F. von Delft

The paper identifies covalent molecular glue inhibitors that stabilize a non-productive Kalirin-Rac1 complex, inhibiting nucleotide exchange and reducing GEF turnover.

A public crystallographic structure and affinity dataset for enteroviral 2A protease (925 binding events, 699 compounds), with docking, cofolding and screening baselines. Fragment fine-tuning of OpenFold3-p2 improves follow-on predictions.

The paper identifies fragment binders and mutationally intolerant sites in Zika virus NS2B-NS3 protease to develop resistance-resilient antiviral inhibitors.

The paper solves the crystal structure of Arabidopsis thaliana FatA and identifies fragments that target novel regions, enabling the design of new herbicides with improved potency and novel interaction modes.

This study identifies multiple binding sites on the dengue virus polymerase through crystallographic fragment screening, providing insights for developing non-nucleoside antiflavivirals.

The paper introduces Di-Gluebodies, covalently-dimerized nanobodies, to improve cryo-EM structure determination of small proteins by providing rigidity and modularity.

The paper identifies orally bioavailable inhibitors of the Zika virus NS2B-NS3 protease that show antiviral activity in vitro and reduce viral RNA load in a murine infection model.

This paper combines crystallographic fragment screening, algorithmic merging, and automated synthesis to generate scaffolds targeting NCS-1 PPIs involved in neurological disorders.

This paper describes the optimization of a SARS-CoV-2 Mpro inhibitor to address genotoxic and metabolic liabilities, leading to improved in vivo efficacy and exposure.

This study produces, purifies, and crystallizes 24C-sterol methyltransferase from Candida albicans to support future crystallographic studies.

The paper identifies chemical fragments targeting the Chikungunya virus nsP3 macrodomain through crystallographic screening, revealing novel binding sites and potential inhibitors.

The paper identifies potent nsp3-mac1 inhibitors but finds they do not translate to antiviral activity in cell culture models.

This paper introduces a graph database approach to expand fragment merging by incorporating bioisosteric substructures, improving computational efficiency and chemical diversity in drug design.

This study identifies fragments binding to EV-D68 3C protease through crystallography and expands them into lead-like compounds, revealing novel binding modes and structural insights for drug design.

The paper identifies 75 fragments that bind to Coxsackievirus A16 2A protease, including 38 unique compounds in the active site, offering a path for broad-spectrum anti-enteroviral inhibitors.

This study uses high-throughput crystallographic fragment screening to identify 46 fragment binding poses on ZIKV NS3Hel, providing templates for antiviral drug design.

The paper introduces IF-SitePred, a method for predicting protein-ligand binding sites using learned protein representations, showing improved performance on predicted structures and simulated novel proteins.

The paper describes a set of Golden Gate vectors for efficient recombinant protein production in E. coli, with high cloning efficiency and useful tags and secretion signals.

The paper uses a graph database to identify fragment merges from chemical catalogues, finding complementary results to traditional similarity searches.

This study characterizes SARS-CoV-2 NSP15 endoribonuclease, identifies its allosteric properties, and discovers new allosteric sites for inhibitor development.

This study compares cryo and room-temperature crystallography of PTP1B with small-molecule fragments, showing RT reveals different binding modes and conformational changes.

The paper computationally designs and experimentally validates potent inhibitors of the SARS-CoV-2 Nsp3 Macrodomain, identifying 160 ligands and 152 crystal structures with submicromolar affinity.

The paper develops a machine learning scoring function for virtual screening that avoids dataset biases and demonstrates its ability to identify important binding interactions through input attribution.

The paper engineers nanobody interfaces to improve crystal reliability and diversity, resulting in better diffraction and multiple polymorphs.

The paper introduces a computational pipeline to design irreversible inhibitors, identifying a potent SARS-CoV-2 Mpro inhibitor with low micromolar IC50.

This study reports a novel dimeric and oligomeric structure of human PrPC's globular domain and explores its structural dynamics and implications for prion conversion.

This paper develops a machine learning framework to predict protein-ligand affinity from structural data, enabling the design of potent inhibitors against SARS-CoV-2 Main Protease.

This study presents crystal structures of SARS-CoV-2 NSP13 and identifies druggable pockets, revealing potential targets for antiviral drug development through fragment screening.

The paper describes an open-science drug discovery campaign that identified a non-covalent, non-peptidic inhibitor of SARS-CoV-2 main protease using crowdsourcing and computational methods.