What challenges did PozeSCAF Discovery Solutions face before AI implementation?

Older GROMACS versions caused scalability and performance issues. Molecular dynamics simulations took more than 30 hours, slowing hit identification and hit-to-lead cycles. Higher compute costs and slower preclinical timelines hurt competitiveness.

What was the impact of the AI solution?

Cut simulation runtime from 30 hours to under 15, a reduction of more than 50%. Increased throughput to about 2.5 times more simulations in the same timeframe. Reduced compute costs by 25–30%. Saved an estimated 2–3 months during the preclinical phase.

Source

PozeSCAF Discovery Solutions — Amazon Bedrock + high-performance simulation on AWS to accelerate drug discovery

Use case typeLife sciences innovationUpdated Jun 13, 2026

PozeSCAF Discovery Solutions (formerly Immunocure Discovery Solutions) turned to AWS for scalable, high-performance infrastructure to optimize molecular dynamics workloads. The company cut simulation runtimes by more than half, reduced compute costs, and accelerated its drug discovery pipeline. It also began exploring generative AI/agentic workflows with Amazon Bedrock to build knowledge graphs from project data and flag potential side effects earlier.

Organization: PozeSCAF Discovery Solutions
Industry: Pharma
Location: United States
Published: May 2026

Reported outcomes

2.5x

simulation throughput increaseProductivity & throughput

−50%simulation runtime reduction−25%compute cost reduction2 monthspreclinical time saved

Strategic outcomes

Speed & agilityAccelerated drug discovery pipelineCost efficiencyReduced compute costsNew product / capabilityExplored agentic AI workflowsBetter decisions & insightBuilt knowledge graphs from project data

Catalog median for productivity & throughput deployments: +45% across 225 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

1HPC optimization
2Drug discovery acceleration
3Generative AI exploration

Older GROMACS versions caused scalability and performance issues.
Molecular dynamics simulations took more than 30 hours, slowing hit identification and hit-to-lead cycles.
Higher compute costs and slower preclinical timelines hurt competitiveness.

Benchmarked Amazon EC2 instance types including GPU and HPC options.
Fine-tuned GROMACS parameters using GPU acceleration and upgraded to the latest version.
Used a Slurm cluster on AWS for large-scale compound screening and explored Amazon Bedrock for knowledge graphs and agentic AI workflows.

Technologies

Amazon EC2 AWS ParallelCluster AWS Batch Amazon Bedrock Slurm

Cut simulation runtime from 30 hours to under 15, a reduction of more than 50%.
Increased throughput to about 2.5 times more simulations in the same timeframe.
Reduced compute costs by 25–30%.
Saved an estimated 2–3 months during the preclinical phase.

Architecture

PozeSCAF optimized molecular dynamics workloads on AWS by benchmarking Amazon EC2 GPU and HPC instances, tuning GROMACS parameters for GPU acceleration, upgrading the software stack, and running large-scale screening on a Slurm cluster on AWS. The article also notes early exploration of Amazon Bedrock for knowledge-graph and agentic workflows.

Sources & evidence1

Groundedness: 5/5Type: Case StudyPublished: May 27, 2026Publisher: AWSEvidence: VendorConfidence: High

Primary source

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Customer evidence

PozeSCAF Discovery Solutions — Amazon Bedrock + high-performance simulation on AWS to accelerate drug discovery

Use case focus

Challenges

Solution

Impact

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