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--- |
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tags: |
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- model_hub_mixin |
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- pytorch_model_hub_mixin |
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license: cc-by-nc-3.0 |
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datasets: |
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- KU-AGI/Mol-LLM |
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language: |
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- en |
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metrics: |
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- bleu |
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- meteor |
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- rouge |
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- roc_auc |
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- mae |
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base_model: |
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- mistralai/Mistral-7B-Instruct-v0.3 |
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--- |
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# Mol-LLM: Multimodal Generalist Molecular LLM |
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Mol-LLM is a multimodal generalist molecular large language model for chemistry that jointly uses molecular 1D sequences and 2D molecular graphs to solve a wide range of molecular tasks in a single unified framework. |
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It introduces **Molecular structure Preference Optimization (MolPO)** to force the LLM to prefer correct molecular graphs over perturbed ones, resolving the **“graph-bypass”** issue common in prior multimodal molecular LLMs. |
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## Model summary |
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- **Backbone**: Mistral-7B-Instruct-v0.3. |
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- **Modalities**: |
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- Text (natural language instructions). |
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- 1D molecular sequences (SELFIES; SMILES supported via translation). |
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- 2D molecular graphs encoded by a hybrid GNN (GINE + TokenGT). |
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- **Architecture**: Mol-LLM uses a BLIP-2–style architecture where Q-Former (32 query tokens) projects graph embeddings into the LLM token space. |
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- **LLM**: |
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- Mistral-7B-Instruct-v0.3 as text backbone. |
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- Extended tokenizer with SELFIES and numeric tokens, plus task tags for heterogeneous outputs (discrete labels, floats, descriptions). |
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- **Hybrid graph encoder**: |
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- GINE for local structural patterns. |
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- TokenGT (transformer-based) for global structural dependencies and large graphs. |
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- Both encoders produce graph and node (and edge) embeddings; concatenated embeddings are fed into the Q-Former. |
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- **Q-Former**: |
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- 5-layer SciBERT-style transformer with 32 learnable queries. |
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- Cross-attends to graph embeddings and outputs fixed-length tokens appended after SELFIES tokens in the LLM input. |
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- Selected over an MLP projector due to better alignment and graph-token efficiency. |
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- **Tokenizer extensions**: 3K SELFIES tokens, numeric tokens, and task tags for `[SELFIES]`, `[BOOLEAN]`, `[FLOAT]`, `[DESCRIPTION]`, and reaction-direction symbols. |
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- **Training data**: ~3.3M instruction-tuning examples over 27 tasks, with ~40K held-out test instances. |
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Mol-LLM is positioned as a state-of-the-art or comparable **generalist** molecular LLM on the most comprehensive benchmark suite evaluated so far, including out-of-distribution (OOD) settings. |
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## Intended use |
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Mol-LLM is intended to solve **molecular tasks** via a single multitask model. |
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Supported task families: |
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- **Reaction prediction**: |
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- Forward synthesis (product prediction, FS) |
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- Retrosynthesis (reactant prediction, RS) |
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- Reagent prediction (RP) |
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- **Property prediction**: |
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- Regression: LogS, LogD, HOMO, LUMO, HOMO–LUMO gap |
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- Classification: BACE, BBBP, ClinTox, HIV, SIDER |
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- **Text–molecule tasks**: |
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- Description-guided molecule generation |
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- Molecule captioning |
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- IUPAC/SELFIES/formula translation as auxiliary tasks |
