Lightning.AI Platform Architecture
Lightning.AI’s Visual Biology neural networks see the pathways driving disease. They are trained with large-scale experimental imaging that visualises differences in biological processes between diseased and healthy cells. By seeing biology in action, we discover novel targets and drugs.
Lightning.AI connects LLMs to Visual Biology - forming a Biology-Aware Discovery Loop that reasons from real disease biology.
Large language models don’t understand biology on their own. We built a system that lets them learn directly from what cells do - not just from what papers say.
At the core is the Visual Biology Experimental AI - trained on over 2 billion proprietary images capturing 2,000+ intracellular processes across diseases, cell types, and perturbations. This creates a neural network fluent in pathway dynamics, disease signatures, and mRNA biology.
Pharma’s LLMs connect to Lightning’s Co-Pilot, a ChatGPT-style interface that plugs into this experimental AI. It doesn’t just respond - it runs visual biology experiments in real time, grounded in patient-derived cells.
Together, they close the loop:
- LLMs propose questions about disease mechanisms
- The Visual Biology AI runs experiments in silico and in cells
- Each iteration sharpens biological reasoning
- Causal pathways and druggable targets emerge - not inferred, but seen
This loop powers multimodal disease models that reason with evidence, not assumptions.
It discovers de-risked targets, visualizes mechanisms, and identifies small molecules modulating mRNA biology - even in diseases considered undruggable.
This isn’t AI over data. It’s AI over real biology.
It’s not a lab-in-a-loop. It’s the Biology-Aware AI-in-the-loop.