Vilar, Santiago published the artcileProbabilistic Neural Network Model for the In Silico Evaluation of Anti-HIV Activity and Mechanism of Action, COA of Formula: C7H15NO, the publication is Journal of Medicinal Chemistry (2006), 49(3), 1118-1124, database is CAplus and MEDLINE.
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.
Journal of Medicinal Chemistry published new progress about 13444-24-1. 13444-24-1 belongs to piperidines, auxiliary class Piperidine,Alcohol, name is 1-Ethylpiperidin-3-ol, and the molecular formula is C16H14O6, COA of Formula: C7H15NO.
Referemce:
https://en.wikipedia.org/wiki/Piperidine,
Piperidine | C5H11N – PubChem