Peptide-Based Drug Design

154 Hilpert et al.
Table 4
Accuracy of Inductive QSAR-ANN Models for Predicting High-Activity Peptides
Accuracy
Section a Set A models b [mean (SD) %] Set B models b [mean (SD) %]
Top 10 83.0 (11) 43.0 (9.5)
Top 25 55.6 (3.0) 44.8 (9.2)
Top 50 45.8 (3.6) 41.8 (3.3)
Top 100 33.3 (2.1) 35.0 (3.3)
Bottom 100 92.4 (2.5) 96.8 (2.1)
Bottom 50 96.4 (1.6) 97.8 (2.2)
Bottom 25 95.6 (1.3) 97.6 (2.8)
Bottom 10 92.0 (4.2) 97.0 (4.8)
Set A models were trained on Set A peptides and tested using Set B peptides. Set B models
were trained on Set B peptides and tested using Set A peptides.
a The section column indicates the selection of the ranked peptides that were used in calculating
accuracy; for example, Top 10 indicates the peptides ranked by model output in the top 10 by the
ANN model were considered.
b Numbers indicate the percentage of peptides in the section that were predicted correctly: for the
top sections, these were correctly predicted more active (relative IC50was > 1.0); for the bottom
sections, these are are the percentage of peptides correctly predicted less active (relative IC50was
< 1.0).
peptide of known activity in order to screen for potentially improved peptides.
These techniques, such as artificial neural network modeling combined with
atomic-resolution inductive QSAR methodology, are therefore expected to allow
in silico screening of very large numbers of antibacterial peptides that may lead
to novel therapeutics in an efficient and effective manner.
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