Journal of chemical information and modeling
Mar 7, 2018
Parametrization of small organic molecules for classical molecular dynamics simulations is not trivial. The vastness of the chemical space makes approaches using building blocks challenging. The most common approach is therefore an individual paramet...
Although there have been impressive strides in detector development for time-of-flight positron emission tomography, most detectors still make use of simple signal processing methods to extract the time-of-flight information from the detector signals...
Journal of applied clinical medical physics
Nov 30, 2016
This work describes the use of 3D printing technology to create individualized boluses for patients treated with electron beam therapy for skin lesions of the eye canthi. It aimed to demonstrate the effectiveness of 3D-printed over manually fabricate...
Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Jul 2, 2015
We modified a commercially available synthesis module for nucleophilic [(18)F]fluorinations (TRACERlab(TM) FXFDG, GE Healthcare) to enable the reliable synthesis of 2-[(18)F]fluoro-4-borono-L-phenylalanine ([(18)F]FBPA) via direct electrophilic subst...
Position-sensitive positron cameras using silicon pixel detectors have been applied for some preclinical and intraoperative clinical applications. However, the spatial resolution of a positron camera is limited by positron multiple scattering in the ...
Journal of chemical theory and computation
Apr 23, 2015
Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to computationa...
Journal of chemical information and modeling
May 12, 2025
The rapid adoption of big data, machine learning (ML), and generative artificial intelligence (AI) in chemical discovery has heightened the importance of quantifying molecular similarity. Molecular similarity, commonly assessed as the distance betwee...
We present a simplest-level electron nuclear dynamics/machine learning (SLEND/ML) approach to predict chemical properties in ion cancer therapy (ICT) reactions. SLEND is a time-dependent, variational, on-the-fly, and nonadiabatic method. In SLEND, nu...
Nucleic acid electron density interpretation after phasing by molecular replacement or other methods remains a difficult problem for computer programs to deal with. Programs tend to rely on time-consuming and computationally exhaustive searches to re...
We report the development of deep-learning coherent electron diffractive imaging at subangstrom resolution using convolutional neural networks (CNNs) trained with only simulated data. We experimentally demonstrate this method by applying the trained ...
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