> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nim/bionemo/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/bionemo/_mcp/server. # BioNeMo NIM Overview NVIDIA BioNeMo NIMs are a collection of GPU-accelerated inference microservices that bring state-of-the-art AI models for biology and drug discovery behind simple, consistent APIs. Each NIM packages a leading model as a self-hosted, easily deployable container, and because they share a common interface, they can be chained together to build complete *in silico* drug discovery pipelines spanning structure prediction, molecular generation, protein design, genomic analysis, and molecular docking. ## Use Cases BioNeMo NIMs span structure prediction, molecular generation and design, genomics, and docking. ### Structure Prediction Use these NIMs when you need 3D structures or alignments from sequence: Predict protein structures from amino acid sequences. Predict protein-complex structures from amino acid sequences. An open, PyTorch reimplementation of AlphaFold2 at parity accuracy with improved speed. Predicts complete biomolecular complexes of proteins, DNA, RNA, and small-molecule ligands. Predicts biomolecular complexes and their binding affinity. Fast, GPU-accelerated multiple sequence alignment that the folding models rely on to improve accuracy. **Benefits**: Shared HTTP APIs, GPU-accelerated inference, pipeline-ready MSA and folding outputs ### Molecular Generation and Design Use these NIMs when you need novel small molecules or protein backbones and sequences: Generates novel small molecules through a clustered latent space for controlled, property-guided generation. Generates small molecules through fragment-based (SAFE) masked-diffusion generation. Generates novel protein backbones. Generates novel protein backbones and sequences for tasks such as motif scaffolding, binder design and antibody CDR design. Predicts amino acid sequences that fold into a given protein structure. **Benefits**: Controlled generation, fragment and property constraints, protein design chaining ### Genomics and Docking Use these NIMs when you need genomic interpretation or protein–ligand poses: A genomic foundation model that interprets and generates DNA across scales, from individual molecules to entire genomes. Predicts protein-ligand binding poses for molecular docking. **Benefits**: Sequence-to-function exploration and docking poses with confidence estimates