Lunai Bioworks Announces Early Results From AI-Based Platform Designed To Identify Compounds That Interact With Acetylcholinesterase, Same Molecular Target Affected By Nerve Agents

In the face of growing global concern over the potential use of chemical weapons such as the nerve agent Sarin, Lunai Bioworks Inc. (NASDAQ:LNAI) today announced promising early results from its AI-based platform designed to identify compounds that interact with acetylcholinesterase (AChE), the same molecular target affected by nerve agents.

Using a combination of chemical feature analysis and machine learning classifiers, Lunai's platform demonstrated an ability to differentiate active AChE inhibitors from inactive compounds with high accuracy (0.94 AUROC; Area Under the Receiver Operating Characteristic curve). The work builds on models trained using the company's in-house neuroactive compound screening data, as well as public sources, and applies predictive modeling to identify chemical signatures associated with AChE inhibition. Compounds with previously unidentified, potential neurotoxic activity have been identified for follow-up and validation.

Lunai's work represents a responsible application of AI, focused on defense and public health, not harm. The announcement comes amid increasing concern about the potential misuse of generative AI technologies, underscored by a recent NBC News investigation showing how AI chatbots could be manipulated to share instructions for creating dangerous substances, including nerve agents.

In future phases, the company plans to integrate its behavioral zebrafish profiling platform to observe real-time physiological effects of predicted inhibitors, helping prioritize candidate compounds for further development as potential countermeasures.

Zebrafish are an FDA-recognized model for toxicology and drug discovery, offering a unique balance of genetic relevance to humans and scalability unmatched by rodent models. Lunai's platform leverages these advantages by coupling high-throughput behavioral assays in zebrafish with machine learning models trained to recognize neurotoxic phenotypes. This approach enables rapid, low-cost screening of both suspected threat agents and potential countermeasure compounds, often identifying hits within days rather than months, a critical advantage in time-sensitive defense and public health scenarios.

To support collaboration, Lunai has embedded its predictive model into a customized dashboard, allowing partners to explore their chemical structures and determine if they have potential neurotoxicity.