
Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.

Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.

Generates high-quality numerical embeddings from text inputs.

ProteinMPNN is a deep learning model for predicting amino acid sequences for protein backbones.

English text embedding model for question-answering retrieval.

Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.

This blueprint shows how generative AI and accelerated NIM microservices can design protein binders smarter and faster.

This workflow shows how generative AI can generate DNA sequences that can be translated into proteins for bioengineering.


Multimodal question-answer retrieval representing user queries as text and documents as images.

Generates a multiple sequence alignment from a query sequence and a protein sequence database search.



The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.

A generative model of protein backbones for protein binder design.

Predicts the 3D structure of a protein from its amino acid sequence.

Predicts the 3D structure of a protein from its amino acid sequence.
