Normalized claim
Quantified impact: 4 x
The platform enabled a 4,000x larger dataset for lymphoma research.
GCPEvidence: Medium50/100
Schrödinger: Physics-based molecular simulation platform on Compute Engine, Cloud GPUs, BigQuery
Use case typeDrug discoveryUpdated Jun 13, 2026
Schrödinger uses Google Cloud as the foundation for its physics-based molecular simulation platform to accelerate drug discovery and reduce the cost and time of lab work. The company runs compute-intensive simulation workloads that previously took up to three weeks on on-premises clusters; with Google Cloud, it can scale horizontally across more GPUs and CPUs to finish computations within hours. Schrödinger added BigQuery as a data hub for hundreds of billions of molecules, reagents, and iterations, and uses machine learning to narrow simulation results toward the most promising candidate molecules.
- Organization
- Schrödinger
- Industry
- Pharma
- Location
- United States
- Published
- May 2026
Reported outcomes
4x
quantified impactOther quantified impact
−97%quantified impact
Strategic outcomes
Speed & agilityAccelerated simulation turnaroundScale & capacityExpanded research dataset scaleNew product / capabilityBuilt a virtual-lab discovery platformBetter decisions & insightNarrowed candidates to promising molecules
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Quantified impact: 97% decrease
The approach reduced physically built molecules in the lab by 97%.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Schrödinger
- Provider
- GCP
- Maturity
- Unknown
- Linked source
- Google Cloud Customer Stories
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 4
- 1Drug Discovery
- 2Scientific Computing
- 3Simulation Platform
- Drug discovery simulations are compute-intensive and bursty, and legacy on-premises clusters could take up to three weeks for a single calculation.
- Schrödinger needed a virtual-lab platform to speed iteration and expand datasets for research programs such as lymphoma research.
- Schrödinger built a platform on Google Cloud using Compute Engine, Cloud GPUs, and BigQuery.
- The platform scales simulation workloads horizontally to use more GPUs and CPUs.
- BigQuery serves as a central data hub for hundreds of billions of molecules, reagents, and iterations.
- Machine learning is used to narrow the field of candidate molecules from very large datasets to the most promising options.
Technologies
- Computations were reduced from weeks to hours.
- The platform enabled a 4,000x larger dataset for lymphoma research.
- The approach reduced physically built molecules in the lab by 97%.
- In one program, the field was narrowed from billions of molecules to 78 candidate molecules.
- The company said the platform can help move programs from years to around 10 months for human readiness.
Architecture
A virtual-lab molecular simulation platform on Google Cloud that uses Compute Engine and Cloud GPUs for bursty scientific workloads and BigQuery as a large-scale data hub for molecules, reagents, and simulation iterations.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: May 26, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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