Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators

• URL: http://arxiv.org/abs/2308.15116v3
• Date: Tue, 10 Oct 2023 03:41:05 GMT
• Title: Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators
• Authors: Jingbang Chen, Yian Wang, Xingwei Qu, Shuangjia Zheng, Yaodong Yang, Hao Dong, Jie Fu
• Abstract summary: We adapt the soft prompt-based learning method to molecular dynamics tasks. Our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples.
• Score: 29.22292758901411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can fluctuate. In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and out-of-distribution scenarios with limited training data. While our work focuses on temperature as a test case, the versatility of our approach allows for efficient simulation through any continuous dynamic conditions, such as pressure and volumes. Our framework has two stages: 1) Pre-trains with data mixing technique, augments molecular structure data and temperature prompts, then applies a curriculum learning method by increasing the ratio of them smoothly. 2) Meta-learning-based fine-tuning framework improves sample-efficiency of fine-tuning process and gives the soft prompt-tuning better initialization points. Comprehensive experiments reveal that our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples.

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