Literature DB >> 29886824

QSAR Studies of Halogenated Pyrimidine Derivatives as Inhibitors of Human Dihydroorotate Dehydrogenase Using Modified Bee Algorithm.

Hossein Atabati1, Kobra Zarei2, Hamid Reza Zare-Mehrjardi1.   

Abstract

AIM AND
OBJECTIVE: Human dihydroorotate dehydrogenase (DHODH) catalyzes the fourth stage of the biosynthesis of pyrimidines in cells. Hence it is important to identify suitable inhibitors of DHODH to prevent virus replication. In this study, a quantitative structure-activity relationship was performed to predict the activity of one group of newly synthesized halogenated pyrimidine derivatives as inhibitors of DHODH.
MATERIALS AND METHODS: Molecular structures of halogenated pyrimidine derivatives were drawn in the HyperChem and then molecular descriptors were calculated by DRAGON software. Finally, the most effective descriptors for 32 halogenated pyrimidine derivatives were selected using bee algorithm.
RESULTS: The selected descriptors using bee algorithm were applied for modeling. The mean relative error and correlation coefficient were obtained as 2.86% and 0.9627, respectively, while these amounts for the leave one out-cross validation method were calculated as 4.18% and 0.9297, respectively. The external validation was also conducted using two training and test sets. The correlation coefficients for the training and test sets were obtained as 0.9596 and 0.9185, respectively.
CONCLUSION: The results of modeling of present work showed that bee algorithm has good performance for variable selection in QSAR studies and its results were better than the constructed model with the selected descriptors using the genetic algorithm method. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Entities:  

Keywords:  Bee algorithm; Leave-one-out-cross-validation; dihydroorotate dehydrogenase; halogenated pyrimidine derivatives; quantitative structure-property relationship; variable selectionzzm321990method.

Year:  2018        PMID: 29886824     DOI: 10.2174/1386207321666180611092540

Source DB:  PubMed          Journal:  Comb Chem High Throughput Screen        ISSN: 1386-2073            Impact factor:   1.339


  1 in total

1.  Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems.

Authors:  Galina Samigulina; Zarina Samigulina
Journal:  Theor Biol Med Model       Date:  2020-07-20       Impact factor: 2.432

  1 in total

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