Learning to learn in structural biology with deep neural networks

通过深度神经网络学习结构生物学

• 批准号: 10437899
• 负责人: Andrew David White
• 金额: $ 34.55万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10437899
• 关键词: Area; Benchmarking; Binding; Data; Data Set; Drug Design; Human; Image; Intelligence; Language; Learning; Medicine; Molecular; Neurodegenerative Disorders; Outcome; Peptides; Play; Proteins; Research; Techniques; Traction; Translating; Work; biophysical model; deep learning; deep learning model; deep neural network; design; functional genomics; novel strategies; novel therapeutics; programs; protein structure; simulation; structural biology; success; tool

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.

项目总结/文摘