Ryan Byrne

TL;DR: The current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects.

TL;DR: An overview of the current state of the art of in silico methodologies is provided, some of the well-established methods are described in detail, and how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve the understanding of (poly)pharmacology are reflected.

TL;DR: The scaffold-hopping ability of the new Weighted Holistic Atom Localization and Entity Shape (WHALES) molecular descriptors compared to seven state-of-the-art molecular representations on 30,000 compounds and 182 biological targets is demonstrated.

TL;DR: CSP Analyzer as discussed by the authors is an automated tool for the assessment and binning of multiple 2D HSQC spectra for ligand binding, based on machine-learning-driven statistical discrimination.

TL;DR: The results of this study corroborate the use of fractal dimensionality as an innovative shape‐based molecular representation for molecular scaffold‐hopping in de novo generated small molecules mimicking the structurally complex natural product (−)‐englerin A.