Predict biomolecular structures and affinities from YAML inputs via CLI diffusion workflow.
npx skills add https://github.com/LiorZ/protein-design-skills --skill boltz-liorz
Please help me install this Agent Skill. Skill: boltz Source: https://github.com/LiorZ/protein-design-skills/tree/main/skills/boltz Command: npx skills add https://github.com/LiorZ/protein-design-skills --skill boltz-liorz
Boltz provides open, scalable biomolecular structure and affinity prediction for proteins, nucleic acids, and ligands, enabling researchers to predict folds, interfaces, and binding tendencies without closed-source tools.
Install Boltz and run boltz predict on a YAML input to generate structure and affinity predictions.
High-intent search queries and answers about installing and using this skill.
Biomolecular structure and affinity prediction for protein-ligand complexes uses a diffusion-based CLI workflow on YAML inputs to generate structural models and binding evaluations. It outputs CIF, NPZ, and JSON files for downstream analysis.
Yes, multimer structure prediction supports protein-RNA and protein-DNA complexes alongside monomers and protein-ligands. It handles cross-chain constraints and template-guided predictions for complex biomolecular modeling.
Optional multiple sequence alignments (MSAs) can be generated via ColabFold and integrated into the YAML input schema. These MSAs guide the diffusion-based inference workflow to enhance prediction accuracy for complex folds.
Open biomolecular structure and affinity modeling enables rapid binder design validation and SAR screening. You can apply multi-pocket constraints to evaluate binding tendencies across protein-ligand complexes without closed-source tools.
Yes, template-guided predictions support multi-pocket and cross-chain constraints for complex campaigns. This allows precise modeling of multimer interfaces and protein-ligand binding affinities directly from YAML inputs.
NVIDIA-BioNeMoPredict 3D biomolecular structures for proteins, DNA, RNA, and ligands via OpenFold3 NIM.
LiorZPredict biomolecular complex structures from FASTA-like inputs to CIF and NPZ outputs.
PKU-YuanGroupPredict protein structures for monomers and multimers with AlphaFold2.
NVIDIA-BioNeMoPredict 3D biomolecular structures and ligand binding affinities for proteins, DNA, and RNA using NVIDIA NIM microservices.
