3. Data, Science & AI

Provides tools for building and training Graph Neural Networks (GNNs) for node classification, link prediction, and molecular property prediction using PyTorch Geometric.

Provides access to 600+ scientific tools, datasets, and APIs for research in bioinformatics, genomics, and drug discovery workflows.

Applies medicinal chemistry rules including Lipinski, Veber, and PAINS filters to prioritize compounds and filter chemical libraries.

Comprehensive toolkit for astronomical data analysis, including FITS I/O, coordinate transformations, cosmology calculations, and WCS support.

Python library for reading, writing, and processing DICOM medical imaging files, including metadata and pixel data extraction.

Manipulates AnnData objects for single-cell genomics, handling data loading, metadata management, and dataset concatenation in scRNA-seq workflows.

Integrates with Electronic Lab Notebook (ELN) APIs to manage entries, attachments, and backups, enabling programmatic workflows with Protocols.io, Jupyter, and REDCap.

Python API for managing microscopy data, enabling image access, pixel analysis, ROI/annotation handling, and batch processing in high-content screening.

Performs comprehensive exploratory data analysis on CSV/Excel/JSON/Parquet files, generating statistical summaries, visualizations, and markdown reports for data profiling and insights.

Provides molecular machine learning tools for drug discovery, including property prediction, GNNs, and MoleculeNet benchmarks.

Queries COSMIC database for cancer mutations, gene fusions, and mutational signatures to support oncology research and precision medicine.

Provides Python-based tools for processing mass spectrometry data, including peak picking, feature detection, and peptide identification in proteomics and metabolomics workflows.

Fast DataFrame library for high-performance data analysis, supporting select, filter, group_by, lazy evaluation, and CSV/Parquet I/O.

Digital pathology toolkit for processing whole slide images (WSI), including tile extraction, tissue segmentation, and dataset preparation for deep learning in computational pathology.

Provides molecular featurization for machine learning, converting SMILES to features using ECFP, MACCS, and ChemBERTa for QSAR and molecular ML applications.

Provides access to RCSB Protein Data Bank for searching, downloading, and retrieving metadata of 3D protein and nucleic acid structures for structural biology research.

Comprehensive Python library for machine learning tasks including classification, regression, clustering, and model evaluation workflows.

Accesses AlphaFold's database of AI-predicted protein structures, enabling retrieval by UniProt ID and analysis of confidence metrics for drug discovery.

Provides programmatic access to Ensembl's REST API for genomic data queries, including gene lookups, sequence retrieval, and variant analysis across 250+ species.

Provides a unified Python API for accessing and analyzing biological data across 40+ bioinformatics databases including UniProt, KEGG, and ChEMBL.

Comprehensive toolkit for time series machine learning tasks including classification, forecasting, anomaly detection, and deep learning with specialized transformations.

Provides a multi-objective optimization framework with algorithms like NSGA-II and MOEA/D for engineering design and scientific optimization problems.

Enables semantic and text search across previous Claude Code conversations to retrieve facts, patterns, decisions, and context for efficient information retrieval.

Autonomous AI framework for biomedical research, enabling genomics, drug discovery, and clinical analysis via LLM reasoning and integrated biomedical databases.