Daniele Canzani

Structure-free, site-resolved contrastive learning extends small-molecule discovery beyond the reach of structure-based modeling

Ptarmigan-1 uses contrastive learning to predict small molecule-protein interactions without requiring 3D structure, outperforming traditional methods on challenging targets.

Carafe2 enables high quality in silico spectral library generation for timsTOF data-independent acquisition proteomics

Carafe2 generates high-quality in silico spectral libraries for timsTOF DIA proteomics by fine-tuning deep learning models on DIA data, outperforming DDA-trained models in multiple applications.

Native, Spatiotemporal Profiling of the Global Human Regulome

This paper introduces a method to profile the regulome across human cell lines and perturbations, revealing transcription factor dynamics and drug responses.

An artificial intelligence accelerated virtual screening platform for drug discovery

The paper develops RosettaVS, an AI-accelerated virtual screening platform that identifies drug candidates with high binding affinity across two targets, achieving a 14% and 44% hit rate in under seven days.