Literature DB >> 26911561

QSAR study on the antimalarial activity of Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) inhibitors.

X Hou1, X Chen1, M Zhang1, A Yan1,2.   

Abstract

Plasmodium falciparum, the most fatal parasite that causes malaria, is responsible for over one million deaths per year. P. falciparum dihydroorotate dehydrogenase (PfDHODH) has been validated as a promising drug development target for antimalarial therapy since it catalyzes the rate-limiting step for DNA and RNA biosynthesis. In this study, we investigated the quantitative structure-activity relationships (QSAR) of the antimalarial activity of PfDHODH inhibitors by generating four computational models using a multilinear regression (MLR) and a support vector machine (SVM) based on a dataset of 255 PfDHODH inhibitors. All the models display good prediction quality with a leave-one-out q(2) >0.66, a correlation coefficient (r) >0.85 on both training sets and test sets, and a mean square error (MSE) <0.32 on training sets and <0.37 on test sets, respectively. The study indicated that the hydrogen bonding ability, atom polarizabilities and ring complexity are predominant factors for inhibitors' antimalarial activity. The models are capable of predicting inhibitors' antimalarial activity and the molecular descriptors for building the models could be helpful in the development of new antimalarial drugs.

Entities:  

Keywords:  Kohonen’s self-organizing map; Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH); Quantitative structure–activity relationships (QSAR); multilinear regression; support vector machine

Mesh:

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Year:  2016        PMID: 26911561     DOI: 10.1080/1062936X.2015.1134652

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  2 in total

1.  A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters.

Authors:  Li Wen; Qing Li; Wei Li; Qiao Cai; Yong-Ming Cai
Journal:  Bioinorg Chem Appl       Date:  2017-07-03       Impact factor: 7.778

2.  Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum.

Authors:  Medard Edmund Mswahili; Gati Lother Martin; Jiyoung Woo; Guang J Choi; Young-Seob Jeong
Journal:  Biomolecules       Date:  2021-11-24
  2 in total

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