. The investigated models are capable of accounting for the origins of aldose reductase inhibitory activity and could be utilized in predicting this property in screening for novel and robust compounds.
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Affiliation(s)
- Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
| | - Teerawat Monnor
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Apilak Worachartcheewan
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Prasit Mandi
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | | | - Virapong Prachayasittikul
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
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Worachartcheewan A, Nantasenamat C, Owasirikul W, Monnor T, Naruepantawart O, Janyapaisarn S, Prachayasittikul S, Prachayasittikul V. Insights into antioxidant activity of 1-adamantylthiopyridine analogs using multiple linear regression. Eur J Med Chem 2014; 73:258-64. [DOI: 10.1016/j.ejmech.2013.11.038] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2013] [Revised: 11/08/2013] [Accepted: 11/24/2013] [Indexed: 01/12/2023]
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