Browse and apply state-of-the-art machine learning methods for protein design, structure prediction, and sequence optimization.
Showing 14 of 14 methods
DeepMind's latest structure prediction model supporting proteins, DNA, RNA, and small molecules with unprecedented accuracy.
All-atom diffusion-based generative model developed for de novo design of protein and peptide binders across a wide array of target types.
A flexible de novo binder design workflow using RFdiffusion for backbone generation with high success rates across diverse protein targets.
Protein binder design pipeline that can be used to design miniprotein and peptide binders with high affinity and specificity.
Open-source framework for combining and composing a variety of structure prediction and other models for design, filtering, or guidance.
Fine-tuning pipeline combining reinforcement learning algorithms (GRPO, DPO) with the ZymCTRL protein language model for enzyme design.
Protein language model trained with masked diffusion to enable both high-quality representation learning and generative protein design.
ESM2 is a masked language model trained on UniRef protein sequences. Used for structure prediction, property prediction, and functional annotation.
Motif-scaffolding pipeline that designs protein sequences around a given motif using the EvoDiff model for sequence-space diffusion.
BLOSUM (BLOcks SUbstitution Matrix) matrix is a substitution matrix used for sequence alignment of proteins, derived from conserved protein blocks.
A reasoning model that proposes modifications to improve protein stability by analyzing structural context and evolutionary conservation.
Antibody-specific transformer language model pre-trained on 558M natural antibody sequences for antibody design and optimization.