196건 · 관련도순
Core cheminformatics toolkit for SMILES/SDF/InChI parsing, descriptors (MW, LogP, TPSA), fingerprints, ECFP/Morgan fingerprints, substructure search, 2D/3D generation, similarity, reactions, and datamol-style molecule standardization when no separate wrapper skill is routed.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external NVIDIA-hosted API endpoints on every call. Use local NIM containers for confidential or proprietary data. Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. Use this skill whenever the user wants to generate and screen small molecule drug candidates, perform hit discovery, optimize leads against a protein target, or do virtual screening combining molecule generation, docking, and affinity prediction. Triggers on: drug discovery pipeline, hit discovery, lead optimization, virtual screening, molecule generation, molecular docking, binding affinity, GenMol, DiffDock, Boltz2, SMILES, SAFE notation, NIM microservice. This is a multi-step pipeline composing three BioNeMo NIMs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized embedding extraction and write model bundles, per-readout .npy embeddings, canonical SMILES, and validity arrays to user-selected host directories.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Invoke for MolMIM, molecular embeddings, hidden states, latent decoding, sampling around a seed SMILES, CMA-ES guided molecule generation, QED or plogP optimization, hosted NVIDIA API calls, or local Docker deployment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run DiffDock molecular docking via NVIDIA NIM to predict small-molecule binding poses against protein targets. Use for DiffDock, molecular docking, ligand docking, blind docking, SMILES or SDF ligands, ranked poses, confidence scores, hosted NVIDIA API, or local Docker deployment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Continue KERMT pretraining on a custom SMILES corpus with a grover_base, cmim, or hybrid checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized training and write model bundles, prepared data, logs, and checkpoints to user-selected host directories.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. The skill validates that the input ckpt has task FFN heads (refuses pretrain ckpts with a redirect to kermt-finetune), validates the CSV, prepares the data (clean + rdkit_2d features), then launches main.py predict inside the kermt container (blocking, minutes-scale).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Invoke for Boltz2, protein structures, protein-ligand/DNA/RNA complexes, SMILES or CCD ligands, pIC50/IC50 affinity scoring, mmCIF output, hosted NVIDIA API calls, or local Docker deployment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Query PubChem, search by name/CID/SMILES, retrieve properties, similarity/substructure searches, bioactivity, for cheminformatics. Use when a user asks about a specific chemical, drug, or molecule.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reads, writes, and converts molecular file formats (SMILES, SDF, MOL2, PDB) using RDKit and Open Babel. Handles structure parsing, canonicalization, and full standardization pipeline including sanitization, normalization, and tautomer canonicalization. Use when loading chemical libraries, converting formats, or preparing molecules for analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
保守解析化学名称、SMILES、InChI、InChIKey、PubChem CID、ChEMBL ID 或 CAS RN，保留多源证据、相关形式、歧义和冲突。用于确认化学记录是谁、对齐开放数据库，或在结构标准化前选择候选。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
离线批量解析、标准化、去盐、提取 parent、检查异常并按原始/标准化/parent 结构分组。用于清洗 SMILES、CSV、SDF、MolBlock，检查价态/立体化学/混合物，或为描述符、相似性检索和建模准备结构数据。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
整理和审查结构化化学反应，保留原始记录并检查参与物、角色、产率、重复、守恒和 ORD 合同。用于清洗 reaction SMILES、ORD 或反应表格。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Query PubChem via PUG-REST API/PubChemPy (110M+ compounds). Search by name/CID/SMILES, retrieve properties, similarity/substructure searches, bioactivity, for cheminformatics.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, and BinaryCIF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
分子格式转换工具。支持在 .smi、.mol、.xyz、.gjf、.sdf 等格式之间转换，并可从 SMILES 或 2D 结构生成带坐标的 3D 结构。 当用户提到分子格式转换、3D构象生成、openbabel等需求时使用此skill。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create or update the project constitution from interactive or provided principle inputs, ensuring all dependent templates stay in sync.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Perform a dual-lens critical review of the specification and plan from both product strategy and engineering risk perspectives before implementation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.