Abstract The recently emerged 2019 Novel Coronavirus (SARS‐CoV‐2) and associated
COVID‐19 disease cause serious or even fatal respiratory tract infection and yet no …

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 …

On average, an approved drug currently costs US $2–3 billion and takes more than 10 years
to develop. In part, this is due to expensive and time-consuming wet-laboratory experiments …

The data sets available to train models for in silico drug discovery efforts are often small.
Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence …

Predicting the functional sites of a protein from its structure, such as the binding sites of small
molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two …

Molecular docking has been widely employed as a fast and inexpensive technique in the
past decades, both in academic and industrial settings. Although this discipline has now had …

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

Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, developing drugs for central nervous system (CNS) disorders remains the most …

The goal of structure-based drug discovery is to find small molecules that bind to a given
target protein. Deep learning has been used to generate drug-like molecules with certain …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …