Machine-Learning Screen of 5M Candidates Yields Light-Activated PARP1 Inhibitors
- Combined protein–ligand docking, ML force fields, quantum chemistry, and free-energy perturbation (FEP) to screen roughly 5 million hypothetical photoactive PARP1 ligands.
- Docking identified candidates with differential PARP1 binding between light-exposed and dark states; ML force fields and QM calculations predicted pKa, absorption spectra, and thermal half-lives, while graph-based surrogate models screened the remaining library.
- Excited-state nonadiabatic dynamics simulations (with ML force fields) estimated photoswitching quantum yields; FEP refined binding-affinity predictions for top hits.
- Prioritized a small set of synthetically accessible candidates combining red-shifted absorption (favoring deeper tissue penetration), therapeutically suitable thermal half-lives, and light/dark-selective PARP1 binding.
- Predictions were experimentally validated, demonstrating an end-to-end computational-to-experimental pipeline for designing light-activated (photopharmacological) cancer therapeutics.
Five million virtual candidates funneled down to a handful that actually work is the real story — that pipeline (docking → ML force fields → QM → nonadiabatic dynamics → FEP) is doing what a med chem team's SAR intuition used to do by hand, just faster and across chemical space no bench chemist would ever hand-explore. The binding-mode hypothesis is straightforward: a photoswitch appended to (presumably) a nicotinamide-mimetic PARP1 pharmacophore toggles pocket occupancy between light and dark isomers, so "selectivity" here means spatiotemporal control over tumor tissue, not isoform selectivity against PARP2/tankyrases — a different axis entirely from what olaparib or niraparib were ever optimized for. Red-shifted absorption for tissue penetration plus a therapeutically workable thermal half-life are the tractability bottlenecks that usually kill photopharmacology leads, so getting synthetically accessible hits past both, and into validated wet-lab data, is the genuinely notable part.
Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors ↗