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 …

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 …

Molecular machine learning has been maturing rapidly over the last few years. Improved
methods and the presence of larger datasets have enabled machine learning algorithms to …

The drug development process is a major challenge in the pharmaceutical industry since it
takes a substantial amount of time and money to move through all the phases of develo** …

Quantitative structure–activity relationship (QSAR) modelling, an approach that was
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …

The discovery and advances of medicines may be considered as the ultimate relevant
translational science effort that adds to human invulnerability and happiness. But advancing …

Computational approaches to drug discovery can reduce the time and cost associated with
experimental assays and enable the screening of novel chemotypes. Structure-based drug …

Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …

We propose a novel deep learning approach for predicting drug–target interaction using a
graph neural network. We introduce a distance-aware graph attention algorithm to …

Motivation Structure based ligand discovery is one of the most successful approaches for
augmenting the drug discovery process. Currently, there is a notable shift towards machine …