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Science, 2025. A microfluidic platform to measure catalytic constants for hundreds of Adenylate Kinase variants, enabling mapping of the sequence-catalysis landscape and development of superior predictive models.

ICML, 2025. an end-to-end differentiable framework that learns to retrieve relevant protein homologs while simultaneously training for downstream tasks, achieving state-of-the-art fitness prediction performance while being orders of magnitude faster than traditional MSA-based approaches.

Nature Biotech, 2024. A unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels is introduced to make sense of the exploding diversity of machine learning approaches.

Nature, 2023. A computational framework to predict viral escape from pre-pandemic information only (evolutionary data and 3D structure).

We introduce DyNeMo, a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical modes.

Nature, 2021. Deep Generative Models (Bayesian VAEs) of evolutionary sequences to predict the effects of missense mutations in human proteins.