Probabilistic Neural Network Model for the In Silico Evaluation of Anti-HV Activity and Mechanism of Action

摘要

A theoretical 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 activeonactive chemicals 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 chemistry.