Alejandro Giorgetti

The paper presents an AI-based method to identify novel chemotype ligands for GPCRs by combining structural data, a random forest classifier, and a kinetic model, demonstrating its effectiveness with a new antagonist for the adenosine A2A receptor.

MoNvIso predicts the correct protein isoform for mutations and builds structural models of wild-type and variant isoforms using gene names and mutation lists.

The paper introduces an open-source systems biology toolkit to simulate GPCR signal-transduction pathways using structural data and mathematical models.

The paper identifies a recurrent insertion in the N-terminal domain of the SARS-CoV-2 spike protein and examines its association with various viral lineages.