Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors

2026-04-27

AI Summary

To mitigate the systemic toxicity associated with conventional PARP1 inhibitors, this study develops a class of photocaged small-molecule therapeutics that can be precisely activated under visible light. By integrating multiscale computational strategies—including protein–ligand docking, machine learning force fields, quantum chemical calculations, graph neural network surrogate models, nonadiabatic excited-state dynamics, and free energy perturbation theory—the authors efficiently identified lead compounds exhibiting red-shifted absorption, moderate thermal stability, and pronounced photocontrolled target binding. Ten candidates were synthesized and experimentally validated, with compound 1 demonstrating a 15-fold enhancement in PARP1 inhibitory potency upon irradiation at 519 nm (IC₅₀ reduced from 208.8 μM to 14.4 μM), thereby achieving, for the first time, spatiotemporally precise modulation of PARP1 activity.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Machine Learning: Hardware-aware ML
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• photoactive inhibitors
• PARP1
• light-activated drugs
• computational design
• photopharmacology

Innovation

• photoactive inhibitors
• machine learning force fields
• computational drug design
• nonadiabatic dynamics
• free energy perturbation