Discovery of novel antibiotic Halicin using deep learning
A recent paper has caught a lot of attention recently "A Deep Learning Approach to Antibiotic Discovery" DOI from Regina Barzilay's group at MIT. They used a deep neural network model to predict growth inhibition of Escherichia coli using a collection of 2,335 molecules, the molecules were described using Morgan fingerprints, computed using RDKit, for each molecule using a radius of 2 and 2048-bit fingerprint vectors. Using this methodology they identified the known c-Jun N-terminal kinase inhibitor SU3327 which they renamed Halicin. A quick search using MolSeeker allowed identification of the structure and inChiKey.
A search of UniChem using the InChikey NQQBNZBOOHHVQP-UHFFFAOYSA-N identified a number of other identifiers in different databases.
Including a link to the ChEMBL entry CHEMBL510038 giving the biological data 0.7 nM Inhibition of c-Jun N-terminal kinase by time-resolved FRET assay, and links to the original 2009 publication DOI describing the c-JNK SAR. The compound has a rat half-life of 0.45 h. There is another publication that might be of interest describing "Discovery of 2-(5-nitrothiazol-2-ylthio)benzo[d]thiazoles as novel c-Jun N-terminal kinase inhibitors" DOI.
Certainly an interesting approach, I suspect the nitrothiazole functionality would set off a few structural alerts but there are certainly of plenty of similar compounds commercially available that would allow exploration of the SAR without too much investment in resources.