The productivity of the pharmaceutical industry is on the decline. Failure rates in clinical
trials exceed 90% after therapies are tested in model organisms, and the cost to develop a …

A few decades ago, drug discovery and development were limited to a bunch of medicinal
chemists working in a lab with enormous amount of testing, validations, and synthetic …

Highlights•Machine learning techniques (MLTs) are progressing the drug discovery
process.•Conventional MLTs require large data, lack transparency and are not …

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 …

Introduction Deep discriminative and generative neural-network models are becoming an
integral part of the modern approach to ligand-based novel drug discovery. The variety of …

Background: Drug discovery, which is the process of discovering new candidate
medications, is very important for pharmaceutical industries. At its current stage, discovering …

Background Computer-aided drug discovery (CADD) has become an essential part of
several projects in different settings and research environments. CADD has largely …

Machine learning has revolutionized drug discovery by speeding up the process and
improving therapeutic interventions, transforming the pharmaceutical research and …

Highlights•Deep learning methods have gained outstanding achievements.•We review deep
learning methods/tools relevant to drug discovery research.•We discuss opportunities …

In recent decades, artificial intelligence and machine learning have played a significant role
in increasing the efficiency of processes across a wide spectrum of industries. When it …