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MIPT: Multilevel Informed Prompt Tuning for Robust Molecular Property Prediction
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Proposes MIPT, a lightweight prompt-tuning framework for pretrained GIN molecular encoders that combines LoRA node-encoder tuning, multi-level graph-informed prompts, and a GMM-based noise penalty loss, evaluated on eight MoleculeNet classification datasets.
Problem
Adapting pretrained graph neural networks to molecular property prediction tasks is hard: universal or manual graph prompts are inflexible or costly, and pretrained features plus prompts can inject task-irrelevant noise, hurting accuracy under limited labels.
Approach
Combines low-rank adaptation of the node encoder with a multi-level graph-informed prompt (random node masking, contrastive global-local alignment, low-rank prompt generation) and a noise penalty that down-weights high-loss samples via a two-component GMM over classification losses.
Key results
- On BBBP with ContextPred pretraining, MIPT reaches 73.62 ROC-AUC vs 69.65 for fine-tuning (about 8.98% relative improvement).
- On ClinTox with AttrMasking, MIPT reaches 85.18 ROC-AUC, surpassing GPF-plus by 11.83%.
- Compared with SOTA models in Table 2, MIPT outperforms them on 5 of 8 tasks while using fewer parameters.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MIPT instead of the strongest listed baseline | Molecular property prediction models · BBBP | ROC-AUC (%): 73.3(0.8) -> 73.6(0.1) | Helped |
| Use MIPT instead of the strongest listed baseline | Molecular property prediction models · BACE | ROC-AUC (%): 85.9(1.3) -> 86.6(0.5) | Helped |
| Use MIPT instead of the strongest listed baseline | Molecular property prediction models · ClinTox | ROC-AUC (%): 92.5(2.1) -> 85.2(0.1) | Hurt |
| Use MIPT instead of the strongest listed baseline | Molecular property prediction models · Tox21 | ROC-AUC (%): 79.9(0.6) -> 80.6 (0.3) | Helped |
| Use MIPT instead of the strongest listed baseline | Molecular property prediction models · ToxCast | ROC-AUC (%): 70.8(0.4) -> 68.8(0.6) | Hurt |
| Use MIPT instead of the strongest listed baseline | Molecular property prediction models · SIDER | ROC-AUC (%): 67.4(0.9) -> 68.5 (0.3) | Helped |
| Use MIPT instead of the strongest listed baseline | Molecular property prediction models · MUV | ROC-AUC (%): 79.9(0.7) -> 85.0 (1.2) | Helped |
| Use MIPT instead of the strongest listed baseline | Molecular property prediction models · HIV | ROC-AUC (%): 82.1(1.3) -> 81.8 (0.1) | Hurt |
| Add MGIP to LoRA and Lcont (ablation) | MIPT · SIDER | ROC-AUC: 60.75 -> 67.76 | Helped |
| Add LNPP to LoRA, MGIP, and Lcont (ablation) | MIPT · SIDER | ROC-AUC: 67.76 -> 68.52 | Helped |
| Add LoRA to MGIP, LNPP, and Lcont (ablation) | MIPT · SIDER | ROC-AUC: 67.23 -> 68.52 | Helped |
| Add Lcont to LoRA, MGIP, and LNPP (ablation) | MIPT · SIDER | ROC-AUC: 66.17 -> 68.52 | Helped |
Surprising findings
- Universal learnable prompts (GPF, GPF-plus) yield limited and inconsistent gains over full fine-tuning across datasets.
Limitations
- Gains over the best baselines are sometimes marginal or absent (e.g., BACE and MUV rankings vary across pretraining strategies).
- Evaluation limited to eight MoleculeNet classification datasets with GIN backbone; the claimed model-agnosticism is not tested.
- Some reported improvements rely on relative percentages and three seeds, and the test-curve fluctuations are interpreted as exploration without deeper analysis.
Experimental setup
Datasets: BBBP Tox21 ToxCast SIDER ClinTox MUV HIV BACE
Benchmarks: BBBP Tox21 ToxCast SIDER ClinTox MUV HIV BACE
Metrics: ROC-AUC
Baselines: Fine-tuning GPF GPF-plus D-MPNN Attentive FP GROVER Uni-Mol GraphMVP MolCLR InstructMol
Builds on: GPF GPF-plus GIN Graph Prompt Deep Graph Infomax
Closest papers by meaning
- Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction (NeurIPS 2024 · similarity 0.89)
- MORE: Molecule Pretraining with Multi-Level Pretext Task (AAAI 2025 · similarity 0.87)
- Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks (ICLR 2024 · similarity 0.86)
- Molecule Design by Latent Prompt Transformer (NeurIPS 2024 · similarity 0.86)
- Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages (ICML 2025 · similarity 0.86)
- Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning (NeurIPS 2024 · similarity 0.86)
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