
How AI is solving the protein folding problem and enabling rational design of therapeutics.
7 parts • Hover to view individual progress
Biology is becoming programmable. This 7-part series explores how generative AI is solving the protein folding problem (AlphaFold), enabling inverse folding (designing proteins with specified functions), and accelerating drug discovery through small-molecule design, antibody engineering, and virtual labs. Discover the flywheel effect where AI-designed therapeutics power a feedback loop of continuous improvement.

From linguistics to biophysics—understanding how Protein Language Models learn the deep grammar of biology from 200M+ sequences.

Solving the inverse folding problem with 3D diffusion models to design novel protein functions from first principles.

Moving beyond brute-force HTS to intelligent, 3D-equivariant generation of optimized small molecule drugs.

Co-generating binding and developability with multi-objective antibody design platforms.

Testing 1,000 drug candidates in one day using Digital Twin systems biology simulations and proteome-wide toxicity screening.

The self-driving laboratory flywheel that connects AI theory to experimental facts through active learning.

Partner with the platform: Two tangible first projects to accelerate your R&D pipeline today.