2026-10-14T06:47:11Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33752780 2026-09-14T19:08:04Z category_1 category_4 category_915 category_7 category_19 category_21 category_873 category_734 category_931 portal_63 item_type_3 month_year_09_2026

Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors Simon Axelrod (9869102) Miroslav Kašpar (1950085) Kristýna Jelı́nková (24896702) Markéta Šmı́dková (24896705) Erika Bartůňková (20791119) Sille Štěpánová (24896708) Eugene Shakhnovich (1467940) Václav Kašička (1991611) Martin Dračı́nský (13271785) Zlatko Janeba (1585501) Rafael Gómez-Bombarelli (1933810) Biophysics Biochemistry Physical Sciences not elsewhere classified Medicine Pharmacology Biotechnology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified treat localized diseases state nonadiabatic dynamics shifted absorption spectra severe side effects quantum chemistry calculations guided screening strategy free energy perturbation estimate quantum yields based surrogate models 9 μm ). 8 ± 28 shifted parp1 photoinhibitors parp1 upon green ml force fields differential parp1 binding dependent parp1 binding 4 ± 1 used computational techniques screen additional compounds synthesized 10 candidates rapid thermal relaxation refine binding predictions parp1 inhibition constants 1 </ b polymerase 1 ml ), validated compounds k </ computational design thermal half promising candidates identify candidates simultaneously optimized results validate promising way predict p photoactive inhibition machine learning fold increase experimentally characterized experimental validation existing treatments dark conditions cancer target biological properties atomistic simulation aqueous media activated drugs Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their development is complicated by the set of photophysical and biological properties that must be simultaneously optimized. Here we used computational techniques to find a set of promising candidates for the photoactive inhibition of the poly(ADP-ribose) polymerase 1 (PARP1) cancer target. Using our recently developed methods based on atomistic simulation and machine learning (ML), we screened a set of 5 million hypothetical photoactive ligands. Our workflow used protein–ligand docking to identify candidates with differential PARP1 binding under light and dark conditions; ML force fields and quantum chemistry calculations to predict pKa, absorption spectra, and thermal half-lives; graph-based surrogate models to screen additional compounds; excited-state nonadiabatic dynamics with ML force fields to estimate quantum yields; and free energy perturbation (FEP) to refine binding predictions. From these predictions, we prioritized a small set of synthetically feasible candidates expected to have red-shifted absorption spectra, thermal half-lives on the order of seconds to minutes, and isomer-dependent PARP1 binding under visible-light control. We synthesized 10 candidates and experimentally characterized their photobehavior and PARP1 inhibition constants. Among the validated compounds, 1 showed a 15-fold increase in inhibition of PARP1 upon green-light irradiation at 519 nm (208.8 ± 28.3 μM vs 14.4 ± 1.9 μM). These results validate the computation-guided screening strategy for identifying red-shifted PARP1 photoinhibitors, while also underscoring current limitations such as rapid thermal relaxation in aqueous media. 2026-09-14T00:00:00Z Dataset Dataset 10.1021/jacs.6c08644.s004 https://figshare.com/articles/dataset/Computational_Design_and_Experimental_Validation_of_Photoactive_PARP1_Inhibitors/33752780 CC BY-NC 4.0