Scientists run structure prediction and generative analyses inside Benchling, with full traceability to experimental records
, /PRNewswire/ -- Benchling today launched Model Hub, a dedicated space inside the Benchling platform where scientists can discover, run, and track scientific AI models alongside their experimental data. Model Hub is available now to all Benchling customers.
Until now, running AI models in a drug program required compute provisioning, DevOps work, API integrations, and ongoing maintenance, often taking months of engineering overhead before a scientist ran a single prediction. Results lived outside the R&D stack, disconnected from experimental records.
Now with Model Hub, scientists select inputs from their Benchling registry, choose from a curated model library, and run predictions individually or in batches of hundreds. They get back structured results with a full audit trail, all without leaving the platform where the rest of their R&D data lives.
"Access to scientific AI models shouldn't depend on whether your team has the engineering resources to build and maintain the infrastructure to run them," said Mihir Trivedi, Product Manager Scientific AI at Benchling. "Model Hub gives any scientist on Benchling a way to run state-of-the-art models and connect those outputs directly to their experimental record."
Model Hub launches with a curated set of models and new features:
- Open source models include AlphaFold, Chai-1, OpenFold 2, OpenFold 3-preview (developed by the OpenFold Consortium and the AlQuraishi Lab at Columbia University), and Protenix.
- Support for bulk predictions across Benchling data and managed compute on the latest GPU hardware
- Proprietary model access includes a partnership with Boltz PBC, the team behind Boltz-2 and BoltzGen, coming in the weeks following launch. Benchling also intends to make Lilly TuneLab available on the platform, as previously announced.
Benchling will expand the range of available models continuously, bringing in both open source advances and proprietary models from partners as the scientific AI ecosystem moves forward.
New capabilities shipping with Model Hub include:
- Batch Predictions, which allow scientists to submit structure predictions across a candidate library in a single run rather than one sequence at a time.
- Prediction tracking: a centralized log of every model run, input set, and result with timestamps and links to source records.
- MSA (Multiple Sequence Alignment) support, which incorporates evolutionary alignment data to improve prediction quality for structure models. Benchling runs GPU-accelerated MSAs, cutting one of the slowest steps in the prediction pipeline.
- Faster execution through upgraded GPU infrastructure across Model Hub. With this update, you can now run 4X the structure predictions with the same credit allocation.
Model Hub is part of Benchling's broader AI Scientist work: where AI can select, run, and interpret models as a natural part of driving a drug program forward, with scientists directing the science rather than managing infrastructure.
Benchling customers can access Model Hub now by looking for the new icon in the platform navigation bar. New users can explore Model Hub with a sample dataset at Benchling.ai
About Benchling
Benchling is the AI platform for biotech R&D, unifying scientific data and automating workflows to accelerate discovery and development. Trusted by more than 1,300 companies worldwide, from pioneering startups to global leaders like Merck, Moderna, and Sanofi, Benchling gives scientists a single place to capture, connect, and act on data across the entire R&D lifecycle. With Benchling AI, agents and models work directly inside scientific workflows, grounded in structured data. The result is faster teams, better molecules, and breakthroughs that reach the world sooner.
SOURCE Benchling
