Geometric Deep Learning for Biomolecules

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Mitnikov, Ilan

Advisor dc:contributor.advisor

• Jacobson, Joseph M.

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156606

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156606