AI Drug Discovery & Development Summit 2026

We're excited to be attending AIDDD in Boston. Don't miss our Nextflow Frontier talk in the Discovery Biology track at 09:30 EST on October 28: A Recursive Closed-Loop System for New Computational Models and Approaches in Biopharma.

Accurate models of biology, such as identifying cell types in single-cell data, are a key capability in understanding disease and deciding what to take into the lab. These better models can provide significant savings in time and downstream costs for the drug discovery and development process. Empirical validation of hypotheses is the foundation of progress in science. That matters even more now that AI agents can propose ideas faster than anyone can realistically test them. As computational capability grows, execution and validation at scale become critical.

We built a closed-loop discovery system using Nextflow that turns every hypothesis into a reproducible pipeline, runs thousands in parallel, scores each on held-out data, learns and plans the next round. It is agnostic to the scientific problem: anything with data and a measure of success can go through the same loop, and scientists steer it with their own domain expertise.

On the Open Problems single-cell benchmark, it produces a kidney cell-type classifier that makes a third fewer errors than the best published method.

In this presentation we'll show what it takes in practice: framing the question, building accurate measures of success, how scientists' deep domain knowledge steers the search, and the differences between public benchmarks and partnership projects inside pharma.