Probabilistic neural network model for the in silico evaluation of anti-HIV activity and mechanism of action.
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 active/nonactive 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.
Vilar
Santiago
S
Faculty of Pharmacy, Department of Organic Chemistry, University of Santiago de Compostela, Santiago de Compostela 15782, Spain. qosanti@yahoo.es
Santana
Lourdes
L
Uriarte
Eugenio
E
D000465:Algorithms; D019380:Anti-HIV Agents / Q000737:chemistry* / Q000145:classification* / Q000494:pharmacology; D016208:Databases, Factual; D015497:HIV-1 / Q000187:drug effects; D008958:Models, Molecular; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011336:Probability; D021281:Quantitative Structure-Activity Relationship
D019380:Anti-HIV Agents