Predictive QSAR models for the inhibition activities of nitrogen-containing bisphosphonates (N-BPs) against
farnesyl pyrophosphate synthase (FPPS) from Leishmania major (LeFPPS) were developed using a data set of 97 compounds.
The QSAR models were developed through the use of Artificial Neural Networks and Random Forest learning
procedures. The predictive ability of the models was tested by means of leave-one-out cross-validation; Q2values ranging
from 0.45-0.79 were obtained for the regression models. The consensus prediction for the external evaluation set afforded
high predictive power (Q2=0.76 for 35 compounds).
The robustness of the QSAR models was also evaluated using a Y-randomization procedure. A small set of 6 new N-BPs
were designed and synthesized applying the Michael reaction of tetrakis (trimethylsilyl) ethenylidene bisphosphonate with
amines. The inhibition activities of these compounds against LeFPPS were predicted by the developed QSAR models and
were found to correlate with their fungistatic activities against Candida albicans. The antifungal activities of N-BPs bearing
n-butyl and cyclopropyl side chains exceeded the activities of Fluconazole, a triazole-containing antifungal drug. In
conclusion, the N-BPs developed here present promising candidate drugs for the treatment of fungal diseases.
Keywords: Artificial neural networks, drug design, fungistatic activity, Michael addition, nitrogen-containing bisphosphonates,
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