3. Data, Science & AI

Provides a hardware-agnostic framework for training quantum machine learning models, supporting variational algorithms and integration with PyTorch, JAX, and TensorFlow.

Direct REST API access to UniProt for protein data retrieval, including searches, FASTA sequences, and ID mapping.

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

Low-level Python library for generating customizable, publication-ready static data visualizations with full control over plot elements.

Enables constraint-based metabolic modeling with FBA, FVA, gene knockouts, and SBML model analysis for systems biology research.

RDKit provides molecular structure processing, descriptor calculation, and substructure search for chemical data analysis.

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

Framework for designing, simulating, and executing quantum circuits on Google Quantum AI hardware with noise modeling capabilities.

DeepChem provides molecular machine learning tools with diverse featurizers, pre-built datasets, and benchmarks for property prediction tasks like ADMET and toxicity using traditional ML or GNNs.

Queries STRING API for protein-protein interaction data, enabling network analysis and GO/KEGG enrichment across 5000+ species in systems biology research.

Framework for Python-based computational fluid dynamics simulations, supporting Navier-Stokes, turbulence analysis, and HPC with FFT methods.

Autonomous AI framework for biomedical research tasks including genomics, drug discovery, and clinical analysis using LLM reasoning and integrated databases.

Comprehensive toolkit for protein language models (ESM3, ESM C) enabling sequence, structure, and function prediction, protein design, and engineering via local/cloud APIs.

Enables symbolic mathematics in Python for algebraic manipulation, calculus operations, equation solving, and scientific computations.

Enables deployment and fine-tuning of pre-trained transformer models for NLP, computer vision, and multimodal AI tasks like text generation and image classification.

Evaluates scientific claims by assessing evidence quality, experimental design validity, and biases using frameworks like GRADE and Cochrane Risk of Bias.

Framework for developing and executing quantum circuits on IBM Quantum hardware with error mitigation and enterprise production capabilities.

Enables model interpretability and explainability for ML models using SHAP, including feature importance and various plot types.

Biosignal processing toolkit for ECG, EEG, and EDA analysis in psychophysiology research.

Provides advanced LLM-as-Judge evaluation techniques including direct scoring, pairwise comparison, and bias mitigation for AI model assessment.

Autonomous web scraping and content extraction tool for deep web data collection, enabling research and analysis workflows.

Automates scientific research workflows including data analysis, methodology development, literature search, and LaTeX paper generation via multiagent AI orchestration.

Integrates with Electronic Lab Notebooks (ELN) via API for programmatic management of notebooks, entries, and attachments, supporting scientific workflows with Protocols.io, Jupyter, and REDCap.

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