2026-09-18T23:43:43Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1566062024-09-04T03:56:40Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Jacobson, Joseph M.
Mitnikov, Ilan
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-09-03T21:11:07Z
2024-09-03T21:11:07Z
2024-05
2024-07-11T15:31:03.679Z
https://hdl.handle.net/1721.1/156606
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.
M.Eng.
Massachusetts Institute of Technology
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-nc-nd/4.0/
Geometric Deep Learning for Biomolecules
Thesis
application/pdf
Master
Master of Engineering in Computation and Cognition
Publication
unknown
unknown
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