Classification of 5-HT(1A) receptor ligands on the basis of their binding affinities by using PSO-Adaboost-SVM.

Zhengjun Cheng, Yuntao Zhang, Changhong Zhou, Wenjun Zhang, Shibo Gao

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Article in International journal of molecular sciences, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor.Pharmaceutics · 2021
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5 authors.

Zhengjun ChengInstitute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China.

Yuntao ZhangInstitute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China.

Changhong ZhouInstitute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China.

Wenjun ZhangInstitute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China.

Shibo GaoInstitute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT(1A) selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies.

Indexed as

AlgorithmsLigandsKineticsModels, MolecularProtein BindingQuantitative Structure-Activity RelationshipReceptor, Serotonin, 5-HT1ASupport Vector MachineLigandsReceptor, Serotonin, 5-HT1A5-HT1A selective ligandsAdaboost-SVMclassificationparticle swarm optimizationtopological descriptor

Identifiers

PMID20111683

DOI10.3390/ijms10083316

PMCPMC2812826

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.