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@@ -13,6 +13,7 @@ A collection of AWS HealthOmics workflows to accelerate drug discovery.
 - [Alphafold2-Multimer](https://github.com/aws-samples/drug-discovery-workflows/tree/main/assets/workflows/alphafold2-multimer): From Google DeepMind. Predict the 3D structure of multi-chain protein complexes.
 - [AlphaBind](https://github.com/A-Alpha-Bio/alphabind): From A-Alpha Bio. Predict and optimize antibodu-antigen binding affinity.
 - [AMPLIFY Pseudo Perplexity](https://github.com/chandar-lab/AMPLIFY): From Amgen and Mila. Calculate the pseudoperplexity of an amino acid sequence using a protein language model.
+- [BioNeMo NiM Protein Design](https://docs.nvidia.com/nim/#bionemo) Use BioNeMo NiM containers to design proteins using RFDifusion, ProteinMPNN, and AlphaFold-Multimer.
 - [ESMfold](https://github.com/aws-samples/drug-discovery-workflows/tree/main/assets/workflows/esmfold): From Meta. Rapidly predict protein structures using embeddings geneted by the ESM2 protein language model.
 - [EvoProtGrad](https://github.com/NREL/EvoProtGrad): From NREL. Directed evolution on a protein sequence with gradient-based discrete Markov chain monte carlo (MCMC).
 - [Generate Protein Sequence Embeddings](https://github.com/aws-samples/drug-discovery-workflows/tree/main/assets/workflows/generate-protein-seq-embeddings):  From Meta. Generate ESM-2 vector embeddings for one or more protein amino acid sequences.