1
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Khan MF, Rashid RB, Rashid MA. Identification of Natural Compounds with Analgesic and Antiinflammatory Properties Using Machine Learning and Molecular Docking Studies. LETT DRUG DES DISCOV 2022. [DOI: 10.2174/1570180818666210728162055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Natural products have been a rich source of compounds for drug discovery. Usually,
compounds obtained from natural sources have little or no side effects, thus searching for new lead
compounds from traditionally used plant species is still a rational strategy.
Introduction:
Natural products serve as a useful repository of compounds for new drugs; however, their
use has been decreasing, in part because of technical barriers to screening natural products in highthroughput
assays against molecular targets. To address this unmet demand, we have developed and validated
a high throughput in silico machine learning screening method to identify potential compounds
from natural sources.
Methods:
In the current study, three machine learning approaches, including Support Vector Machine
(SVM), Random Forest (RF) and Gradient Boosting Machine (GBM) have been applied to develop the
classification model. The model was generated using the cyclooxygenase-2 (COX-2) inhibitors reported
in the ChEMBL database. The developed model was validated by evaluating the accuracy, sensitivity,
specificity, Matthews correlation coefficient and Cohen’s kappa statistic of the test set. The molecular
docking study was conducted on AutoDock vina and the results were analyzed in PyMOL.
Results:
The accuracy of the model for SVM, RF and GBM was found to be 75.40 %, 74.97 % and 74.60
%, respectively, which indicates the good performance of the developed model. Further, the model has
demonstrated good sensitivity (61.25 % - 68.60 %) and excellent specificity (77.72 %- 81.41 %). Application
of the model on the NuBBE database, a repository of natural compounds, led us to identify a natural
compound, enhydrin possessing analgesic and anti-inflammatory activities. The ML methods and the
molecular docking study suggest that enhydrin likely demonstrates its analgesic and anti-inflammatory
actions by inhibiting COX-2.
Conclusion:
Our developed and validated in silico high throughput ML screening methods may assist in
identifying drug-like compounds from natural sources.
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Affiliation(s)
- Mohammad Firoz Khan
- Computational Chemistry and Bioinformatics Laboratory, Department of Pharmacy, State University of Bangladesh,
Dhaka, 1205, Bangladesh
| | - Ridwan Bin Rashid
- Computational Chemistry and Bioinformatics Laboratory, Department of Pharmacy, State University of Bangladesh,
Dhaka, 1205, Bangladesh
| | - Mohammad A. Rashid
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka,
1000, Bangladesh
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2
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Virtual Screening of Antitumor Inhibitors Targeting BRD4 Based on Machine Learning Methods. ChemistrySelect 2022. [DOI: 10.1002/slct.202104054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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3
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Zhou S, Huang G. Some important inhibitors and mechanisms of rheumatoid arthritis. Chem Biol Drug Des 2021; 99:930-943. [PMID: 34942050 DOI: 10.1111/cbdd.14015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/17/2021] [Accepted: 12/21/2021] [Indexed: 11/29/2022]
Abstract
Rheumatoid arthritis is a chronic disease that seriously affects human health and quality of life, and it is one of the main causes of labor loss and disability. Many countries have listed rheumatoid arthritis as one of the national a key diseases to tackle. The pathogenesis of RA in humans is still unknown, and medical researchers believe that the pathogenesis of RA may be the result of a combination of genetic and environmental factors. RA is an incurable condition that can only be controlled and treated with conventional drugs. In this paper, the pathologic features and pathogenesis of RA were introduced, and the research progress of new anti-rheumatoid arthritis chemical drugs in recent years was reviewed.
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Affiliation(s)
- Shiyang Zhou
- Chongqing Chemical Industry Vocational College, Chongqing, 401228, China.,College of Chemistry, Chongqing Normal University, Chongqing, 401331, China
| | - Gangliang Huang
- College of Chemistry, Chongqing Normal University, Chongqing, 401331, China
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4
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Huang J, Dong M, Lu S, Yu Y, Liu C, Yoo JH, Lu J. A hybrid model combining wavelet transform and recursive feature elimination for running state evaluation of heat-resistant steel using laser-induced breakdown spectroscopy. Analyst 2019; 144:3736-3745. [PMID: 30984923 DOI: 10.1039/c9an00370c] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Heat-resistant steel is widely used in various industries, and the running state is of great importance for equipment function and safety. In this work, laser-induced breakdown spectroscopy (LIBS) is applied to evaluate the running state of steel using indicators of micro and macro properties. The hybrid model based on wavelet threshold denoising (WTD) and K-fold-support vector machine-recursive feature elimination (K-SVM-RFE) is proposed to estimate the different indictors of various service conditions of steel. Fourteen T91 specimens, including 4 industrial specimens obtained from different service conditions in the power plant boiler, were used as the analytes. Firstly, the noise signal of the LIBS spectra of each specimen was analyzed and removed with WTD. Secondly, an improved approach K-SVM-RFE was applied to select the optimal feature subset and build the classification models of aging grade and hardness grade. The influence of denoising pretreatment on model performance was compared and discussed. Finally, the assessment matrix, established using the indicators from the aging grade and hardness grade, was used to evaluate the running state of steel. The results show that the test assessment matrix obtained with the hybrid model based on WTD and K-SVM-RFE is consistent with the reference matrix on the running state of steel.
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Affiliation(s)
- Jianwei Huang
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China. and Guangdong Province Engineering Research Center of High Efficiency and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
| | - Meirong Dong
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China. and Guangdong Province Engineering Research Center of High Efficiency and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
| | - Shengzi Lu
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China. and Guangdong Province Engineering Research Center of High Efficiency and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
| | - Yishan Yu
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China. and Guangdong Province Engineering Research Center of High Efficiency and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
| | - Chunyi Liu
- Applied Spectra, Inc., 46665 Fremont Blvd, Fremont, CA 94538, USA
| | - Jong H Yoo
- Applied Spectra, Inc., 46665 Fremont Blvd, Fremont, CA 94538, USA
| | - Jidong Lu
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China. and Guangdong Province Engineering Research Center of High Efficiency and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
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5
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Machine Learning Models Combined with Virtual Screening and Molecular Docking to Predict Human Topoisomerase I Inhibitors. Molecules 2019; 24:molecules24112107. [PMID: 31167344 PMCID: PMC6601036 DOI: 10.3390/molecules24112107] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 05/20/2019] [Accepted: 05/28/2019] [Indexed: 12/15/2022] Open
Abstract
In this work, random forest (RF), support vector machine, k-nearest neighbor and C4.5 decision tree, were used to establish classification models for predicting whether an unknown molecule is an inhibitor of human topoisomerase I (Top1) protein. All these models have achieved satisfactory results, with total prediction accuracies from 89.70% to 97.12%. Through comparative analysis, it can be found that the RF model has the best forecasting effect. The parameters were further optimized to generate the best-performing RF model. At the same time, features selection was implemented to choose properties most relevant to the inhibition of Top1 from 189 molecular descriptors through a special RF procedure. Subsequently, a ligand-based virtual screening was performed from the Maybridge database by the optimal RF model and 596 hits were picked out. Then, 67 molecules with relative probability scores over 0.7 were selected based on the screening results. Next, the 67 molecules above were docked to Top1 using AutoDock Vina. Finally, six top-ranked molecules with binding energies less than −10.0 kcal/mol were screened out and a common backbone, which is entirely different from that of existing Top1 inhibitors reported in the literature, was found.
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6
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Ge QD, Xie C, Zhang H, Tan Y, Wan CW, Wang WJ, Jin TX. Differential Expression of miRNAs in the Hippocampi of Offspring Rats Exposed to Fluorine Combined with Aluminum during the Embryonic Stage and into Adulthood. Biol Trace Elem Res 2019; 189:463-477. [PMID: 30033483 DOI: 10.1007/s12011-018-1445-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 07/12/2018] [Indexed: 11/25/2022]
Abstract
A previous study from our team found that continuous exposure to fluorine combined with aluminum (FA) impaired the neurobehavioral reflexes, spatial learning, and memory of offspring rats. To date, the specific mechanisms for these changes are unclear. Here, high-throughput sequencing was utilized to analyze the microRNA (miRNA) profile of the hippocampi in the offspring of rats exposed to FA during the embryonic stage and into adulthood through tap water supplemented with NaF and AlCl3 at concentrations of (0, 0); (60, 600); (120, 600); and (240, 600) mg/L, respectively. qRT-PCR was performed to validate the reliability of the sequence data. Twenty differentially expressed miRNAs were selected for further analysis using bioinformatics tools. Several genes related to neuromodulation were found to be regulated by miR-10a-5p, miR-34b-5p, and miR-182, which might be harmful to normal nerve function. The protein levels of brain-derived neurotrophic factor (BDNF) and tyrosine receptor kinase B (TrkB) in hippocampus were markedly downregulated. These data suggest that miR-10a-5p, miR-34b-5p, and miR-182 and BDNF-TrkB signaling pathway are involved in mechanisms of hippocampal damage in the offspring of rats exposed to FA. HIGHLIGHTS: • Multiple miRNAs were significantly differentially expressed in offspring rat hippocampus after fluorine combined with aluminum (FA) exposure. • Twenty differentially expressed miRNAs might mediate FA-induced developmental neurotoxicity. • MiR-10a-5p, miR-34b-5p, and miR-182 were closely related to neurotoxic signaling of FA. • The BDNF-TrkB learning and memory-associated pathway was downregulated in the hippocampus after FA exposure.
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Affiliation(s)
- Qi-Di Ge
- Department of Occupational Health and Environmental Hygiene, School of Public Health, Guizhou Medical University, University Town, Guian new district, Guiyang, 550025, Guizhou, China
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, Guizhou, China
| | - Chun Xie
- Department of Occupational Health and Environmental Hygiene, School of Public Health, Guizhou Medical University, University Town, Guian new district, Guiyang, 550025, Guizhou, China.
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, Guizhou, China.
| | - Hua Zhang
- Department of Occupational Health and Environmental Hygiene, School of Public Health, Guizhou Medical University, University Town, Guian new district, Guiyang, 550025, Guizhou, China
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, Guizhou, China
| | - Ying Tan
- Department of Occupational Health and Environmental Hygiene, School of Public Health, Guizhou Medical University, University Town, Guian new district, Guiyang, 550025, Guizhou, China
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, Guizhou, China
| | - Chang-Wu Wan
- Department of Forensic Pathology, School of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Wen-Juan Wang
- Department of Occupational Health and Environmental Hygiene, School of Public Health, Guizhou Medical University, University Town, Guian new district, Guiyang, 550025, Guizhou, China
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, Guizhou, China
| | - Ting-Xu Jin
- Department of Occupational Health and Environmental Hygiene, School of Public Health, Guizhou Medical University, University Town, Guian new district, Guiyang, 550025, Guizhou, China
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, Guizhou, China
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7
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Li B, Hu L, Xue Y, Yang M, Huang L, Zhang Z, Liu J, Deng G. Prediction of matrix metal proteinases-12 inhibitors by machine learning approaches. J Biomol Struct Dyn 2018; 37:2627-2640. [DOI: 10.1080/07391102.2018.1492460] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Bingke Li
- Institute of Functional Molecules, College of Chemistry and Life Science, Chengdu Normal University, Chengdu, China
| | - Li Hu
- Institute of Functional Molecules, College of Chemistry and Life Science, Chengdu Normal University, Chengdu, China
| | - Ying Xue
- Key Lab of Green Chemistry and Technology in Ministry of Education, College of Chemistry, Sichuan University, Chengdu, China
| | - Min Yang
- Institute of Functional Molecules, College of Chemistry and Life Science, Chengdu Normal University, Chengdu, China
| | - Long Huang
- Institute of Functional Molecules, College of Chemistry and Life Science, Chengdu Normal University, Chengdu, China
| | - Zhentao Zhang
- Beijing Key Laboratory of Thermal Science and Technology, Beijing, China
- CAS Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Beijing, China
| | - Jialei Liu
- Beijing Key Laboratory of Thermal Science and Technology, Beijing, China
- CAS Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Beijing, China
| | - Guowei Deng
- Institute of Functional Molecules, College of Chemistry and Life Science, Chengdu Normal University, Chengdu, China
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8
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Discovery of novel Syk/PDGFR-α/c-Kit inhibitors as multi-targeting drugs to treat rheumatoid arthritis. Bioorg Med Chem 2018; 26:4375-4381. [DOI: 10.1016/j.bmc.2018.06.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 06/14/2018] [Accepted: 06/21/2018] [Indexed: 12/19/2022]
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9
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Zhang Q, He S, Wang H, Zhang Y, Lv Z, Wang Y. Structural similarity-based prediction of the potential active ingredients and mechanism of action of traditional Chinese medicine formulations used to anti-aging. JOURNAL OF TRADITIONAL CHINESE MEDICAL SCIENCES 2018. [DOI: 10.1016/j.jtcms.2018.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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10
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Zhang L, Ai HX, Li SM, Qi MY, Zhao J, Zhao Q, Liu HS. Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function. Oncotarget 2017; 8:83142-83154. [PMID: 29137330 PMCID: PMC5669956 DOI: 10.18632/oncotarget.20915] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 08/28/2017] [Indexed: 01/27/2023] Open
Abstract
In recent years, an epidemic of the highly pathogenic avian influenza H7N9 virus has persisted in China, with a high mortality rate. To develop novel anti-influenza therapies, we have constructed a machine-learning-based scoring function (RF-NA-Score) for the effective virtual screening of lead compounds targeting the viral neuraminidase (NA) protein. RF-NA-Score is more accurate than RF-Score, with a root-mean-square error of 1.46, Pearson’s correlation coefficient of 0.707, and Spearman’s rank correlation coefficient of 0.707 in a 5-fold cross-validation study. The performance of RF-NA-Score in a docking-based virtual screening of NA inhibitors was evaluated with a dataset containing 281 NA inhibitors and 322 noninhibitors. Compared with other docking–rescoring virtual screening strategies, rescoring with RF-NA-Score significantly improved the efficiency of virtual screening, and a strategy that averaged the scores given by RF-NA-Score, based on the binding conformations predicted with AutoDock, AutoDock Vina, and LeDock, was shown to be the best strategy. This strategy was then applied to the virtual screening of NA inhibitors in the SPECS database. The 100 selected compounds were tested in an in vitro H7N9 NA inhibition assay, and two compounds with novel scaffolds showed moderate inhibitory activities. These results indicate that RF-NA-Score improves the efficiency of virtual screening for NA inhibitors, and can be used successfully to identify new NA inhibitor scaffolds. Scoring functions specific for other drug targets could also be established with the same method.
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Affiliation(s)
- Li Zhang
- School of Life Science, Liaoning University, Shenyang 110036, China.,Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang 110036, China
| | - Hai-Xin Ai
- School of Life Science, Liaoning University, Shenyang 110036, China.,Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang 110036, China.,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang 110036, China
| | - Shi-Meng Li
- School of Life Science, Liaoning University, Shenyang 110036, China
| | - Meng-Yuan Qi
- School of Life Science, Liaoning University, Shenyang 110036, China
| | - Jian Zhao
- School of Life Science, Liaoning University, Shenyang 110036, China
| | - Qi Zhao
- School of Mathematics, Liaoning University, Shenyang 110036, China
| | - Hong-Sheng Liu
- School of Life Science, Liaoning University, Shenyang 110036, China.,Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang 110036, China.,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang 110036, China
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11
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Sanz J, Paternain D, Galar M, Fernandez J, Reyero D, Belzunegui T. A new survival status prediction system for severe trauma patients based on a multiple classifier system. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 142:1-8. [PMID: 28325437 DOI: 10.1016/j.cmpb.2017.02.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Revised: 01/12/2017] [Accepted: 02/10/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Severe trauma patients are those who have several injuries implying a death risk. Prediction systems consider the severity of these injuries to predict whether the patients are likely to survive or not. These systems allow one to objectively compare the quality of the emergency services of trauma centres across different hospitals. However, even the most accurate existing prediction systems are based on the usage of a single model. The aim of this paper is to combine several models to make the prediction, since this methodology usually improves the performance of single models. MATERIALS AND METHODS The two currently used prediction systems by the Hospital of Navarre, which are based on logistic regression models, besides the C4.5 decision tree are combined to conform our proposed multiple classifier system. The quality of the method is tested using the major trauma registry of Navarre, which stores information of 462 trauma patients. A 10x10-fold cross-validation model is applied using as performance measures the specificity, sensitivity and the geometric mean between the two former ones. The results are supported by the usage of the Mann-Whitney's U statistical test. RESULTS The proposed method provides 0.8908, 0.6703 and 0.7661 for sensitivity, specificity and geometric mean, respectively. It slightly decreases the sensitivity of the currently used systems but it notably increases the specificity, which implies a large enhancement on the geometric mean. The same behaviour is found when it is compared versus four classical ensemble approaches and the random forest. The statistical analysis supports the quality of our proposal, since the obtained p-values are less than 0.01 in all the cases. CONCLUSIONS The obtained results show that the multiple classifier systems is the best choice among the considered methods to obtain a trade-off between sensitivity and specificity.
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Affiliation(s)
- José Sanz
- Departamento de Automatica y Computacion and Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, P.O. Box 31006, Pamplona, Spain.
| | - Daniel Paternain
- Departamento de Automatica y Computacion and Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, P.O. Box 31006, Pamplona, Spain
| | - Mikel Galar
- Departamento de Automatica y Computacion and Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, P.O. Box 31006, Pamplona, Spain
| | - Javier Fernandez
- Departamento de Automatica y Computacion and Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, P.O. Box 31006, Pamplona, Spain
| | - Diego Reyero
- Prehospital Emergency, Navarre Health Services, Pamplona, Spain
| | - Tomás Belzunegui
- Department of Health, Universidad Publica de Navarra, Barañaín Avenue s/n, P.O. Box 31008, Pamplona, Spain; Accident and Emergency Department, Hospital of Navarre, Navarre, Spain
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12
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch002] [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
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
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Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
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13
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Umeyama H, Iwadate M, Taguchi YH. <i>In silico</i> Spleen Tyrosine Kinase Inhibitor Screening by ChooseLD. IPSJ TRANSACTIONS ON BIOINFORMATICS 2015. [DOI: 10.2197/ipsjtbio.8.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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14
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS IN DRUG DESIGN, PREDICTIVE TOXICOLOGY, AND RISK ASSESSMENT 2015. [DOI: 10.4018/978-1-4666-8136-1.ch001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
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Affiliation(s)
- Maryam Hamzeh-Mivehroud
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Siavoush Dastmalchi
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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