Inference Time Search for Protein Structure Prediction

Scaling inference-time compute for deep learning models has led to superhuman performance in games and enhanced reasoning capabilities for language models. However, similar gains have not yet been made in the field of biomolecular structure prediction. We introduce a new paradigm for inference-time search by adding architectural components and a finetuning procedure to state-of-the-art structure prediction models that give rise to a discrete latent space. We implement algorithms for searching and sampling in this discrete latent space and conduct experiments on a small model, demonstrating an increase in oracle and top-1-selected accuracy for predicted protein-protein complex structures.

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

2025

Author dc:creator

• Qi, Richard

Advisor dc:contributor.advisor

• Barzilay, Regina

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162989

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162989