Lemna provides an AI platform that uses a geometric transformer to model protein‑protein and protein‑ligand interactions at atomic resolution, delivering explainable predictions of binding interfaces and molecular hotspots. The cloud‑based service lets pharmaceutical and biotech researchers prioritize targets, assess druggability, and design molecules with minimal data requirements, integrating via API and tools like PeSTo and CARBonAra.
Funding
$188.3K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Drug discovery often fails because researchers lack detailed, atomic‑level understanding of how proteins interact with each other and with small‑molecule ligands, leading to low‑efficacy targets, unexpected side effects, and costly late‑stage failures.
Solution
Lemna Bio offers an AI platform built around a geometric transformer architecture that models protein‑protein and protein‑ligand interactions at atomic resolution. The system generates explainable predictions of interaction interfaces and molecular hotspots, enabling scientists to prioritize high‑confidence targets, assess druggability, and design molecules with desired engagement profiles. By reasoning from first‑principles atomic structures and incorporating biological context, the platform provides data‑efficient insights that accelerate target validation and reduce the risk of late‑stage failure. Researchers can access the models through the Lemna platform, where tools such as PeSTo and CARBonAra are already used by thousands of scientists.
Target Audience
Primary users are pharmaceutical and biotech researchers, including computational chemists, structural biologists, and drug discovery teams seeking AI‑driven insights into protein interactions and ligand design.
Features
- Geometric transformer core that reasons from atomic structures and biological context to predict interaction interfaces
- Data‑efficient models that require limited training data while maintaining high accuracy
- Explainable output highlighting molecular hotspots and binding sites for target validation
- Integrated tools (PeSTo, CARBonAra) for protein‑protein interface prediction and protein sequence design
- Cloud‑based platform allowing researchers to run predictions without local hardware
- Compatibility with existing drug discovery workflows through API access and downloadable results