# Learning to learn in structural biology with deep neural networks

> **NIH NIH R35** · UNIVERSITY OF ROCHESTER · 2020 · $360,287

## Abstract

Project Summary/Abstract
 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.

## Key facts

- **NIH application ID:** 10027477
- **Project number:** 1R35GM137966-01
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Andrew David White
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $360,287
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10027477

## Citation

> US National Institutes of Health, RePORTER application 10027477, Learning to learn in structural biology with deep neural networks (1R35GM137966-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10027477. Licensed CC0.

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