Federated Learning

3 posts

Two professionals in a futuristic lab — a tech-styled figure in a cap and headphones sitting next to a scientist in a white coat and safety glasses — surrounded by holographic molecular structures, AI visualizations, and flasks on a workbench, with the words 'Geek Life' across the image

AI Drug Discovery·April 20, 2026·6 min read

Uniting Tech and Science Geeks: Three Plays for AI-Driven Drug Discovery

AI drug discovery's hardest problem isn't a model — it's a coordination problem between engineers who don't speak biology and biologists who don't ship code. Three high-leverage plays for bridging the two cultures.

Stack diagram showing the four layers AI drug discovery depends on: a data base layer of genomic, clinical, and experimental data; a pathway layer with cell-type-specific and time-resolved edges; a digital twins and simulation layer with virtual cells and organ-scale models; and AI drug discovery outcomes including target discovery, lead optimization, and in-silico Day Zero success. On the right, a multi-omic foundation model functions as an AlphaFold of pathway state.

AI Drug Discovery·April 18, 2026·4 min read

The Pathway Layer: What AI Drug Discovery Needs to Build Next

AI drug discovery's biggest gap isn't models — it's pathway infrastructure. Six investments biology needs before AI can deliver on its promise.

Diverse group of tech and science professionals collaborating — illustrating the bridge between computational expertise and scientific domain knowledge in AI-driven drug discovery

AI Drug Discovery·April 18, 2025·1 min read

Uniting Tech and Science Geeks in AI Drug Discovery

Three strategies to bridge AI/ML expertise and scientific domain knowledge in drug discovery — collaboration hubs, shared data platforms, and AI-first experimentation.