Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators
This work addresses the challenge of efficient simulation under various conditions for researchers in computational biology, though it is incremental as it builds on existing meta-learning and data augmentation techniques.
The paper tackles the problem of sample-efficient fine-tuning for protein simulators by adapting soft prompt-based learning to molecular dynamics tasks, achieving strong generalization to unseen and out-of-distribution scenarios with limited data.
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.