Skill UI

Molfeat is a Python toolkit that converts SMILES/RDKit molecules into embeddings via 100+ calculators, transformers, and pretrained models for QSAR, screening, similarity search, and deep learning pipelines.

Query UCSF’s ZINC22 repository of 230M+ purchasable compounds via the web UI or CartBlanche APIs to fetch IDs/SMILES, run similarity, supplier, or random sampling, and assemble docking-ready libraries for virtual screening.

Molfeat is a comprehensive Python library designed for molecular featurization. It converts chemical structures (SMILES/RDKit) into numerical feature vectors, supporting over 100 featurizers like ECFP, MACCS, and deep embeddings (ChemBERTa). It unifies featurization pipelines, making it ideal for QSAR modeling, virtual screening, and integrating into deep learning ML workflows.

This skill provides a comprehensive Python API integration for the OMERO platform, an open-source solution for managing and analyzing large-scale microscopy datasets. Users can programmatically access images, retrieve structured data, analyze raw pixel data, manage complex annotations (ROIs), and execute batch processing workflows essential for high-content screening and advanced biological research.

This skill provides deep, cross-border financial analysis and valuation metrics for stocks across multiple markets (e.g., US, HK). Users can access detailed income statements, balance sheets, cash flow data, analyze business segments, compare valuation multiples (PE, PB), run sophisticated DCF models, and perform value screening. It is an essential tool for fundamental research, investment modeling, and corporate analysis.