X-ray multi-projection imaging (XMPI) is an emerging experimental technique for the acquisition of rotation-free, time-resolved, volumetric information on stochastic processes. The technique is developed for high-brilliance light-source facilities, aiming to address known limitations of state-of-the-art imaging methods in the acquisition of 4D sample information, linked to their need for sample rotation. XMPI relies on a beam-splitting scheme, that illuminates a sample from multiple, angularly spaced viewpoints, and employs fast, indirect, X-ray imaging detectors for the collection of the data.
View Article and Find Full Text PDFThe pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.
View Article and Find Full Text PDFIn silico prediction of xenobiotic metabolism is an important strategy to accelerate the drug discovery process, as candidate compounds often fail in clinical phases due to their poor pharmacokinetic profiles. Here we present Meta, a dataset of quantum-mechanical (QM) optimized metabolic substrates, including force field parameters, electronic and physicochemical properties. Meta comprises 2054 metabolic substrates extracted from the MetaQSAR database.
View Article and Find Full Text PDFX-ray multi-projection imaging (XMPI) has the potential to provide rotation-free 3D movies of optically opaque samples. The absence of rotation enables superior imaging speed and preserves fragile sample dynamics by avoiding the centrifugal forces introduced by conventional rotary tomography. Here, we present our XMPI observations at the ID19 beamline (ESRF, France) of 3D dynamics in melted aluminum with 1000 frames per second and 8 µm resolution per projection using the full dynamical range of our detectors.
View Article and Find Full Text PDFThe ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data.
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