Rocco Moretti

The paper validates and analyzes 12,000 AI-designed CAR-T binders from a global competition, identifying successful designs and factors improving their performance.

The paper develops a computational method to prioritize neuroactive ligands using structural data and zebrafish testing, identifying effective antagonists for hypocretin receptors.

This paper evaluates combining graph neural networks with expert descriptors for ligand-based virtual screening, finding that simpler GNNs with descriptors perform comparably to complex ones and that expert descriptors excel in scaffold-split scenarios.

This study compares self-supervised ML methods with biophysical approaches in protein design, finding ML improves sampling but not scoring of high-fitness variants.

The paper evaluates computational hit-finding methods for targeting the WDR domain of LRRK2, a Parkinson's disease protein, finding that diverse strategies yielded weakly potent compounds.

The paper introduces a chirality-aware graph neural network for drug discovery that improves predictive performance and interpretability in quantitative structure activity relationship modeling.

The paper presents a framework for ensuring reproducibility in bio-macromolecular modeling through automated benchmarks and a test server, demonstrating its utility for Rosetta and other tools.