Researchers have developed a computational method to design photoactive PARP1 inhibitors for cancer treatment. By screening 5 million hypothetical ligands using machine learning and atomistic simulations, they identified promising candidates that show differential binding to PARP1 under light and dark conditions. Ten compounds were synthesized and tested, with one candidate demonstrating a 15-fold increase in PARP1 inhibition when exposed to green light. AI
IMPACT Advances computational drug discovery, potentially accelerating the development of targeted light-activated therapies.
RANK_REASON Academic paper detailing a new computational method for drug discovery.
- arXiv
- free energy perturbation
- graph-based surrogate models
- machine learning
- PARP1
- quantum chemistry
- atomistic simulation
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