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

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

World-class multilingual and cross-lingual question-answering retrieval.

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

Generates embeddings of proteins from their amino acid sequences.

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

This blueprint shows how generative AI and accelerated NIM microservices can design optimized small molecules smarter and faster.

NV-DINOv2 is a visual foundation model that generates vector embeddings for the input image.

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

English text embedding model for question-answering retrieval.

Multilingual text question-answering retrieval, transforming textual information into dense vector representations.

NV-CLIP is a multimodal embeddings model for image and text.

Generates high-quality numerical embeddings from text inputs.

Embedding model for text retrieval tasks, excelling in dense, multi-vector, and sparse retrieval.

A generative model of protein backbones for protein binder design.

Optimized community model for text embedding.

GPU-accelerated generation of text embeddings used for question-answering retrieval.

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

Predicts the 3D structure of how a molecule interacts with a protein.