Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
LLMs frequently generate invalid SMILES or produce molecules that don't match their stated reasoning. This skill provides strict validation, structural comparison, and modification verification.
Key checks:
pip install rdkit-pypi
python scripts/validate.py --smiles "c1ccccc1"
python scripts/validate.py --original "c1ccccc1" --proposed "c1ccc(O)cc1" --check-modification "Added hydroxyl group"
python scripts/validate.py --input generated_molecules.csv --output validation_report.json
| Script | Purpose | Key Outputs |
|---|---|---|
validate.py | SMILES validation, comparison, and modification checking | JSON report with validity, similarity, scaffold match, modification verification |
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概要と使いどころ
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
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。
ADMET property prediction for drug candidates. Full pharmacokinetic panel (Caco-2, PPB, clearance, CYP), toxicity (hERG, AMES, DILI), drug-likeness (Lipinski, QED), using RDKit descriptors and TDC models.
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).
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 for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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。
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
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。
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
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。