Tools & Software
Software ecosystems for AI in science — organized by domain.
Overview
The AI4Science ecosystem includes tools for:
- Data Processing — Reading, manipulating, and featurizing scientific data
- Machine Learning — Training and deploying models
- Visualization — Exploring and presenting results
- Workflows — Reproducible research pipelines
Tools by Domain
| Domain | Key Tools |
|---|---|
| Chemistry | RDKit, DeepChem, Chemprop |
| Biology | DeepChem, DGL-LifeSci |
| Materials | pymatgen, matminer, ASE |
Cross-Domain Tools
Deep Learning Frameworks
| Tool | Description | Link |
|---|---|---|
| PyTorch | Primary DL framework for science | pytorch.org |
| PyTorch Geometric | Graph neural networks | pyg.org |
| DeepChem | ML for chemistry and life sciences | deepchem.io |
Data Science
| Tool | Description | Link |
|---|---|---|
| Jupyter | Interactive notebooks | jupyter.org |
| pandas | Data manipulation | pandas.pydata.org |
| scikit-learn | Classical ML | scikit-learn.org |
Environment Setup
Recommended: Conda
# Install miniconda
# https://docs.conda.io/en/latest/miniconda.html
# Create environment
conda create -n ai4science python=3.10
conda activate ai4science
# Install core tools
pip install jupyter pandas numpy scikit-learn matplotlib
pip install torchGoogle Colab
For quick experiments without local setup, use Google Colab . Most tutorials include “Open in Colab” badges.