Deep learning has disrupted nearly every field of research, including those of direct
importance to drug discovery, such as medicinal chemistry and pharmacology. This …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Various eutectic systems have been proposed and studied over the past few decades. Most
of the studies have focused on three typical types of eutectics: eutectic metals, eutectic salts …

A discussion of many of the recently implemented features of GAMESS (General Atomic and
Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library …

Density functional theory (DFT) plays a pivotal role in chemical and materials science
because of its relatively high predictive power, applicability, versatility, and computational …

De novo drug design is a computational approach that generates novel molecular structures
from atomic building blocks with no a priori relationships. Conventional methods include …

Drug discovery and development are among the most important translational science
activities that contribute to human health and wellbeing. However, the development of a new …

As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …

Machine learning enables computers to address problems by learning from data. Deep
learning is a type of machine learning that uses a hierarchical recombination of features to …

The past 20 years have witnessed a renaissance of dye chemistry, moving from traditional
colorant research toward functional materials. Different from traditional colorant research …