The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20^100 possible variants-more combinations than atoms in the observable universe.

Bridging the technical divide in biological engineering Co-founders Tristan Bepler and Tim Lu developed the platform to resolve a persistent operational bottleneck in life science research. Bepler ...

An AI approach developed by researchers from the University of Sheffield and AstraZeneca, could make it easier to design proteins needed for new treatments. Inverse protein folding is a critical ...

The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20 100 possible variants—more combinations than atoms in the observable universe.

In this video, Arc Institute Postdoctoral Fellow Vincent Tran walks through MULTI-evolve, an AI-guided framework that compresses protein engineering from months of iterative experimentation into weeks ...

Proteins have broad potential applications across medicine, materials science, and energy research, but designing proteins with predictable functional properties remains a major challenge. Many ...

CGSchNet, a fast machine-learned model, simulates proteins with high accuracy, enabling drug discovery and protein engineering for cancer treatment. Operating significantly faster than traditional all ...

New platform gives researchers access to the quantity and quality of antibody-antigen affinity and structural data required for next-generation protein engineering models. Atlas by the Numbers Atlas ...

Companies aim to improve drug discovery by training AI on one another’s data and generating large, open datasets ...

The in silico protein design market offers key opportunities in AI-driven drug discovery, personalized medicine, and automation in biological research. With cloud-based simulation growth, partnerships ...