G.J. Correy

On 489 evaluable new Mac1 complexes, AlphaFold3, Chai-1 and Boltz-2 placed over half of ligands within 2 Å, but on docking hit-lists AF3 and Boltz-2 added little over docking.

This study uses cryo-EM to reveal structural plasticity and ligand promiscuity of CYP3A4, showing multiple conformations and highlighting the F/G loop's role in binding pocket remodeling.

The paper uses machine learning-augmented virtual screening to discover a novel series of Mac1 inhibitors for SARS-CoV-2 NSP3, validated by experimental assays and crystallography.

This study systematically evaluates the effectiveness of random small perturbations in ligand optimization, finding 11.2% of analogs improved potency by ≥10-fold but often had worse pharmacokinetics.

This study shows how a designed generalist binding protein can evolve into specific functions like a fluorophore binder and an efficient enzyme through weak, promiscuous interactions.

This paper identifies AVI-6451, an orally bioavailable inhibitor of SARS-CoV-2 Mac1 with once-daily efficacy in a mouse model.

This study identifies Mac1 as a therapeutic target for SARS-CoV-2 and demonstrates that AVI-4206, a small molecule inhibitor, reduces viral replication and improves survival in an animal model.

This study shows that CHIKV macrodomain catalytic activity affects virus replication and transmission in Aedes mosquitoes, with mutation-specific outcomes in different host contexts.

This study characterizes AcrIF11, an ADP-ribosyltransferase that inhibits CRISPR-Cas systems with high potency and specificity, and shows its reversibility through macrodomain enzymes.

The paper improves qFit-ligand to better model ligand conformations in structural data, enhancing analysis of conformational heterogeneity in ligand-bound structures.

Shape-based fragment merging (FrankenROCS) with Thompson sampling over 22 billion molecules found neutral SARS-CoV-2 macrodomain inhibitors; analogs reached single-digit micromolar IC50, backed by 137 crystal structures.

This paper describes the discovery of new coronavirus MPro inhibitors with improved pharmacokinetics, broad-spectrum antiviral activity, and superior in vivo efficacy compared to existing drugs.

This paper uses unsupervised machine learning to identify active fragments from crystallographic data, distinguishing them from inactive ones without activity data.