Deep learning is gaining traction across many elds as a powerful tool. In medicine, there have been recent successes in drug design, predicting protein structure, and in functional genomics. These successes have thus far been in areas where there are hundreds of thousands of data points and deep learning in medicine is still limited by lack of large homongeous datasets. This proposal focuses on applying a new kind of deep learning called meta-learning that mimics the human-like ability to learn from few examples. The PI will establish a sustainable research program on meta-learning by developing benchmark problems and datasets. The PI will further explore meta-learning speci cally on peptide-protein structure and NMR spectra prediction. Due to the imperative need for interpretability when using deep learning in medicine, a strong component will be connecting biophysical modeling with the deep learning models. The outcome of this work will be a demonstrated new approach to deep learning that can work with little data. The PI will bring these research ideas together to design peptides that can bind to intrinsically disordred proteins, a challenging but important task for curing neurodegenerative diseases. This will be accomplished through meta-learning, molecular simulation, and iterative peptide design.
Deep learning is a technique from arti cial intelligence that has driven many high-pro le break- throughs in recognizing objects in images, translating human languages, and playing games like Chess and Go. Its use is medicine is currently limited by deep learning's need for large amounts of data and its lack of interpretability. This researh plan works towards solving these challenges and applies interpretable deep learning to designing new therapeutics.
