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M9490432.TXT
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1994-09-19
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Document 0432
DOCN M9490432
TI HIV-1 reverse transcriptase inhibitor design using artificial neural
networks.
DT 9411
AU Tetko IV; Tanchuk VYu; Chentsova NP; Antonenko SV; Poda GI; Kukhar VP;
Luik AI; Biomedical Department, Institute of Bioorganic and Petroleum;
Chemistry, Kiev, Ukraine.
SO J Med Chem. 1994 Aug 5;37(16):2520-6. Unique Identifier : AIDSLINE
MED/94334909
AB Artificial neural networks were used to analyze and predict the human
immunodeficiency virus type 1 reverse transcriptase inhibitors. The
training and control sets included 44 molecules (most of them are
well-known substances such as AZT, dde, etc.). The activities of the
molecules were taken from literature. Topological indices were
calculated and used as molecular parameters. The four most informative
parameters were chosen and applied to predict activities of both new and
control molecules. We used a network pruning algorithm and network
ensembles to obtain the final classifier. Increasing of neural network
generalization of the new data was observed, when using the
aforementioned methods. The prognosis of new molecules revealed one
molecule as possibly very active. It was confirmed by further biological
tests.
DE Algorithms Cell Line Comparative Study *Drug Design Human
HIV-1/DRUG EFFECTS Molecular Structure *Neural Networks (Computer)
Pyrimidines/*CHEMISTRY/PHARMACOLOGY Reverse Transcriptase/*ANTAGONISTS
& INHIB Structure-Activity Relationship T-Lymphocytes/MICROBIOLOGY
Zidovudine/PHARMACOLOGY JOURNAL ARTICLE
SOURCE: National Library of Medicine. NOTICE: This material may be
protected by Copyright Law (Title 17, U.S.Code).