Terray Therapeutics Tech Stack
AI-driven small molecule drug discovery platform combining high-throughput experimentation with computational chemistry
Terray pairs ultra-high throughput wet-lab automation (Tecan, Agilent, liquid handlers) with computational chemistry (RDKit, OpenMM) and Python-based data infrastructure (MySQL, PostgreSQL, AWS) to compress small molecule discovery timelines. The tech stack reveals a company executing a closed DMTA loop at scale — they're benchmarking computational methods, running cell assays against protein targets in parallel, and feeding experimental results back into molecule design. Hiring skews toward data and research roles (6 of 8 open positions) with senior/director-level seniority, suggesting they're scaling validation and pipeline velocity rather than early-stage exploration.
Tech Stack 15 technologies
What Terray Therapeutics Is Building
◆Challenges
- Expanding computational pipeline
- Benchmarking computational methods
- Continuous method calibration
- Accelerating hit-to-lead conversion
- Improving experimental feedback loop
- Friction points in dmta cycle
▲Active Projects
- Lims informatics infrastructure
- Ci/cd infrastructure and pipelines
- Cell-based assay development for inflammation targets
- Hit validation and lead optimization for small molecule pipeline
- Free energy calculation deployment code and database
- Benchmarking computational chemistry methods
- Continuous method calibration
- Run biochemical assays specific to the protein targets at scale using liquid handling robots to evaluate hits from tarray screens
- High-throughput computational molecule design
- Lead chemistry pipeline projects
