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).