「smil」の検索結果

Validate Jira/JPD ticket reference and create a feature branch

日本語の概要は準備中です。原文の説明を表示しています。

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

日本語の概要は準備中です。原文の説明を表示しています。

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

日本語の概要は準備中です。原文の説明を表示しています。

Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.

日本語の概要は準備中です。原文の説明を表示しています。

Diffusion-based docking that predicts protein-ligand poses without a predefined site. Use for blind docking, when traditional docking fails, or exploring multiple binding modes. Pipeline: prep protein (PDB) and ligand (SMILES/SDF), run inference, analyze confidence-ranked poses.

日本語の概要は準備中です。原文の説明を表示しています。

Molecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN, Graphormer), pharmacophores. Scikit-learn compatible with parallelization/caching. For QSAR, virtual screening, similarity, and molecular DL.

日本語の概要は準備中です。原文の説明を表示しています。

Query IUPHAR/BPS Guide to Pharmacology (GtoPdb) for receptor-ligand interactions, target/ligand metadata, families, and approved drugs. Affinities (pKi/pIC50/pKd), action (Agonist/Antagonist/etc.), species, structures (SMILES/InChI). No auth. Always resolve targets via geneSymbol/accession; most metadata lives in sub-resources (/databaseLinks, /structure, /synonyms).

日本語の概要は準備中です。原文の説明を表示しています。

Cheminformatics toolkit for molecular analysis and virtual screening: SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints (Morgan/ECFP, MACCS), Tanimoto similarity, SMARTS substructure filtering, Lipinski drug-likeness, reaction enumeration, 2D/3D coordinates. For simpler API use datamol; use RDKit for fine-grained sanitization, custom fingerprints, or SMARTS/reaction control.

日本語の概要は準備中です。原文の説明を表示しています。

Query PubChem (110M+ compounds) directly via the PUG-REST/JSON API with plain `requests` — no SDK install required. Search by name/CID/SMILES/InChIKey/formula, retrieve properties (MW, XLogP, TPSA, H-bond counts), do similarity/substructure searches with async ListKey polling, fetch synonyms, descriptions, assay summaries, and download SDF/PNG. For local cheminformatics use rdkit; for bioactivity-centric workflows use chembl-database-bioactivity.

日本語の概要は準備中です。原文の説明を表示しています。

Parse local DrugBank XML for drug info, interactions, targets, and properties. Search by ID/name/CAS, extract DDIs with severity, map targets/enzymes/transporters, compute SMILES similarity. Primary via local XML; REST API rate-limited (3k/month dev). For live bioactivity use chembl-database-bioactivity; for compound properties use pubchem-compound-search.

日本語の概要は準備中です。原文の説明を表示しています。

Query the ZINC22 virtual compound library (CartBlanche API, billions of make-on-demand + purchasable molecules). Look up substances by ZINC ID, resolve a SMILES to its ZINC ID (exact match), inspect purchasability/catalogs, and assemble compound sets for docking. Property (MW/logP) filtering is done locally with RDKit. For bioactivity use chembl-database-bioactivity; for approved drugs use drugbank-database-access.

日本語の概要は準備中です。原文の説明を表示しています。

Validate and repair source-locked Blender models using named landmarks such as leaf tips, shell corners, eyes, smile, rim thickness, aura center/radius, and view-specific depth markers. Use when bbox/IoU is insufficient and the model must align to templates at designed feature points.

日本語の概要は準備中です。原文の説明を表示しています。

Filter rows of a Datagrok DataFrame via the datagrok_exec tool through the Filters panel — by range, equals/contains/in-set, multi-value, boolean, free-text row expressions, or substructure (SMILES / SMARTS / molblock). Also covers clearing, inverting, the show-only-filtered vs destructive-drop split, and the filter event lifecycle (onRowsFiltering / onFilterChanged / onRowsFiltered). Use whenever the user says "filter", "show only", "hide rows where", "narrow to subset", "find rows that", "contains", "substructure search", "categorical filter", "range filter", "invert", "clear the filter", "clear filters", "drop rows", or asks for the filtered subset as a new table. Does NOT cover selection (separate skill) or generic DataFrame cloning (datagrok-df-and-columns).

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used when the user asks to "animate an SVG", "make a line draw itself on", "do a stroke draw-on / signature animation", "morph one shape into another", "move an element along a path", "animate an icon/logo", or "animate an SVG gradient or filter". Covers stroke-dashoffset draw-on, path morphing, motion-along-path, and animated icons/gradients/filters via CSS, SMIL, and GSAP.

日本語の概要は準備中です。原文の説明を表示しています。

Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds and scaffold splits, BRICS/RECAP fragmentation, 3D conformer generation, SDF/CSV/Excel and cloud I/O, and parallel processing via n_jobs. Returns native rdkit.Chem.Mol objects, so it composes with RDKit throughout. Also trigger on datamol, `import datamol as dm`, dm.to_mol, dm.standardize_mol, dm.cluster_mols, or dm.pick_diverse. For advanced control or custom parameters, use the rdkit skill directly.

日本語の概要は準備中です。原文の説明を表示しています。

Molecular featurization hub with one consistent interface over 100+ featurizers. Fingerprints (ECFP/Morgan, MACCS, atom pair, topological torsion, Avalon, RDKit, ERG), RDKit and Mordred descriptor sets, pharmacophore and 3D shape descriptors, scaffold keys, and pretrained embeddings (ChemBERTa, ChemGPT, MolT5, GIN, Graphormer) through a common transformer API with caching and parallelism. Use this skill to convert SMILES into model-ready feature matrices for QSAR, virtual screening, and molecular ML, and to choose between featurizer families. Also trigger on molfeat, MoleculeTransformer, FPVecTransformer, PretrainedHFTransformer, molfeat model store, or featurizer selection.

日本語の概要は準備中です。原文の説明を表示しています。

Query the ChEMBL database web services for measured bioactivity data, compound records and calculated properties, targets, assays, mechanisms of action, drug indications and warnings. Use this skill to build curated SAR or QSAR datasets for a target, look compounds up by SMILES, InChIKey, name, or ChEMBL id, run similarity and substructure searches, and check what chemistry is already known against a protein. Also trigger when a query mentions ChEMBL ids (CHEMBL...), pChEMBL values, IC50/Ki/Kd/EC50 retrieval, assay confidence scores, or ebi.ac.uk/chembl.

日本語の概要は準備中です。原文の説明を表示しています。

DiffDock and DiffDock-L diffusion-based molecular docking. Use for blind protein-small-molecule pose prediction from a PDB file or sequence plus SMILES/SDF/MOL2, batch docking over a CSV of complexes, virtual screening triage, sampling multiple poses per complex, and reading the confidence score correctly. Also trigger on DiffDock, DiffDock-L, inference.py, confidence_model, samples_per_complex, ESM embedding preparation for docking, or blind docking without a defined box. Not for binding affinity prediction — the confidence score ranks pose plausibility, not potency.

日本語の概要は準備中です。原文の説明を表示しています。

用于实现或审查 SVG 动画、路径绘制、图标动效、Logo 动画、插画动效、微交互、CSS/SMIL/Framer Motion/GSAP 选型、动效无障碍或降级行为；中文触发词包括 SVG 动画、路径描边、动效。

日本語の概要は準備中です。原文の説明を表示しています。

Use when implementing or reviewing SVG animation, path drawing, icon motion, logo animation, illustration motion, micro-interactions, CSS/SMIL/Framer Motion/GSAP choices, motion accessibility, or fallback behavior; Chinese triggers include SVG animation, path stroke, animation.

日本語の概要は準備中です。原文の説明を表示しています。

Refresh the managed Spec Kit section in coding agent context file(s)

日本語の概要は準備中です。原文の説明を表示しています。

Execute the implementation plan by processing and executing all tasks defined in tasks.md

日本語の概要は準備中です。原文の説明を表示しています。

Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.

日本語の概要は準備中です。原文の説明を表示しています。

Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.

日本語の概要は準備中です。原文の説明を表示しています。