Benchling

Benchling released Science Foundries, a way for scientists and AI agents to divide work over the same scientific data and context and run processes such as protein production, lead optimization or process improvement end to end. A user picks a process or goal, then sets which steps agents run and where scientists must approve; agents execute and monitor work, coordinate handoffs and surface key decisions. In a high-throughput antibody example, design, cloning, lab and analysis agents connect to models, a CRO, lab automation and training data, so assay results finished late at night are analyzed before the scientist starts the day.

Benchling released Science Foundries, a way for scientists and AI agents to divide work over the same scientific data and context and run processes such as protein production, lead optimization or process improvement end to end. A user picks a process or goal, then sets which steps agents run and where scientists must approve; agents execute and monitor work, coordinate handoffs and surface key decisions. In a high-throughput antibody example, design, cloning, lab and analysis agents connect to models, a CRO, lab automation and training data, so assay results finished late at night are analyzed before the scientist starts the day.

Benchling's Model Hub lets scientists discover, run and track scientific AI models inside Benchling, with each prediction's inputs, model version and timestamp wired back into the R&D record. Previously, running a model meant waiting for DevOps to stand up infrastructure, and results landed on a shared drive disconnected from the sequence that produced them. The hub currently carries structure prediction models including AlphaFold 2, Chai-1, Boltz-2, OpenFold 2, OpenFold 3 and Protenix, plus property prediction models such as Lilly TuneLab, Inductive Bio and open-source DeepSP, with batch prediction and a v3 alpha API.

Benchling's Model Hub lets scientists discover, run and track scientific AI models inside Benchling, with each prediction's inputs, model version and timestamp wired back into the R&D record. Previously, running a model meant waiting for DevOps to stand up infrastructure, and results landed on a shared drive disconnected from the sequence that produced them. The hub currently carries structure prediction models including AlphaFold 2, Chai-1, Boltz-2, OpenFold 2, OpenFold 3 and Protenix, plus property prediction models such as Lilly TuneLab, Inductive Bio and open-source DeepSP, with batch prediction and a v3 alpha API.