NMR based chemical shifts are an important diagnostic parameter for structure elucidation as they capture rich
information related to conformational, electronic and stereochemical arrangement of functional groups in a molecule
which is responsible for its activity towards any biological target. The present work discusses the importance of
computing NMR chemical shifts from molecular structures. The NMR chemical shift data (experimental or computed)
was used to generate fingerprints in binary formats for mapping molecular fragments (as descriptors) and correlating with
the bioactivity classes. For this study, chemical shift data derived binary fingerprints were computed for 149 classes and
4800 bioactive molecules. The sensitivity and selectivity of fingerprints in discriminating molecules belonging to different
therapeutic categories was assessed using a LibSVM based classifier. An accuracy of 82% for proton and 94% for carbon
NMR fingerprints were obtained for anti-psoriatic and anti-psychotic molecules demonstrating the effectiveness of this
approach for virtual screening.
Keywords: Chemical shift, fingerprints, NMR, similarity searching, virtual screening.
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