Using the computer system PASS (prediction of activity spectra for substances), which predicts
simultaneously several hundreds of biological activities, a training set for discriminating
between drugs and nondrugs is created. For the training set, two subsets of databases of drugs
and nondrugs (a...
Using the computer system PASS (prediction of activity spectra for substances), which predicts
simultaneously several hundreds of biological activities, a training set for discriminating
between drugs and nondrugs is created. For the training set, two subsets of databases of drugs
and nondrugs (a subset of the World Drug Index, WDI, vs the Available Chemicals Directory,
ACD) are used. The high value of prediction accuracy shows that the chemical descriptors and
algorithms used in PASS provide highly robust structure-activity relationships and reliable
predictions. Compared to other methods applied in this field, the direct benchmark undertaken
with this paper showed that the results obtained with PASS are in good accordance with these
approaches. In addition, it has been shown that the more specific drug information used in the
training set of PASS, the more specific discrimination between drug and nondrug can be
obtained.
Journal of Medicinal Chemistry, 2001, 44 (15), 2432-2437.