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 developing …

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 …

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

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 …

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 …

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 past few years have witnessed enormous progress toward applying machine learning
approaches to the development of protein–ligand scoring functions. However, the robust …

Currently, molecular docking is becoming a key tool in drug discovery and molecular
modeling applications. The reliability of molecular docking depends on the accuracy of the …

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 …