BoltzGen uses generative diffusion models to design protein, peptide, nanobody, and Fab binders against protein and small-molecule targets.
Design linear peptide binders for target proteins using a target sequence-conditioned masked language model. PepMLM generates peptide sequences optimized to bind specific protein targets based on ESM-2 protein language modeling.
PocketXMol is a pocket-interacting generative foundation model for small-molecule or peptide docking and design in protein binding pockets.
PocketFlow is a structure-based molecular generative model that designs novel drug-like molecules within protein binding pockets. It uses autoregressive flow modeling with chemical knowledge to generate 100% chemically valid, highly drug-like compounds.
Design protein binders against a target structure with NVIDIA BioNeMo's Proteina-Complexa generative pipeline.
Structure-based de novo antibody and nanobody design pipeline combining antibody-tuned RFdiffusion, ProteinMPNN sequence design, and antibody-tuned RoseTTAFold2 filtering.
EvoDiff is a diffusion-based protein sequence generation framework from Microsoft Research. ProteinIQ currently runs the EvoDiff-Seq OA_DM_38M model for unconditional protein generation, motif scaffolding, and user-sequence inpainting.
Generate protein structures and scaffolds with Genie 3, an all-atom SE(3)-equivariant diffusion model. Genie 3 supports unconditional protein generation, motif scaffolding, and hotspot-targeted binder design.
All-atom generative AI for designing protein binders. Specify target binding sites and generate diverse binding proteins with fine-grained control over interaction parameters.
Exploratory antibody CDR co-design for antibody-antigen complexes using Proteo-R1 reasoning and raw diffusion. The standard online workflow does not include the framework structure-inpainting assets required for the published-quality target.
BoltzGen uses generative diffusion models to design protein, peptide, nanobody, and Fab binders against protein and small-molecule targets.
Design linear peptide binders for target proteins using a target sequence-conditioned masked language model. PepMLM generates peptide sequences optimized to bind specific protein targets based on ESM-2 protein language modeling.
PocketXMol is a pocket-interacting generative foundation model for small-molecule or peptide docking and design in protein binding pockets.
PocketFlow is a structure-based molecular generative model that designs novel drug-like molecules within protein binding pockets. It uses autoregressive flow modeling with chemical knowledge to generate 100% chemically valid, highly drug-like compounds.
Design protein binders against a target structure with NVIDIA BioNeMo's Proteina-Complexa generative pipeline.
Structure-based de novo antibody and nanobody design pipeline combining antibody-tuned RFdiffusion, ProteinMPNN sequence design, and antibody-tuned RoseTTAFold2 filtering.
EvoDiff is a diffusion-based protein sequence generation framework from Microsoft Research. ProteinIQ currently runs the EvoDiff-Seq OA_DM_38M model for unconditional protein generation, motif scaffolding, and user-sequence inpainting.
Generate protein structures and scaffolds with Genie 3, an all-atom SE(3)-equivariant diffusion model. Genie 3 supports unconditional protein generation, motif scaffolding, and hotspot-targeted binder design.
All-atom generative AI for designing protein binders. Specify target binding sites and generate diverse binding proteins with fine-grained control over interaction parameters.
Exploratory antibody CDR co-design for antibody-antigen complexes using Proteo-R1 reasoning and raw diffusion. The standard online workflow does not include the framework structure-inpainting assets required for the published-quality target.
Configure inputs to begin
Set options on the left, then click “Submit job” — or start from an example.
Source example (CD38 / SAR650984 Fab)
PepMimic designs short peptides that mimic the binding interface of a known protein binder (such as an antibody, nanobody, or natural receptor) on its target protein. A latent diffusion model co-designs each peptide's sequence and all-atom structure to reproduce the reference interface, enabling high-affinity target binding in a small peptide format.
The method is validated across multiple drug targets (PD-L1, CD38, HER2, BCMA, CD4, TROP2) in Kong et al., Nature Biomedical Engineering (2025).
Use PepMimic when you already have at least one structure of your target bound to a binder (an antibody, nanobody, receptor, or any protein partner), and you want to convert that binding interface into a short therapeutic-style peptide.
If you only have an apo target structure, design a binder first (for example with RFdiffusion) and then run PepMimic on the resulting complex.
A,B).B,C for an antibody heavy + light chain).Each generated candidate is returned as a complete protein–peptide complex PDB (the target receptor chains plus the designed peptide), together with:
Candidates are ranked by interface hits (descending), matching the source's native interface_hit.txt sort order.
PepMimic runs through its documented generation entry point (mimic_design.py) and scores candidates with its documented interface-mimicry scorer (evaluation.runner.interface_hit). No scientific logic is reimplemented.
The source's full selection pipeline additionally runs Rosetta and FoldX interface free-energy scoring and a final top-K selection. Those steps require proprietary academic licenses (PyRosetta via Salilab, FoldX via the CRG license with manual yearly renewal) and are therefore not part of the hosted run. Use the returned interface-hit ranking and perplexity to narrow candidates, and run licensed scoring locally if needed for final synthesis decisions.
If you use PepMimic results in published work, please cite the source paper.