# bionemo-examples

**Repository Path**: mirrors_NVIDIA/bionemo-examples

## Basic Information

- **Project Name**: bionemo-examples
- **Description**: BioNeMo NIMs example notebooks: for optimized inference at scale
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-10-10
- **Last Updated**: 2026-03-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## NVIDIA Digital Biology Examples

This repository provides a collection of examples showcasing NVIDIA's suite of digital biology and computational drug discovery solutions. From AI-powered protein folding and molecular generation to structure-based drug design and virtual screening, these examples demonstrate how NVIDIA's accelerated computing platform can transform biological research and pharmaceutical development workflows.

### BioNeMo
NVIDIA BioNeMo is an AI platform for drug discovery that simplifies and accelerates the building and training of models using your own data and scaling the deployment of models for drug discovery applications.

NVIDIA NIM offer a set of optimized microservices for AI models used in drug discovery. These prebuilt containers provide state-of-the-art performance and can be deployed anywhere to go from zero to inference in minutes. Explore our preview APIs, then download the NIMs to deploy on premises or in the cloud as the fastest way to achieve enterprise-grade accelerated AI inference at scale in your drug discovery research.

### Getting Started
To get started with **NVIDIA NIM Microservices**, see the set of example [NIM workflows](examples/nims).

To get started with **NIM Blueprints**, see the [blueprint examples](examples/blueprints).

### Disclaimer
These examples are provided for educational and demonstration purposes only. They are offered as-is without any implied maintenance, support, or warranty. Users are encouraged to adapt and maintain these examples for their own use cases.