Francesco Calvanese

This paper introduces ILMC, a sampling strategy for protein language models that improves diversity while steering generation toward desired properties.

The paper uses probabilistic generative models to estimate the number of self-reproducing RNAs and finds that at least 10^39 such ribozymes may exist.

The paper improves generative models for biological sequences by integrating experimental feedback, increasing functional sequence generation from 6.7% to 63.7% in ribozyme design.

This paper introduces eaDCA, a sparse generative model for RNA families that efficiently predicts functional sequences and mutation effects, showing comparable performance to complex methods with fewer parameters.