「gan」の検索結果

Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.

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Calculates molecular fingerprints (ECFP/Morgan, FCFP, MACCS, RDKit, AtomPair, TopologicalTorsion, Avalon, MAP4, MHFP6) and physicochemical descriptors (Lipinski, QED, TPSA, Crippen LogP, 3D shape) with explicit choice tables, bit vs count semantics, and partial-charge model selection. Use when featurizing molecules for similarity, QSAR, virtual screening, or ML, or selecting the correct fingerprint for a chemotype-aware task.

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Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.

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Validates docked / generated protein-ligand poses using PoseBusters physical-validity tests, strain energy quantification, geometric checks (planarity, vdW overlap, bond/angle distortion), and pose-energy reasonableness. Filters AI-docking outputs (DiffDock, EquiBind, NeuralPLexer) where ~50% of poses fail physical-validity tests. Use when QC-ing docking results, comparing classical vs ML docking outputs, or filtering pose lists before SAR analysis.

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Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.

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Normalizes ChIP-seq data using exogenous spike-in (ChIP-Rx with Drosophila chromatin per Orlando 2014 / Egan 2016; E. coli carryover for CUT&RUN/CUT&Tag). Distinguishes RRPM from Rx-Input scaling, integrates with DiffBind / DESeq2 / edgeR / csaw via sizeFactors and DiffBind library-size vectors, and applies the Patel et al 2024 *Nat Biotechnol* review's failure-mode framework to validate that normalization is correctly applied at the read level (not peak counts). Use when global signal shifts are expected (HDACi, BETi, EZH2i, dosage, target knockdown), when ChIPseqSpikeInFree detects post-hoc shifts, or when validating internal-control regions before publication.

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Map nucleosome center positions, occupancy, and fuzziness from ATAC-seq fragment-size patterns using NucleoATAC, ATACseqQC, DANPOS3, or scprinter. Use when characterizing nucleosome organization at promoters and enhancers, calling +1/-1 nucleosomes flanking NFRs, generating V-plots for chromatin structure visualization, or comparing nucleosome positioning between conditions.

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Audita calidad de código Kotlin/Spring Boot en este proyecto. Úsalo cuando el usuario pida revisar código, verificar estándares, hacer un code review, auditar calidad, ejecutar linters, verificar cobertura, o antes de hacer commit/PR. También úsalo cuando mencionen KTLint, Detekt, JaCoCo, cobertura, o simplemente digan "revisa el código" / "está listo para commit" / "qué tan bueno está el código".

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Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx) or Word templates (.dotx). Triggers include: any mention of Microsoft Word Documents, such as 'Word doc', 'word document', '.docx', '.dotx', 'microsoft doc'. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a deliverable as a Word or .docx file (to download, email or print), use this skill. However, if they ask for a document, page, report, memo, or notes WITHOUT naming a file format and the session offers Claude's own dedicated document or page skill or connector, use that instead, even if they will email or print it. Do NOT use for PDFs, spreadsheets, Google Docs, or coding unrelated to document generation.

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Automate Basecamp project management, to-dos, messages, people, and to-do list organization via Rube MCP (Composio). Always search tools first for current schemas.

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Azure API Center Management SDK for Python. Use for managing API inventory, metadata, and governance across your organization.

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Manage MongoDB Atlas Organizations as Azure ARM resources with unified billing through Azure Marketplace.

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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.

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SwiftUI UI component catalog. Use when implementing NavigationStack, Swift Charts, PhotosPicker, TipKit, ScrollView enhancements, animations, layouts, WebView, Liquid Glass and other UI components. Organized by feature category with OS version compatibility info. Reference for discovering available components and implementation patterns.

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Extract dominant-component soil profile variables for EcoSIM grid or template inputs from gSSURGO, with FAO HWSD v2.0 fallback when gSSURGO data are unavailable or incomplete. Use when deriving soil depth, bulk density, field capacity, wilting point, hydraulic conductivity, texture, rock fraction, pH, CEC, or soil organic carbon; or when converting EcoSIM grid NetCDF files to editable Excel workbooks and back.

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Sort AmeriFlux BASE-BADM BIF variables from Excel or CSV files into observation, management-event, static metadata, and plotting-ready target time-series tables. Use when Codex is asked to sort AmeriFlux BIF variables into time series; organize BADM/BIF variables by date; pivot AMF *_BIF_*.xlsx files by GROUP_ID; output total LAI, leaf mass per area, above-ground biomass, canopy height, fruit yield, or total yield time series; write MATLAB readers for BIF time-series plotting; or prepare dated AmeriFlux site observations and management records for EcoSIM workflows.

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。

Frosted glass effect with translucent layers, subtle blur, and luminous borders for depth and modern elegance.

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Graceful, refined aesthetic with delicate typography, minimal palettes, and polished layouts that exude sophistication.

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Magazine-inspired editorial layout with refined serif typography, structured grids, and elegant reading experiences.

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Carefully curated, modern minimal style with elegant serif typography and understated, sophisticated palettes.

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。

Modular grid layout with card-like blocks, clear hierarchy, soft spacing, and subtle visual contrast for organized, scannable interfaces.

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Organiza, compara e seleciona entre as variações geradas, e publica a grade de comparação como Artifact para aprovação do time. Use quando o usuário disser "qual dessas", "comparar as variações", "escolher a melhor", "mostrar as opções", "montar grade de aprovação", ou após qualquer lote de geração. Selecionar é trabalho, não detalhe.

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Leitura, escrita e edição estruturada de notas, busca, tags, arquivo ativo e comandos da paleta no Obsidian via MCP (plugin Local REST API). Use quando o usuário disser "Obsidian", "vault", "nota", "anotação", "criar nota", "buscar nota", "tag", "daily note", "frontmatter", "abrir arquivo no Obsidian", ou pedir para ler/editar/organizar conteúdo do vault.

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Transforma análise de desempenho em plano de ação priorizado por tiers — ganhos rápidos, mudanças estruturais, experimentos e o que parar de fazer. Use quando o usuário disser "o que eu faço agora", "como melhorar", "plano de ação", "por onde começo", "otimizar campanha", ou logo após perf-analyzer e perf-patterns. Fecha o ciclo: escrever → medir → aprender → escrever melhor.

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。