Ligand-Based
Target Prediction with Signature Fingerprints
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Abstract
When evaluating a potential drug
candidate it is desirable to predict
target interactions in silico prior to synthesis in order to assess,
e.g., secondary pharmacology. This can be done by looking at known
target binding profiles of similar compounds using chemical similarity
searching. The purpose of this study was to construct and evaluate
the performance of chemical fingerprints based on the molecular signature
descriptor for performing target binding predictions. For the comparison
we used the area under the receiver operating characteristics curve
(AUC) complemented with net reclassification improvement (NRI). We
created two open source signature fingerprints, a bit and a count
version, and evaluated their performance compared to a set of established
fingerprints with regards to predictions of binding targets using
Tanimoto-based similarity searching on publicly available data sets
extracted from ChEMBL. The results showed that the count version of
the signature fingerprint performed on par with well-established fingerprints
such as ECFP. The count version outperformed the bit version slightly;
however, the count version is more complex and takes more computing
time and memory to run so its usage should probably be evaluated on
a case-by-case basis. The NRI based tests complemented the AUC based
ones and showed signs of higher power