DDDR-15. UTILIZING DEEP DOCKING AND ARTIFICIAL INTELLIGENCE FOR THE DISCOVERY OF NOVEL PARP1-SELECTIVE INHIBITORS FOR USE AGAINST BRAIN TUMORS

Abstract 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. 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’ve achieved commercial success, first generation PARP inhibitors are limited in their utility as they cannot readily pass through the blood-brain barrier and have 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, an advanced artificial intelligence (AI) approach, to discover a novel, PARP1-selective inhibitor for use against brain tumors. 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 drug-like properties such as metabolism, permeability, and safety profiles, can be combined to rapidly perform hit-to-lead optimization. We will present preliminary Deep Docking screening results from billions of compounds, identifying several with predicted high binding affinities for PARP-1. Validating in vitro and in vivo data, including PARP1 selectivity, metabolic stability, pharmacokinetic profile, CNS penetration and safety profile, will be described. Application of the Deep Docking AI platform is being used to significantly accelerate the discovery of 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.