Exécutez n'importe quel Skill dans Manus
en un clic

Exécutez n'importe quel Skill dans Manus en un clic

boltz2-biomolecular-interactions

Étoiles31

Forks8

Mis à jour18 juin 2026 à 18:53

Run Boltz-2 biomolecular interaction predictions for protein, nucleic-acid, ligand, and complex structures with binding-affinity outputs. Use for hit discovery, binder-versus-decoy prioritization, hit-to-lead comparisons, lead optimization, complex modeling, or reproducible Boltz YAML and batch inference workflows.

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

Exécuter dans Manus

Source

mdbabumiamssm

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Ouvrir le dépôt GitHub Voir les dépôts du créateur

Téléchargement

Exécuter dans Manus

Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

Explorateur de fichiers

3 fichiers

SKILL.md

readonly

name	boltz2-biomolecular-interactions
description	Run Boltz-2 biomolecular interaction predictions for protein, nucleic-acid, ligand, and complex structures with binding-affinity outputs. Use for hit discovery, binder-versus-decoy prioritization, hit-to-lead comparisons, lead optimization, complex modeling, or reproducible Boltz YAML and batch inference workflows.

Boltz-2 Biomolecular Interactions

Use Boltz-2 when structure and affinity need to be modeled together. Treat predictions as prioritization evidence, not assay results.

Workflow

Define the decision:
- complex structure prediction;
- binder-versus-decoy ranking for hit discovery;
- relative affinity comparison for hit-to-lead or lead optimization.
Standardize every input:
- verify polymer sequences and chain identities;
- preserve ligand stereochemistry, protonation assumptions, and identifiers;
- document covalent bonds, templates, restraints, and modified residues.
Create one Boltz YAML per complex or a directory for batch inference.
Pin the Boltz package or container version. Do not rely on the latest model changing silently.
Run prediction with an approved MSA path. Record whether the public MSA server, a private server, or precomputed MSAs were used.
Interpret affinity outputs according to their training objective:
- use affinity_probability_binary to separate likely binders from decoys;
- use affinity_pred_value to compare affinity among plausible binders and close analogs.
Inspect structural confidence, interface plausibility, ligand pose, clashes, chemistry, and agreement across replicates.
Compare with orthogonal docking, physics-based calculations, known SAR, and biochemical assays before advancing compounds.

Affinity Semantics

The official repository states that affinity_pred_value is reported as log10(IC50) derived from IC50 measured in micromolar. Do not mix it with pKd, binding free energy, or untransformed IC50 values.

Guardrails

Do not compare affinity values across incompatible assay types or unrelated target systems without calibration.
Do not rank molecules that differ because of unresolved protonation, tautomer, stereochemistry, or covalent-state errors.
Do not use a high structural confidence score as proof of binding.
Keep train/test leakage and benchmark overlap in mind when evaluating known complexes.
Flag unsupported chemistry, missing atoms, low-complexity sequences, and MSA failures.
Require experimental confirmation before medicinal-chemistry or clinical decisions.

Output Contract

Return input provenance, Boltz version, YAML and MSA settings, structural confidence, affinity fields with units and semantics, QC findings, ranked candidates, and the experimental validation plan.

Read references/operations.md for installation, command patterns, affinity interpretation, and canonical sources.

Plus depuis ce dépôt

même dépôt

medsam2-3d-segmentation

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Operate MedSAM2 for promptable segmentation of 3D medical images and medical videos, including CT lesion propagation, MRI volumes, RECIST-guided prompts, efficient CPU-oriented variants, training, and 3D Slicer integration. Use when generating or validating volumetric masks from sparse prompts or propagating masks through image slices or video frames.

2026-06-1831

monai-medical-imaging

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Build reproducible healthcare imaging pipelines with Project MONAI for DICOM, NIfTI, pathology, and multidimensional imaging tasks including preprocessing, augmentation, training, sliding-window inference, evaluation, model bundles, labeling, and deployment. Use when implementing medical image classification, segmentation, registration, detection, generative, or foundation-model workflows in PyTorch.

2026-06-1831

txgemma-therapeutics

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Operate Google TxGemma prediction and chat models for therapeutic property prediction across small molecules, proteins, nucleic acids, diseases, targets, and cell lines. Use when formatting Therapeutics Data Commons tasks, choosing TxGemma model size or variant, running local or Model Garden inference, fine-tuning on private therapeutic data, or evaluating TxGemma in drug-discovery workflows.

2026-06-1831

opencrispr-gene-editors

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Evaluate and operate released Profluent OpenCRISPR gene-editing systems, especially OpenCRISPR-1, for controlled research workflows using its published Cas9-like protein, compatible guide RNA designs, protocols, licensing, specificity testing, and experimental validation. Use when comparing OpenCRISPR-1 with SpCas9, planning nonclinical editing studies, or assessing use in nuclease, nickase, deactivated, base, prime, or epigenome-editing contexts.

2026-06-1831

transcriptformer-cell-embeddings

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Operate CZI TranscriptFormer cross-species generative single-cell models to produce cell embeddings, contextual gene embeddings, likelihoods, zero-shot classifiers, disease-state representations, and regulatory analyses from raw-count AnnData files. Use when selecting TF-Sapiens, TF-Exemplar, or TF-Metazoa, processing in- or out-of-distribution species, or scaling embedding extraction across GPUs.

2026-06-1831

protenix-structure-prediction

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Operate ByteDance Protenix-v2 for open biomolecular structure prediction of proteins, antibodies, nucleic acids, ligands, and complexes using JSON inputs, MSA and template features, constraints, and inference-time sampling. Use when running Protenix locally or through its server, comparing AlphaFold3-style open models, or building reproducible co-folding evaluations.

2026-06-1831

name	boltz2-biomolecular-interactions
description	Run Boltz-2 biomolecular interaction predictions for protein, nucleic-acid, ligand, and complex structures with binding-affinity outputs. Use for hit discovery, binder-versus-decoy prioritization, hit-to-lead comparisons, lead optimization, complex modeling, or reproducible Boltz YAML and batch inference workflows.

Boltz-2 Biomolecular Interactions

Use Boltz-2 when structure and affinity need to be modeled together. Treat predictions as prioritization evidence, not assay results.

Workflow

Define the decision:
- complex structure prediction;
- binder-versus-decoy ranking for hit discovery;
- relative affinity comparison for hit-to-lead or lead optimization.
Standardize every input:
- verify polymer sequences and chain identities;
- preserve ligand stereochemistry, protonation assumptions, and identifiers;
- document covalent bonds, templates, restraints, and modified residues.
Create one Boltz YAML per complex or a directory for batch inference.
Pin the Boltz package or container version. Do not rely on the latest model changing silently.
Run prediction with an approved MSA path. Record whether the public MSA server, a private server, or precomputed MSAs were used.
Interpret affinity outputs according to their training objective:
- use affinity_probability_binary to separate likely binders from decoys;
- use affinity_pred_value to compare affinity among plausible binders and close analogs.
Inspect structural confidence, interface plausibility, ligand pose, clashes, chemistry, and agreement across replicates.
Compare with orthogonal docking, physics-based calculations, known SAR, and biochemical assays before advancing compounds.

Affinity Semantics

Guardrails

Do not compare affinity values across incompatible assay types or unrelated target systems without calibration.
Do not rank molecules that differ because of unresolved protonation, tautomer, stereochemistry, or covalent-state errors.
Do not use a high structural confidence score as proof of binding.
Keep train/test leakage and benchmark overlap in mind when evaluating known complexes.
Flag unsupported chemistry, missing atoms, low-complexity sequences, and MSA failures.
Require experimental confirmation before medicinal-chemistry or clinical decisions.

Output Contract

Return input provenance, Boltz version, YAML and MSA settings, structural confidence, affinity fields with units and semantics, QC findings, ranked candidates, and the experimental validation plan.

Read references/operations.md for installation, command patterns, affinity interpretation, and canonical sources.