New York-based Schrödinger and German pharma Bayer have penned a five-year drug trial tech deal aimed at boosting Bayer's drug discovery efforts.
Schrödinger’s platform aims to predict how strongly a potential drug will bind and affect a target protein with the goal of optimizing its chemical properties and reducing the number of compounds that need to be synthesized before settling on a lead candidate.
With Bayer, it will be working on a “comprehensive de novo design solution” with the tech slated to have the capacity for “enumerating, screening, and scoring billions of synthetically feasible, virtual compounds supporting the identification and optimization of potential new therapeutic candidates.”
Schrödinger gets a small fee, just €10 million, for its work.
“Underscoring our efforts in digital transformation along our value chain, our collaboration with Schrödinger is intended to leverage advanced physics-based methods and modern machine learning capabilities to increase discovery of viable drug candidates,” said Karl Ziegelbauer, Ph.D., head of open innovation and digital technologies at the pharmaceuticals division of Bayer.
“The new co-developed technological solution is aimed at opening up new avenues for therapeutic discovery in the future, ultimately for the benefit of the patients.”
This builds on a growing deal trend for Schrödinger, which has pacts with the likes of AstraZeneca and Sanofi as well as a joint venture with WuXi.
It also comes as Schrödinger aims to move into the clinic itself with wholly owned assets focused on at least three oncology targets. The company has recently brought on translational science veterans from Merck and Eisai to help move its programs out of the discovery phase, after raising a total of $110 million to support its internal efforts and a dedicated staff.
Schrödinger counts more than two dozen potential therapies in a range of disease areas currently being developed through its collaborations, including several clinical-stage assets and two FDA-approved cancer treatments.