Lantern Pharma (NASDAQ: LTRN) announced it will host its first live, public demonstration of its withZeta.ai multi-agentic AI co-scientist platform on April 30, 2026. The event will feature two sessions to accommodate global audiences and will be led by CEO Panna Sharma. The demonstration will showcase the platform's ability to execute complex oncology research workflows by integrating clinical, molecular, and scientific datasets.
The platform is designed to accelerate drug discovery timelines, particularly in rare cancers, while supporting a scalable, subscription-based commercial model. According to the company, withZeta.ai is now commercially available as a subscription-based research platform for the global biomedical and drug development community, representing a new revenue stream for Lantern. The full press release is available at https://ibn.fm/wzevl.
Lantern Pharma is a clinical-stage precision oncology company leveraging AI and machine learning, including its proprietary RADR platform, to transform the development of cancer therapies. Its clinical pipeline includes LP-184 (acylfulvene), LP-284 (a TC-NER targeting compound), and LP-300 (cisplatin/ethacraplatin analog). LP-300 is being evaluated in the HARMONIC Phase 2 trial for never-smoker patients with relapsed advanced lung adenocarcinoma. LP-184 is also being developed for pediatric CNS cancers through Starlight Therapeutics, Lantern's wholly owned CNS-focused subsidiary.
The company operates an AI Center of Excellence in Bengaluru, India, and is headquartered in Dallas, Texas. The demonstration event is expected to highlight how withZeta.ai can integrate diverse data sources to streamline research processes, potentially reducing the time and cost associated with drug development. More information about Lantern Pharma is available at their newsroom: https://ibn.fm/LTRN.
This event marks a significant step for Lantern as it seeks to commercialize its AI capabilities beyond internal use, tapping into the growing demand for AI-driven drug discovery tools. The implications for the broader biomedical community include access to advanced multi-agentic AI that could democratize complex research workflows, particularly in rare cancers where traditional development is often slow and costly.
