Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).
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 hallucinate molecular properties. This skill grounds predictions by retrieving structurally similar compounds with experimentally measured properties from ChEMBL and ZINC. When the agent says "this compound should have good hERG safety," it can now check what happened with similar compounds in real assays.
Based on:
Do NOT use this skill for:
chembl-database or pubchem-database directly)denovo-design)pip install rdkit-pypi requests pandas
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--similarity-threshold 0.6 \
--max-results 20 \
--output analogs.json
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--target CHEMBL25 \
--output target_analogs.json
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--include-activities \
--output sar_context.json
| Script | Purpose | Key Outputs |
|---|---|---|
retrieve_analogs.py | Find similar compounds with experimental data | JSON with analogs, similarities, bioactivities |
まだレビューはありません。使ってみた感想をお寄せください。
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。