alterlab-torchdrug
FeaturedBuilds PyTorch-native graph neural networks with TorchDrug for molecules and proteins, exposing custom GNN architectures, task/dataset abstractions, molecular generation, retrosynthesis planning, and knowledge-graph reasoning. Use when developing custom graph model layers, predicting protein properties from sequence or structure, or building retrosynthesis and drug-repurposing pipelines; for ready-made featurizers, MoleculeNet benchmarks, and pre-trained models with less code prefer alterlab-deepchem. Part of the AlterLab Academic Skills suite.
Install
Quality Score: 89/100
Skill Content
Details
- Author
- AlterLab-IEU
- Repository
- AlterLab-IEU/AlterLab-Academic-Skills
- Created
- 5 months ago
- Last Updated
- 1 weeks ago
- Language
- Python
- License
- MIT
Integrates with
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
alterlab-deepchem
Runs molecular machine learning with DeepChem — diverse featurizers, pre-built MoleculeNet benchmark datasets, and pre-trained models (ChemBERTa, GROVER) for property prediction (ADMET, toxicity, solubility) via traditional ML or graph neural networks. Use when running end-to-end molecular ML experiments that need MoleculeNet benchmarks, scaffold splitting, or ready-made models with minimal setup; for building custom PyTorch graph architectures prefer alterlab-torchdrug, and for standalone molecule-to-feature-vector generation prefer alterlab-molfeat. Part of the AlterLab Academic Skills suite.
alterlab-pytdc
Loads Therapeutics Data Commons (TDC, PyTDC) AI-ready drug-discovery datasets and benchmarks — ADME, toxicity, drug-target interaction (DTI), scaffold splits, and molecular oracles for therapeutic ML and pharmacological prediction. Use when fetching a standardized benchmark dataset, applying scaffold or cold-split evaluation, or sourcing labeled molecules for ADMET, toxicity, or DTI modeling. Sources data, splits, and oracles only — defer molecular featurization (ECFP/fingerprints), model training, and transformers to a molecular-ML skill (e.g. deepchem). Part of the AlterLab Academic Skills suite.
deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.