Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …

Drug discovery based on artificial intelligence has been in the spotlight recently as it
significantly reduces the time and cost required for developing novel drugs. With the …

The drug discovery process starts with identification of a disease-modifying target. This
critical step traditionally begins with manual investigation of scientific literature and …

The strategy of using existing drugs originally developed for one disease to treat other
indications has found success across medical fields. Such drug repurposing promises faster …

Several new or emerging drugs for dyslipidemia owe their existence, in part, to human
genetic evidence, such as observations in families with rare genetic disorders or in …

Through the representation of small molecule structures as numerical descriptors and the
exploitation of the similarity principle, chemoinformatics has made paramount contributions …

Small molecules are usually compared by their chemical structure, but there is no unified
analytic framework for representing and comparing their biological activity. We present the …

Understanding the mechanisms of candidate drugs play an important role in drug discovery.
The activating/inhibiting mechanisms between drugs and targets are major types of …

Many machine learning applications in bioinformatics currently rely on matching gene
identities when analyzing input gene signatures and fail to take advantage of preexisting …

Deep generative models for molecular generation have been gaining much attention as
structure generators to accelerate drug discovery. However, most previously developed …