Geometric Deep Learning for Biomolecules
Name
mitnikov-ilanm-meng-bcs-2024-thesis.pdf
Description
Thesis PDF
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5.57 MB
Format
Adobe PDF
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Author(s)
Mitnikov, Ilan
Advisor(s)
Jacobson, Joseph M.
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
Recent advancements in machine learning offer a promising pathway to deeper insights into biological phenomena. This manuscript explores the integration of geometric deep learning techniques to model biological structures. By embedding inductive biases based on geometry and physical laws, we aim to enhance our understanding and predictive capabilities in biomolecular systems. We present methods using equivariant neural networks for geometrical protein representation learning, molecular representation learning for electron density prediction, and scalable molecular dynamics simulations using stochastic interpolants.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
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