ESM-2 is a 650M parameter protein language model from Meta AI trained on 250M protein sequences. Generate rich sequence representations for downstream tasks like structure prediction, function annotation, and variant effect prediction.

Inverse folding with ESM-IF1. Design protein sequences for given 3D backbone structures using a geometric deep learning model. Generate multiple sequence variants optimized for your target structure.

Generate hydrophobicity plots using 24 different amino acid scales. Visualize hydrophobic and hydrophilic regions for protein analysis, epitope prediction, and membrane protein studies.

All-atom generative diffusion model for protein design with complex constraints. Design binders, enzymes, and symmetric protein assemblies.

Open-source structure prediction neural network for proteins, nucleic acids, and small molecules. State-of-the-art accuracy with multi-chain support.

Predict pKa values of ionizable groups in proteins based on 3D structure. PROPKA calculates the pKa shifts caused by the protein environment for ASP, GLU, HIS, CYS, TYR, LYS, ARG, and terminal groups.