Inference Time Search for Protein Structure Prediction
Name
qi-rqi-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
1.99 MB
Format
Adobe PDF
Checksum (MD5)
0776fa2cd6b308f9f4b65ef6449f2a57
Author(s)
Qi, Richard
Advisor(s)
Barzilay, Regina
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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