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概要と使いどころ
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
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Enumerates virtual chemical libraries via reaction SMARTS transformations using RDKit and Reaction templates, with explicit handling of atom mapping, template extraction (RDKit reaction mining), product validation, RECAP/BRICS fragmentation, R-group decomposition, matched molecular pair analysis (MMPA), and Free-Wilson analysis. Use when generating combinatorial libraries from building blocks, enumerating analog series, deriving structure-activity rules, or extracting transformations from reaction data.
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Designs QC, corrects signal drift, removes batch effects, filters features, normalizes samples, and imputes missing values for untargeted LC-MS/GC-MS metabolomics, framing each step as a measurement model that can create or erase biological signal. Use when processing a peak/feature table before statistical analysis, choosing a drift-correction or sample-normalization method, deciding QC RSD vs D-ratio filtering, or handling left-censored missing values. The feature table is produced by metabolomics/xcms-preprocessing or metabolomics/msdial-preprocessing; transformation/scaling for modeling defers to metabolomics/statistical-analysis; cross-study design issues link to experimental-design/batch-design.
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Searches molecular libraries for substructure matches using SMARTS patterns with explicit handling of recursive SMARTS, ring membership, aromaticity dialect, vector binding, atom map indices, and reactive/PAINS/REOS/Brenk/Aldridge filter catalogs. Use when filtering compounds by pharmacophore features, functional groups, scaffold matches, or screening for assay-interference / structural alerts.
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Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).
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Analyzes chemical libraries by scaffold using Bemis-Murcko scaffolds, generic frameworks, cyclic skeletons, matched molecular pair (MMP) analysis via mmpdb, R-group decomposition, Free-Wilson analysis, scaffold hopping, and chemotype-aware ML train/test splits. Use when identifying chemotype clusters in a library, deriving SAR transformation rules, decomposing series into R-groups, performing scaffold-balanced QSAR splits, or planning analog campaigns.
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Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, Annoy MHFP6, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.
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 fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
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。
Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).
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Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda window scheduling, soft-core potentials, REST2 enhanced sampling, MBAR/BAR analysis, and cycle closure validation. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.
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Experimental data: discover and acquire raw or near-raw measurements, sample-level observations, and reusable datasets from sequencing, transcriptomics, single-cell, perturbation, proteomics, metabolomics, genomics, and structural-biology repositories. Use for study or sample metadata, matrices, reads, spectra, experimental structures, and downloadable research artifacts—not curated database assertions or reference marker collections.
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。
Implement privileged commands, admin UI, role checks, audit logs, and safe operational controls.
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When the user wants to plan a content strategy, decide what content to create, or figure out what topics to cover. Also use when the user mentions "content strategy," "what should I write about," "content ideas," "blog strategy," "topic clusters," or "content planning." For writing individual pieces, see copywriting. For SEO-specific audits, see seo-audit.
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。
Guide technology stack for accounting firms. TRIGGERS - Use when user needs help with accounting-tech-stack related tasks.
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Evaluate third-party logistics providers. TRIGGERS - Use when user needs help with 3pl-evaluation related tasks.
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。
Generate a personalized email banner image with text, logo, and brand colors.
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。
Generate an XLSX timesheet with logged hours, hourly rates, per-entry amount formulas, and totals for client billing or payroll.
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。
Generate a branded PDF report with custom client logo placeholder, colors, and content sections for white-label agency delivery.
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。
Generate a branded Open Graph image with a generative wave background, logo, and tagline.
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Extract HS codes, declared values, and duty amounts from customs declaration documents, then generate an XLSX import duty log.
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。
Runs standard-format scenarios through ScenarioRunner — ASAM OpenSCENARIO 1.x (.xosc, via --openscenario, with catalogs and ParameterDeclaration overrides) and OpenSCENARIO 2.0 (.osc, via --openscenario2, the ANTLR/osc2 pipeline). Validates a file against the schema before running, lists the bundled examples, and reports which OSC features ScenarioRunner actually implements. Use when the user asks to "run an xosc", "run an OpenSCENARIO file", "use the OpenSCENARIO standard", "run a .osc / OSC2 scenario", or hands over a scenario file from another tool.
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Records a running CARLA simulation to a .log file via start_recorder/stop_recorder for later deterministic replay — capturing actor spawns, transforms, traffic-light states and animations (optionally velocities/controls). Use when the user asks to "record the simulation", "start/stop the recorder", "capture this run", or "save a scenario to replay later". Does NOT capture sensor images.
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。
Replays a CARLA .log recorded by the recorder — the whole run or a time window, at any speed, following a chosen actor, optionally regenerating sensors and restoring weather. Use when the user asks to "replay the recording", "play back the run", "watch it again in slow motion / at 2x", "follow vehicle N during replay", or "replay onto a different map".
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Expert fintech engineer specializing in financial systems, regulatory compliance, and secure transaction processing. Masters banking integrations, payment systems, and building scalable financial technology that meets stringent regulatory requirements.
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。