AI in Drug Discovery: How Machine Learning Is Accelerating Timelines
A grounded look at where ML actually moves the needle and where it still does not.
Working notes, white papers, and field guides for teams evaluating AI in their pipeline.
A grounded look at where ML actually moves the needle and where it still does not.
What docking is, what it can predict, and how modern scoring functions have shifted in the last 5 years.
The attrition math behind pharma R&D and where computational screening compresses the funnel.
Off-target prediction, on-target activity, and the trade-offs that decide a payload.
Where the $2.6B per-drug number comes from, and which line items computational methods can erase.
Benchmarks on internal datasets vs. published baselines.
Methods, scoring functions, and data formats we accept.
Peer-reviewed work from our group on Google Scholar.
We share early drafts with active and prospective clients.
Request access