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 p$K_\mathrm{a}$, 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, \textbf{1} showed a 15-fold increase in inhibition of PARP1 upon green-light irradiation at 519 nm (208.8 $\pm$ 28.3 $μ$M vs 14.4 $\pm$ 1.9 $μ$M). 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.

翻译：光激活药物是治疗现有疗法具有严重副作用的局部疾病的一种有前景的方法。然而，其开发因必须同时优化的光物理和生物学特性组合而变得复杂。本文利用计算技术寻找用于光活性抑制聚（ADP-核糖）聚合酶1（PARP1）癌症靶标的一组有前景的候选分子。采用我们近期发展的基于原子模拟和机器学习（ML）的方法，筛选了500万个假设的光活性配体。我们的工作流程使用蛋白质-配体对接识别在光照和黑暗条件下与PARP1差异结合的候选分子；利用ML力场和量子化学计算预测p$K_\mathrm{a}$、吸收光谱和热半衰期；通过基于图的替代模型筛选额外化合物；采用ML力场进行激发态非绝热动力学以估算量子产率；并利用自由能微扰（FEP）精化结合预测。基于这些预测，我们优先考虑了一小组合成可行的候选分子，预期具有红移吸收光谱、数秒至数分钟量级的热半衰期，以及在可见光控制下依赖于异构体的PARP1结合。我们合成了10种候选分子，并实验表征了其光行为和PARP1抑制常数。在验证的化合物中，\textbf{1}在519 nm绿光照射下对PARP1的抑制提高了15倍（208.8 $\pm$ 28.3 $μ$M vs 14.4 $\pm$ 1.9 $μ$M）。这些结果验证了计算引导的筛选策略用于识别红移PARP1光抑制剂，同时也揭示了当前局限性，如在水相介质中的快速热弛豫。