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.

All code and data is available on GitHub and there is also a website where you can test your own molecules http://chemprop.csail.mit.edu.