Probabilistic Neural Network Model for the In Silico Evaluation of Anti-HIV Activity and Mechanism of Action was written by Vilar, Santiago;Santana, Lourdes;Uriarte, Eugenio. And the article was included in Journal of Medicinal Chemistry in 2006.Product Details of 5003-48-5 The following contents are mentioned in the article:
A theor. model has been developed that discriminates between active and nonactive drugs against HIV-1 with four different mechanisms of action for the active drugs. The model was built up using a probabilistic neural network (PNN) algorithm and a database of 2720 compounds The model showed an overall accuracy of 97.34% in the training series, 85.12% in the selection series, and 84.78% in an external prediction series. The model not only correctly classified a very heterogeneous series of organic compounds but also discriminated between very similar active/nonactive chems. that belong to the same family of compounds More specifically, the model recognized 96.02% of nonactive compounds, 94.24% of active compounds that inhibited reverse transcriptase, 97.24% of protease inhibitors, 97.14% of virus uncoating inhibitors, and 90.32% of integrase inhibitors. The results indicate that this approach may represent a powerful tool for modeling large databases in QSAR with applications in medicinal chem. This study involved multiple reactions and reactants, such as 4-Acetamidophenyl 2-acetoxybenzoate (cas: 5003-48-5Product Details of 5003-48-5).
4-Acetamidophenyl 2-acetoxybenzoate (cas: 5003-48-5) belongs to esters. Carboxylic acid esters of low molecular weight are colourless, volatile liquids with pleasant odours, slightly soluble in water. Because of their lack of hydrogen-bond-donating ability, esters do not self-associate. Consequently, esters are more volatile than carboxylic acids of similar molecular weight.Product Details of 5003-48-5
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