Abstract 4186: Discovery of novel PARP1-selective inhibitors for treatment of brain tumors using artificial intelligence
Authors: Sarah Truong, Beibei Zhai, Louise Ramos, Mona Marzban, Fariba Ghaidi, Fuqiang Ban, Jason Smith, Mohit Pandey, Ekaterina Manskaia, Kally Singh, Hans Adomat, Xiaoqi Chen, John Langlands, Dennis Brown, Jeffrey Bacha, Colin Collins, Artem Cherkasov, Mads Daugaard
Journal: Cancer Research (2025)

Abstract Introduction: Brain tumors, including primary brain tumors and central nervous system (CNS) metastases, remain among the most challenging malignancies to treat, with therapeutic options often limited by the inability of drugs to penetrate the blood-brain barrier (BBB). Poly(ADP-ribose) polymerase (PARP) is a key enzyme in DNA repair, and inhibition of PARP1 specifically drives synthetic lethality in BRCA-mutated disease. While they have achieved commercial success, first generation PARP inhibitors are limited in their utility as they cannot readily pass through the blood-brain barrier and produce adverse side effects, likely driven by the collateral inhibition of PARP2. Development of a PARP1-selective CNS penetrant inhibitor could reduce toxicity, while providing a new therapeutic option for brain tumors. Traditional drug discovery methods are time-consuming and costly, necessitating innovative approaches. Here, we describe the application of Deep Docking combined with generative artificial intelligence (AI), to discover a novel, PARP1-selective inhibitor for use against brain tumors. Methods: Deep Docking utilizes deep learning to accelerate the prediction of the binding affinity of a large number of compounds to target proteins, streamlining the virtual screening process and allowing for rapid docking of billions of compounds against the PARP1 protein. Additionally, state-of-the-art generative algorithms and deep learning techniques for predicting CNS penetrance can be combined with Deep Docking to rapidly perform hit-to-lead optimization of a CNS-penetrating PARP1-selective inhibitor. Results: We will present Deep Docking screening results from billions of compounds. Validating in vitro and in vivo data, including PARP1 inhibition and selectivity, BBB permeability, metabolic stability, pharmacokinetic profile, and CNS penetration will be described. Conclusion: The Deep Docking platform is being used in concert with generative AI approaches to discover and finetune a selective PARP-1 inhibitor for brain tumors. This approach will not only improve the efficiency of drug discovery but also enhance the specificity and efficacy of potential therapeutics. Citation Format: Sarah Truong, Beibei Zhai, Louise Ramos, Mona Marzban, Fariba Ghaidi, Fuqiang Ban, Jason Smith, Mohit Pandey, Ekaterina Manskaia, Kally Singh, Hans Adomat, Xiaoqi Chen, John Langlands, Dennis Brown, Jeffrey Bacha, Colin Collins, Artem Cherkasov, Mads Daugaard. Discovery of novel PARP1-selective inhibitors for treatment of brain tumors using artificial intelligence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4186.