AI tools akin to ChatGPT are dramatically changing the way in which text, images, and code are generated. Similarly, machine learning algorithms and generative AI are disrupting conventional methods in life sciences and accelerating timelines in drug discovery and materials development.

DeepMind’s AlphaFold is arguably probably the most renowned machine learning model on this domain. It predicts a protein’s 3D structure from its amino acid sequence and has been utilized by over one million researchers within the 18 months since its public release. Quite a few other AI tools have emerged since then, including the recently open-sourced RFDiffusion, which allows researchers to generate computational protein designs using only their laptops.

Nonetheless, translating these computational designs into tangible, functional proteins stays a challenge. Adaptyv Bio goals to handle this issue with its next-generation protein foundry. By integrating advanced robotics, microfluidics, and artificial biology techniques, Adaptyv Bio is constructing a full-stack platform to enable protein engineers to validate their AI-generated protein designs.

Julian Englert, CEO and co-founder of Adaptyv Bio, said,

AI models thrive on data for training and improving their predictions. By simplifying the technique of generating data concerning the effectiveness of designed proteins, Adaptyv Bio enables protein engineers and AI models to receive more feedback about their designs, guiding them toward better-performing proteins.

Englert added,

Adaptyv Bio was established by a bunch of engineers from EPFL, the Swiss Federal Institute for Technology in Lausanne, motivated by the time-consuming processes of conducting biological experiments in labs. In 2022, they secured $2.5 million in pre-seed funding from Wingman Enterprise, after participating in Y Combinator, the world’s most selective startup accelerator. The team has since expanded to 12 engineers with diverse backgrounds in synthetic biology, microengineering, software development, and machine learning. The corporate is situated on the newly constructed Biopole life science campus in Lausanne, Switzerland, where they’re developing their technology in cutting-edge lab facilities with picturesque views of Lake Geneva and the Swiss-French Alps.

Adaptyv Bio’s foundry centers around protein engineering workcells—custom, automated setups that miniaturize processes typically requiring multiple laboratory machines, performing them in parallel on tiny microfluidic chips. Users can write experimental protocols (or have AI write them) and the workcells execute the experiments autonomously, while closely controlling and monitoring the experiments’ parameters. All measurement data is mechanically processed and uploaded to permit users to refine their machine learning models with each experiment.

Englert said,

To further speed up the sector of protein engineering, Adaptyv Bio has open-sourced two of their internal tools which have already began gaining traction amongst researchers and engineers in the sector. ProteinFlow is a Python library that permits protein designers to simply create high-quality datasets for higher AI models. Automancer is an extensible software platform to run automated experiments, enabling researchers to construct their very own experimental protocols and integrate different laboratory instruments.

added Julian Englert.