'binder' 검색 결과

Own whether a multi-document product-architecture binder is internally consistent, complete against its stated customer/contingency/architecture coverage, and honest about which older product ambitions it has absorbed, superseded, deferred, contradicted, orphaned, or rejected. Use when running a binder Architect of Record pass, before an implementation chain cites a binder chapter as ready, when a proof-gate owner is missing, or when reconciling the binder against the older ambition corpus (website, plans, examples, ADRs). NOT for auditing a single Claude Skill bundle's structure or frontmatter (use skill-hygiene), rendering a single-PM accept/reject verdict on one finished deliverable (use port-daddy-user-surrogate-pm-review), or sequencing/stewarding a roadmap's sidequests once a gap or operator decision has been surfaced (use legible-roadmap-with-sidequests).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate new protein, peptide, antibody, or nanobody candidates that bind a chosen target using Boltz. The workflow covers target exploration, design setup, cost estimates, job submission, result downloads, and ranking by predicted binding confidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design new peptide binders for a target
• Generate antibody or nanobody candidates
• Explore binding sites and target input configurations

Screen supplied proteins, peptides, antibodies, or nanobodies against a target with Boltz. Prepares inputs, obtains a cost estimate for approval, downloads results, and ranks candidates by binding confidence, a score for predicted binding reliability.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Rank existing protein binders against a target
• Compare antibody or nanobody candidates
• Screen candidates using target sequences alone

Predict three-dimensional structures of complexes containing proteins, RNA, DNA, and small molecules with Chai-1. Compare candidate structures using confidence scores and clash indicators, or run repeated predictions from Python to evaluate designed binders.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting antibody–antigen complex structures
• Evaluating protein–ligand binding structures
• Reassessing designed binders

Use Boltz-2 to predict structures of complexes containing proteins, DNA, RNA, and small molecules. Obtain structure files and confidence scores to assess designed binders, with optional predictions of binding strength between a protein and a small molecule.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing a designed binder against its target
• Predicting a protein and small-molecule complex
• Predicting complexes containing DNA or RNA

Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fetch and analyze protein structures from RCSB PDB. Use this skill when: (1) Need to download a structure by PDB ID, (2) Search for similar structures, (3) Prepare target for binder design, (4) Extract specific chains or domains, (5) Get structure metadata. For sequence lookup, use uniprot. For binder design workflow, use binder-design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Binder design tool selection and workflow routing guidance. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Binder design campaign planning, monitoring, and troubleshooting. Use this skill when: (1) Planning a complete binder design campaign, (2) Converting high-level goals into runnable pipelines, (3) Assessing campaign health and pass rates, (4) Diagnosing why designs are failing QC, (5) Estimating time, cost, and expected yields, (6) Selecting between design tools for a specific target. This skill orchestrates the other protein design tools. For individual tool parameters, use the specific tool skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design new proteins that bind a chosen surface, using BindCraft's AlphaFold2-guided hallucination or the RFdiffusion backbone plus ProteinMPNN sequence pipeline. Use this skill to specify a target epitope by hotspot residue, trim a receptor to the region worth designing against, set up a design campaign, and filter the output on the in-silico metrics that predict experimental success — interface predicted TM-score, predicted aligned error at the interface, buried surface area, and shape complementarity. Also trigger on BindCraft, RFdiffusion, ProteinMPNN, minibinder, hallucination, inverse folding, hotspot residue, epitope targeting, ipTM, or de novo binder.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run protein structure prediction, molecular design, and docking tools on Tamarind Bio without a local GPU. Guides tool selection, input validation, job submission, batch processing, and result retrieval through its REST API or MCP server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting protein structures in the cloud
• Running binder or antibody design tools
• Submitting batches of sequences for analysis

Predict structures of protein, nucleic acid, and small-molecule complexes with ESMFold2, using single sequences or aligned related sequences. The skill also covers ESMC models for numerical sequence representations, mutation scoring, and predictions of contacts between residues.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting complex structures from single sequences
• Assessing designed binders with ESMFold2-Fast
• Predicting structures using aligned related sequences

Organize care for an aging or ill family member across multiple helpers — the shared care map, a fair rotation with backup rules, the information binder, and family-meeting agendas that prevent the one-sibling-does-everything spiral. Use when asked help me coordinate care for my mom, my siblings and I need to split caregiving, organize care for a sick family member, or set up a care schedule. Produces the care map of needs and coverage, the rotation schedule with escalation rules, the binder outline, and the family meeting agenda.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or chai. For QC thresholds, use protein-qc.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Goal-oriented binder design campaign planning and health assessment. Use this skill when: (1) Planning a complete binder design campaign, (2) Converting high-level goals into runnable pipelines, (3) Assessing campaign health and pass rates, (4) Diagnosing why designs are failing QC, (5) Estimating time, cost, and expected yields, (6) Selecting between design tools for a specific target. This skill orchestrates the other protein design tools. For individual tool parameters, use the specific tool skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Binder design ranking using ipSAE (interprotein Score from Aligned Errors). Use this skill when: (1) Ranking binder designs for experimental testing, (2) Filtering BindCraft or RFdiffusion outputs, (3) Comparing AF2/AF3/Boltz predictions, (4) Predicting binding success rates, (5) Need better ranking than ipTM or iPAE. For structure prediction, use chai or alphafold. For QC thresholds, use protein-qc.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

吉里吉里Z (kirikiri Z) 本体クラス API のリファレンス。TJS2 で吉里吉里Z 上のスクリプトを書く、レビューする、デバッグするときに使う。Layer / Window / Bitmap / System / Storages / Font / Plugins / Timer / Debug / AsyncTrigger / Scripts / BinaryStream / Matrix32 / Matrix44 / Rect / ImageFunction のコア API、サウンド系 (WaveSoundBuffer / SoundBuffer / VideoOverlay)、DrawDevice (BasicDrawDevice / SDLDrawDevice / OGLDrawDevice / GPUDrawDevice / NullDrawDevice)、OpenGL 描画系 (Canvas / Texture / ShaderProgram / Offscreen / VertexBinder / VertexBuffer)、および主要プラグイン提供クラス (HttpRequest / GdiPlus.* / WIN32Dialog / CSVParser / LineParser / Process / Pad / MenuItem / Unzip / Zip / PSD / SimpleHTTPServer) を網羅。**呼び出されたら必ず「共通パターン」と「クロスカッティング概念」を確認し、必要な詳細クラスは doc/reference/*.md を Read しに行くこと。** TJS2 言語そのものや組み込みクラス (Array / Dictionary / Math 等) は別 skill (`tjs2`) を参照。エンジン内部構造 (C++ 実装、レンダリングパイプライン詳細) は対象外。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate new small molecules intended to bind a target protein with Boltz. The workflow covers cost estimation, job submission, result downloads, and ranking candidates by binding confidence or a score for improving promising molecules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate binding candidates without an existing compound library
• Rank generated molecules by binding confidence
• Review solubility and permeability of promising candidates

Run RFDiffusion protein backbone design via NVIDIA NIM. Use for de novo protein backbones, motif scaffolding, binder design, hotspot residues, contigs syntax, diffusion steps, hosted NVIDIA API calls, local Docker deployment, and PDB backbone outputs for ProteinMPNN sequence design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.