AI-powered optimization of clinical trial design, patient recruitment, site selection, and endpoint analysis. Enables adaptive trials, synthetic control arms, and real-world evidence integration to reduce time and cost.
The FDA issues draft guidance acknowledging AI-generated synthetic control arms as acceptable in certain clinical trial contexts, potentially reducing trial sizes by 20-30% and accelerating rare disease drug development.
Apr 1, 2026 · 6 min read
Commercial platform predicting 175+ ADMET properties with integrated PBPK (GastroPlus) and generative drug design (AIDD module).
Diffusion-based generative model for de novo protein backbone design. Generates novel protein structures conditioned on binding targets, symmetry, or functional sites.
Successor to RFdiffusion using flow matching. Designs enzymes directly from active site geometry (theozyme) specifications.
Inverse folding model: generates amino acid sequences predicted to fold into a target 3D backbone structure. Standard component of all modern protein design pipelines.
Extension of ProteinMPNN that conditions sequence design on bound ligands, small molecules, metals, and nucleotides.
RL + transformer platform for de novo small molecule design. Supports scaffold decoration, R-group replacement, linker design, and multi-parameter optimization.