AI-driven design and automation, built for discovery

3D physics-based equivariant generative diffusion

State-of-the-art prediction of protein structures and protein-ligand complexes, including conformational response to binding, uncovering new mechanisms of action, including allostery and cryptic pockets

Graph neural network architecture based on quantum features

Protein-ligand binding energetics, 1000x faster than conventional quantum chemistry methods such as density functional theory (DFT), without compromising accuracy

Massively pre-trained graph neural network, deployed across dozens of drug properties for lead optimization, and supported by automated training, uncertainty quantification and explainability

State-of-the-art data efficiency

Suite of generative technologies, fully aware of the chemical space efficiently accessible through our high-throughput chemistry platform

Optimally exploring chemical space, while ensuring rapid and successful execution on our platform