Breaking the Data Dogma
Model Medicines Pioneers a Built-to-Purpose Data Approach to Drug Discovery
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.
LEAD AUTHOR
Tyler Umansky, Machine Learning Engineer
WITH CONTRIBUTION FROM
Model Medicines Discovery Research & Development Team
Virgil Woods, Navya Ramesh, M.S., Summer Batasin, Bastiaan Bergman, Ph.D., Sean M. Russell, and Daniel Haders, Ph.D.
