Build · Schrödinger
Advancing the design and optimization of drug formulations with combined computational and experimental approaches
The customer is anonymised in Schrödinger’s own case study — not withheld by us.
Schrödinger is a physics-plus-machine-learning computational platform for molecular design; all top-20 pharma companies by revenue are customers of its drug-discovery software.
This sits in the Build stage of the AI use-case lifecycle: Named examples from the foundation-model labs and platforms pharma teams use to actually construct, train and generate the model or application.
Source
Published by Schrödinger on its own site — read the original case study on schrodinger.com ↗
More like this
Related use cases
- 01Hit-to-development-candidate in 10 months: rapid discovery of a novel, potent MALT1 inhibitor (SGR-1505) using large-scale virtual screening of 8.2B compounds
- 02Design of a highly selective, allosteric, picomolar TYK2 inhibitor using novel FEP+ strategies
- 03Design of a novel, potent CDC7 inhibitor development candidate with high ligand efficiency and optimized properties
- 04Discovery of a novel, potent ACC inhibitor driven by computationally guided design
- 05High-precision, computationally guided discovery of highly selective Wee1 inhibitors
- 06Enables drug discovery using CDD Vault (with Schrödinger platform integration