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Powerful generative AI models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically relevant drug targets, hindering their widespread adoption in drug discovery efforts. In this work, we propose FlowDock, the first deep geometric generative model based on conditional flow matching that learns to directly map unbound (apo) structures to their bound (holo) counterparts for an arbitrary number of binding ligands.
View Article and Find Full Text PDFChallenges and compromises: Predicting unbound antibody structures with deep learning.
Curr Opin Struct Biol
January 2025
Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, United Kingdom.
Therapeutic antibodies are manufactured, stored and administered in the free state; this makes understanding the unbound form key to designing and improving development pipelines. Prediction of unbound antibodies is challenging, specifically modelling of the CDRH3 loop, where inaccuracies are potentially worse due to a bias in structural data towards antibody-antigen complexes. This class imbalance provides a challenge for deep learning models trained on this data, potentially limiting generalisation to unbound forms.
View Article and Find Full Text PDFFeasibility of Ex Vivo Ligandomics.
Biomolecules
January 2025
Cullen Eye Institute, Department of Ophthalmology, Baylor College of Medicine, Houston, TX 77030, USA.
We developed ligandomics for the in vivo profiling of vascular ligands in mice, discovering secretogranin III (Scg3) as a novel angiogenic factor that selectively binds to retinal vessels of diabetic but not healthy mice. This discovery led to the development of anti-Scg3 therapy for ocular vasculopathies. However, in vivo ligandomics requires intracardial perfusion to remove unbound phage clones, limiting its use to vascular endothelial cells (ECs).
View Article and Find Full Text PDFEffect of Target-Mediated Disposition on Iptacopan Clinical Pharmacokinetics in Participants with Normal or Impaired Hepatic Function.
Clin Pharmacol Ther
January 2025
Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA.
Iptacopan, a first-in-class complement factor B inhibitor acting proximally in the alternative complement pathway, has been shown to be safe and effective for patients with complement-mediated diseases. Iptacopan selectively binds with high affinity to factor B, a soluble, plasma-based, hepatically produced protein. Factor B is abundant in the circulation but can be saturated at the iptacopan clinical dose of 200 mg twice daily.
View Article and Find Full Text PDFThe clinical breakpoint for a drug-pathogen combination reflects the drug susceptibility of the pathogen wild-type population, the location of the infection, the integrity of the host immune response, and the drug-pathogen pharmacokinetic (PK)/pharmacodynamic (PD) relationship. That PK/PD relationship, along with the population variability in drug exposure, is used to determine the probability of target attainment (PTA) of the PK/PD index at a specified minimum inhibitory concentration (MIC) for a selected target value. The PTA is used to identify the pharmacodynamic cutoff value (CO), which is one of the three components used to establish the clinical breakpoint.
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