Breaking the Data Dogma
Papers

Tyler Umansky

Model Medicines Pioneers a Built-to-Purpose Data Approach to Drug Discovery

Tyler Umansky
Head of Platform and Machine Learning
The Model Medicines GALILEO™ AI Drug Discovery Platform’s data pipeline creates proprietary Built-To-Purpose datasets that outperform the commercial benchmark by as much as 1541%.

This paper is part of a series that reviews various aspects of Model Medicines GALILEO™ AI Drug Discovery Platform and its two parallel drug discovery modules CHEMPrint™ and Constellation™. Each paper demonstrates the capabilities of the GALILEO™ platform through quantitative case studies.
Specifically, this paper focuses on CHEMPrint™’s proprietary data acquisition and curation pipeline and compliments Constellation™’s proprietary, cutting edge Cryo-EM data acquisition pipeline that was reviewed in a publication earlier this year.
A review of our proprietary, class leading AI-Graph Mining PAINS Classification model, which plays a critical role in the CHEMPrint™ discovery pipeline, and the rationale for the Target Product Profile (TPP) of our infectious disease program and MDL-001, a potential best-in-class therapeutic discovered using the CHEMPrint™ platform, was published previously.
We invite researchers, industry professionals, and enthusiasts to explore the intricacies of our approach within the full text of "Breaking the Data Dogma."

