Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Protein–ligand scoring functions are widely used in structure-based drug design for fast
evaluation of protein–ligand interactions, and it is of strong interest to develop scoring …

Classical scoring functions have reached a plateau in their performance in virtual screening
and binding affinity prediction. Recently, machine-learning scoring functions trained on …

Structure-based drug design is critically dependent on accuracy of molecular docking
scoring functions, and there is of significant interest to advance scoring functions with …

In the last decade, machine learning and artificial intelligence applications have received a
significant boost in performance and attention in both academic research and industry. The …

In recent years, protein–ligand interaction scoring functions derived through machine-
learning are repeatedly reported to outperform conventional scoring functions. However …

Virtual screening—predicting which compounds within a specified compound library bind to
a target molecule, typically a protein—is a fundamental task in the field of drug discovery …

Background: Calculation of ligand-binding affinity is an open problem in computational
medicinal chemistry. The ability to computationally predict affinities has a beneficial impact …

In current study, a simple multispectral imaging (430–1010 nm) system along with linear and
non-linear regressions were used to assess the various fish spoilage indicators during 12 …

The new generation of post-genomic targets, such as protein–protein interactions (PPIs),
often require new chemotypes not well represented in current compound libraries. This is …