Summary of Pin-tuning: Parameter-efficient In-context Tuning For Few-shot Molecular Property Prediction, by Liang Wang et al.
Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
by Liang Wang, Qiang Liu, Shaozhen Liu, Xin Sun, Shu Wu, Liang Wang
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Molecular Networks (q-bio.MN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Molecular property prediction is crucial in drug discovery and material science, but often faces data scarcity challenges. To address this, few-shot molecular property prediction (FSMPP) has been developed using pre-trained molecular encoders and context-aware classifiers. However, existing methods struggle with fine-tuning pre-trained encoders due to the imbalance between tunable parameters and labeled molecules, as well as a lack of contextual perceptiveness. Our proposed method, Pin-Tuning, uses parameter-efficient in-context tuning to overcome this hurdle. We introduced lightweight adapters for message passing layers (MP-Adapter) and Bayesian weight consolidation for atom/bond embedding layers (Emb-BWC), which enable efficient tuning without over-fitting or catastrophic forgetting. Additionally, we enhanced MP-Adapters with contextual perceptiveness. Our method demonstrates superior tuning performance using fewer trainable parameters, improving few-shot predictive performance on public datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Molecular property prediction is important for finding new medicines and creating materials. But sometimes it’s hard to train models because there isn’t enough data. To fix this, scientists developed a way called few-shot molecular property prediction (FSMPP). It uses special computers that understand molecules and other things that help them make good predictions. The problem is that these computers need to be trained again for each new task, but it’s hard to do this because there isn’t enough data. Our team found a way to make the training process better by using something called Pin-Tuning. It helps the computer understand what it needs to know and only uses a little bit of information to get good results. |
Keywords
* Artificial intelligence * Embedding * Few shot * Fine tuning * Parameter efficient
