Developments in computational omics technologies have provided new means to access
the hidden diversity of natural products, unearthing new potential for drug discovery. In …

In recent years, extensive research in the field of Deep Learning (DL) has led to the
development of a wide array of machine learning algorithms dedicated to solving complex …

The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and
unambiguous identification and characterization of peaks is a difficult, but critically important …

Background Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method
in clinical medicine, but it has always suffered from the problem of long acquisition time …

Accurate and efficient molecular spectra simulations are crucial for substance discovery and
structure identification. However, the conventional approach of relying on the quantum …

Background Food adulteration has emerged as a significant challenge in the food industry,
impacting consumer health and trust in the market. Utilizing machine learning especially …

The reliability of organic molecular crystal structure prediction has improved tremendously in
recent years. Crystal structure predictions for small, mostly rigid molecules are quickly …

NMR spectroscopy has been applied to cells and tissues analysis since its beginnings, as
early as 1950. We have attempted to gather here in a didactic fashion the broad diversity of …

The analysis of nuclear magnetic resonance (NMR) spectra to detect peaks and
characterize their parameters, often referred to as deconvolution, is a crucial step in the …

The interpretation of spectral data, including mass, nuclear magnetic resonance, infrared,
and ultraviolet–visible spectra, is critical for obtaining molecular structural information. The …