Boltz

Boltz PBC is a public-benefit AI research company that develops open-source biomolecular foundation models to accelerate drug discovery. The company's origins trace back to MIT, where the initial research for its models was conducted. The founders include Gabriele Corso, a former computer science PhD student at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL); Saro Passaro, also a former research scientist at MIT; and Jeremy Wohlwend. Their research focused on applying machine learning to molecular biology.

Boltz provides a platform, Boltz Lab, which integrates its AI models and generative workflows for tasks like protein structure prediction, binding affinity estimation, and the design of small molecules and biologics. The company's core products include Boltz-1, an open-source model for predicting the 3D structure of biomolecular complexes ; Boltz-2, which adds the capability to predict binding affinity with high speed and accuracy ; and BoltzGen, a generative model for designing novel proteins and peptides to bind with specific targets. These tools are designed to be used by scientists in pharmaceutical companies, biotechs, and academic labs to shorten discovery timelines and reduce the costs associated with early-stage drug development.

The company operates on a business model that involves building and operating software, explicitly stating it will not develop its own drugs to avoid competing with its users. Revenue is generated through partnerships and providing access to its platform and custom models. In January 2026, Boltz announced a $28 million seed round with backing from Zetta, Amplify, a16z, and others. Concurrently, it launched Boltz Lab and announced a strategic collaboration with Pfizer. This partnership allows Pfizer to use Boltz's platform and collaborate on creating new, exclusive foundation models using Pfizer's extensive data.

Keywords: biomolecular AI, drug discovery, protein design, computational chemistry, foundation models, open-source AI, binding affinity prediction, small-molecule design, generative AI, biologics design, protein structure prediction, preclinical research, AI-powered drug discovery, molecular modeling, therapeutic design, pharmaceutical AI, biotech software, de novo protein design, computational drug discovery, biomolecular interaction