Biomolecular Modeling at Scale

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.

Name thesis:degree_name

Doctoral

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

2025

Author dc:creator

• Wohlwend, Jeremy

Advisor dc:contributor.advisor

• Barzilay, Regina

Statement dc:rights

• Attribution 4.0 International (CC BY 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164122

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164122