Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …

Despite the fact that deep learning has achieved remarkable success in various domains
over the past decade, its application in molecular informatics and drug discovery is still …

Over the last decade, deep learning (DL) methods have been extremely successful and
widely used to develop artificial intelligence (AI) in almost every domain, especially after it …

Increases in throughput and installed base of biomedical research equipment led to a
massive accumulation of-omics data known to be highly variable, high-dimensional, and …

Deep learning is rapidly advancing many areas of science and technology with multiple
success stories in image, text, voice and video recognition, robotics, and autonomous …

Computational prediction of drug–target interactions (DTIs) has become an essential task in
the drug discovery process. It narrows down the search space for interactions by suggesting …

Over the past decade we have witnessed the increasing sophistication of machine learning
algorithms applied in daily use from internet searches, voice recognition, social network …

A major challenge in cancer treatment is predicting the clinical response to anti-cancer
drugs on a personalized basis. The success of such a task largely depends on the ability to …

Highlights•Conceptual foundations of the drug repurposing paradigm are
reviewed.•Description of the technological trends behind the raise of in silico …