Post-Doctoral Research Visit F/M Why Do Protein Structure Predictors Confidently Produce Nonsense, and Can We Anticipate These Failures?

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description Deep learning has transformed protein structure prediction. However, current models can still produce physically implausible structures with high confidence. These failures, known as hallucinations , remain poorly understood and raise fundamental questions about the reliability, interpretability, and limitations of protein language models (pLMs). This project aims to systematically characterize these failures and develop methods to understand and anticipate them. It builds on FLoPS, a dataset of hallucinated protein language model predictions, with physically grounded quality annotations generated using the abstraqt method. These annotations provide residue- and contact-level information, going beyond conventional metrics based on comparisons with reference structures. The project comprises two complementary research directions:. Model interpretability and explainability: identify sequence features, attention mechanisms, and internal representations associated with hallucinations, with the goal of understanding why models generate physically implausible structures. Generative protein structure modelling: depending on the candidate’s interests and expertise, contribute to the development of a novel flow-matching architecture for protein structure prediction, aiming to reduce reliance on evolutionary information and incorporate physically motivated mechanisms of structural formation.