Skills for NVIDIA BioNeMo
A decade of NVIDIA life-sciences libraries, NIMs, and open models as portable agent skills: structure prediction, docking, generative chemistry, genomics, and de novo protein design. The plugin helps an agent pick the right tool, prepare inputs, run it, and interpret outputs.
Skills 31 in the ChatGPT listing
Boltz2 nim Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Complexa binder design Run a complete protein binder design campaign with NVIDIA Proteina-Complexa: resolve a target structure and hotspots from a name/sequence/PDB, co-design binder sequence+structure with reward-guided test-time search (best-of-n, beam search, FK steering, MCTS), select with the internal AF2 reward gate, then INDEPENDENTLY validate each binder by refolding the complex with Boltz2 (default) or OpenFold3 and rank on interface confidence, pLDDT, ipSAE, apo/holo stability, and hotspot contact. Complexa design End-to-end Proteina-Complexa design pipeline driver. Complexa evaluate pdbs Standalone evaluation of an existing PDB directory with Proteina-Complexa. Complexa setup First-time setup, environment configuration, and model-weight installation for Proteina-Complexa. Complexa sweep Use this skill whenever the user wants to run a parameter sweep over a Proteina-Complexa design pipeline — cartesian-product hyperparameter scans, Pareto search over generation/reward/evaluation knobs, or any "compare configurations" workflow. Complexa target Use this skill whenever the user wants to add, register, edit, list, show, or validate a Proteina-Complexa design target for any pipeline — protein binder (default), ligand binder, or AME / enzyme scaffolding. cuequivariance Define custom groups (Irrep subclasses), build segmented tensor products with CG coefficients, create equivariant polynomials and IrDictPolynomials, and use built-in descriptors (linear, tensor products, spherical harmonics). Diffdock nim Run DiffDock molecular docking via NVIDIA NIM to predict small-molecule binding poses against protein targets. Drug discovery pipeline Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. Evo2 nim Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice. Genmol nim Generate novel drug-like molecules using the GenMol NIM microservice. Genomics workflow acceleration Use when accelerating existing genomics workflows with NVIDIA Parabricks, improving runtime or price/performance, converting pipeline steps to GPUs, or comparing CPU and GPU workflow outputs. Kermt add cmim pretrain Convert a grover_base checkpoint (encoder-only or encoder + vocab heads) into a hybrid checkpoint by adding a randomly-initialized cMIM decoder + latent_dist, then continue pretraining on the user's corpus as hybrid (vocab + contrast). Kermt continue pretrain Continue pretraining from an existing KERMT checkpoint. Kermt embed Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint (grover_base / cmim / hybrid / finetuned). Kermt finetune Finetune a pretrained KERMT encoder on a labeled CSV. Kermt infer Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. Kermt monitor Check progress for a detached KERMT run (pretrain, finetune, or any kermt_run_detached invocation). Kermt pretrain scratch Pretrain a fresh KERMT model from scratch on a user-provided corpus. Kermt setup Bootstrap the KERMT agent environment — verify host docker + nvidia-container-toolkit, build the kermt:latest image from the repo's Dockerfile if it doesn't yet exist, and run a GPU smoke test inside the container. Molmim nim Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Msa search nim Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM. Msa structure prediction pipeline Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Nvmolkit usage Write code that calls the installed nvMolKit Python API for GPU-accelerated, batched RDKit-style operations - Morgan fingerprints, Tanimoto/cosine similarity, ETKDG conformer embedding, MMFF/UFF optimization, TFD, conformer RMSD, Butina clustering, and substructure search. Openfold2 nim Use this skill for OpenFold2, NVIDIA's BioNeMo NIM microservice for monomer protein structure prediction. Openfold3 nim Use this skill for OpenFold3, NVIDIA's BioNeMo NIM microservice for biomolecular structure prediction. parabricks Route NVIDIA Parabricks pbrun tools, assess GPU/runtime readiness, and provide version-aware command guidance for FASTQ/BAM processing, RNA-seq, variant calling, BAM QC, and GVCF workflows. Protein binder design Orchestrate an end-to-end de novo protein binder design campaign against a protein target by composing BioNeMo NIM skills. Proteinmpnn nim Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Rfdiffusion nim Run RFDiffusion protein backbone design via NVIDIA NIM. 
Inductive Bio#285