Prescient Design

Gene Ontology prediction for malaria parasite proteins, trained on SAR supergroup structure embeddings with calibrated uncertainty and FDR control.

Multimodal tokenizer for antibody CDR loops, encoding backbone dihedrals and sequence as discrete tokens that plug into antibody language models.

Antibody, nanobody, and T-cell receptor structure prediction that resolves bound and unbound conformations separately in under a second per domain.

Protein-protein interface embedding model built on Delaunay graphs, reused frozen for antibody-antigen affinity and antibody viscosity prediction.

Generative masked protein language model with an interpretable concept layer, letting designers set 718 biophysical and annotation concepts directly.

Walk-jump sampler that runs molecular dynamics in a smoothed, noised space of all-atom coordinates to generate peptide conformational ensembles.

Efficient protein language model library from Prescient Design enabling high-quality sequence representations and fitness prediction in 24 GPU hours.

Discrete generative model for antibody protein sequences combining MCMC walks on a smoothed energy landscape with one-step denoising jumps.

Protein structure prediction model pairing SE(3)-equivariant networks with a coarse-grained representation to fold sequences fast, without MSA inputs.