Use cases
To demonstrate how the OSS platform can power real-world AI science labs, we have build a reference lab at ARTgarage, an beautifully conceptualized garage for AI and robotics startups at ARTPark (IIsc, Bengaluru).
Drug Discovery Lab
Drug discovery is a long, resource-intensive process.
A typical preclinical drug discovery pipeline has following stages:
According to Eli Lilly research, preclinical stages alone can take up to six years and account for 33% of out-of-pocket R&D costs (around 46% of total capitalized costs).
Every stage in the pipeline — from target identification to lead optimization — requires tens of thousands of wet-lab experiments. Typically, researchers need to:
- Run more than 100,000 assays in early screening (target-to-hit)
- Perform around 10,000 in hit-to-lead optimization experiments
- Run more than 1,000 experiments related to ADME testing, toxicity studies, and pharmacological profiling
These experiments are iterative and repetitive — ideal candidates for automation and AI-driven optimization.
The reference lab focuses on the first two stages of the drug discovery pipeline: target-to-hit and hit-to-lead.
Supported instruments:
- Automated liquid handlers
- Multimode spectrometers
- Incubators, centrifuges, and shakers
- Freezers and refrigerators for sample storage
The experimental setup is shown below:
Supported assays:
- Enzymatic activity
- Solubility
- Permeability
- Additional early-stage drug screening workflows
The lab supports automatically converting English protocols into OSS experiment specification, using the logical actions and language supported by ULA. Each instrument is connected through the ULI layer, enabling seamless coordination across devices and robotic systems. By automating these workflows through the ULE and AI-driven feedback loops, repetitive steps are handled autonomously, freeing scientists to focus on hypothesis design, data interpretation, and innovation.
Collaborate with Us
OSS is actively working with drug discovery startups, academic groups, and research teams to automate real-world assays and validate autonomous lab workflows.
These collaborations help us refine the platform and scale it for commercial and national deployments.
If you’d like to partner with OSS to run your experiments in an automated lab: