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| title: Meta-LoRA Molecular Generator | |
| emoji: ⚗️ | |
| colorFrom: yellow | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 5.38.0 | |
| app_file: app.py | |
| python_version: "3.11" | |
| pinned: false | |
| license: mit | |
| short_description: Few-shot molecular generation via context-conditioned LoRA | |
| # Meta-LoRA Molecular Generator | |
| Scaffold-Episodic Meta-Learning with Context-Conditioned LoRA for Few-Shot Molecular Generation. | |
| Given a small support set of SMILES strings from the same scaffold family, the model generates | |
| novel, valid, drug-like molecules — **zero gradient steps at inference**. | |
| ## Architecture | |
| - **BaseGrammarTransformer** — 3.2M frozen params, pre-trained on ZINC250k | |
| - **EnhancedContextEncoder** — GRU + Morgan FP fusion → constraint vector z | |
| - **ContextConditionedLoRA** — rank-16 LoRA on Q and V projections, conditioned on z | |
| - **Training** — Scaffold-episodic meta-training (Bemis-Murcko families) | |
| ## Metrics (10-trial mean ± std, ZINC250k, 5-shot) | |
| | Metric | Value | | |
| |---|---| | |
| | Validity | 96.8 ± 2.1% | | |
| | Uniqueness | 99.1 ± 0.8% | | |
| | Novelty | 98.3 ± 1.4% | | |
| | Avg Tanimoto | 0.4231 ± 0.038 | | |
| ## Usage | |
| Paste 3–10 SMILES from the same scaffold family into the support set box and click Generate. | |