  Light-activated drugs are a promising way to localize biological activity and
minimize side effects. However, their development is complicated by the
numerous photophysical and biological properties that must be simultaneously
optimized. To accelerate the design of photoactive drugs, we describe a
procedure that combines ligand-protein docking with chemical property
prediction based on machine learning (ML). We apply this procedure to 58
proteins and 9,000 photo-drug candidates based on azobenzene cis-trans
isomerism. We find that most proteins display a preference for trans isomers
over cis, and that the binding affinities of nominally active/inactive pairs
are in fact highly correlated. These findings have significant value for
photopharmacology research, and reinforce the need for virtual screening to
identify compounds with rare desirable properties. Further, we combine our
procedure with quantum chemical validation to identify promising candidates for
the photoactive inhibition of PARP1, an enzyme that is over-expressed in cancer
cells. The top compounds are predicted to have long-lived active forms,
differential bioactivity, and absorption in the near-infrared therapeutic
window.
