Prediction of Bioactive Compounds Using Computed NMR Chemical Shifts

• Published in: Combinatorial chemistry & high throughput screening Vol. 18; no. 6; p. 562
• Main Authors: Karthikeyan, Muthukumarasamy, Rajamohanan, Pattuparambil Ramanpillai, Vyas, Renu
• Format: Journal Article
• Language: English
• Published: United Arab Emirates 01.01.2015
• Subjects: Aspirin - chemistry; Datasets as Topic; Drug Discovery; Hypoglycemic Agents - chemistry; Magnetic Resonance Spectroscopy; Molecular Structure; Small Molecule Libraries - chemistry
• ISSN: 1875-5402
• DOI: 10.2174/1386207318666150703113312

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.