Francesca Grisoni

This paper develops a framework for protein fitness prediction that improves generalization across distribution shifts by integrating structural and co-evolutionary information.

ACER improves protein-ligand co-folding by exploring alternative binding pockets and enhancing pose accuracy without retraining.

This paper uses machine learning to predict DNA origami stability in physiological conditions based on physicochemical parameters and DLS measurements.

The paper introduces a computational package for visualizing, describing, and comparing nanoparticle datasets from super-resolution microscopy to analyze material properties and heterogeneity.

This paper introduces LM-ABC, a language model assistant for biocatalysis that streamlines enzyme engineering workflows using natural language inputs and integrates with computational tools.

nanoFeatures is a cross-platform tool for quantitative analysis of super-resolution microscopy images of nanoparticles, extracting features like size, shape, and molecular abundance.