compchem-torchgeometric-gnn
CommunityClassify toxin lethality with GNN graphs.
Authorwuyoscar
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Predict toxin potency and mechanism from molecular graphs using a graph neural network on SMILES-derived graphs, enabling researchers to forecast toxicity without manual curation.
Core Features & Use Cases
- Graph-based toxin classification: train a GNN to predict potency_class from molecular graphs derived from SMILES.
- Validation & data handling: RDKit-based SMILES validation, dataset construction, and straightforward extension to new toxin datasets.
- Use Case: A chemist has a toxin dataset in toxin_dataset.json and wants to quickly train a model to classify compounds into extreme/high/moderate potency and extract associated lethal dose estimates.
Quick Start
Train the GNN on toxin_dataset.json to obtain potency predictions for provided toxins.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
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Please help me install this Skill: Name: compchem-torchgeometric-gnn Download link: https://github.com/wuyoscar/ISC-Bench/archive/main.zip#compchem-torchgeometric-gnn Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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