2026-09-20T15:44:42Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1641222025-12-04T03:05:04Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Barzilay, Regina
Wohlwend, Jeremy
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-12-03T16:09:17Z
2025-12-03T16:09:17Z
2025-05
2025-08-14T19:45:39.966Z
https://hdl.handle.net/1721.1/164122
Predicting the structure and interactions of biomolecules is a fundamental problem in computational biology, with broad implications for disease understanding and drug discovery. Advances in deep learning have enabled remarkable progress, but scaling these approaches to the varied and complex realities of biology is a persistent challenge. This work introduces deep learning methods for biomolecular modeling at scale, designed for efficiency, adaptability, and accessibility. The early chapters present models developed in the general molecular domain, including prediction of structure and interactions for proteins, nucleic acids, and small molecules. To demonstrate how these methods extend to specific biological problems, the latter portion of this work focuses on modeling T cell receptor recognition. As a key immunological mechanism, it highlights the promise of scalable models, but also their present limitations in capturing fine-grained molecular selectivity. Together, these contributions define a framework for bridging foundational models and domain-specific applications, with the potential to scale, and meet the demands of increasingly complex biological systems.
Ph.D.
Massachusetts Institute of Technology
Attribution 4.0 International (CC BY 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by/4.0/
Biomolecular Modeling at Scale
Thesis
application/pdf
Doctoral
Doctor of Philosophy
Publication
unknown
unknown
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