Academic and pharmaceutical industry research are both key for progresses in the field of
molecular machine learning. Despite common open research questions and long-term …

Abstract Machine learning (ML) is a promising approach for predicting small molecule
properties in drug discovery. Here, we provide a comprehensive overview of various ML …

Currently, machine learning methods drive the success of artificial intelligence in academia
and industry. Among machine learning methods, Deep Learning has emerged as a game …

Highlights•We review machine learning methods/tools relevant to ligand-based virtual
screening.•Machine learning methods classify compounds and predict new active …

With the development of computer-assisted techniques, research communities, including
biochemistry and deep learning, have been devoted into the drug discovery field for over a …

The chemistry community is currently witnessing a surge of scientific discoveries in organic
chemistry supported by machine learning (ML) techniques. Whereas many of these …

QSAR. Despite this long tradition, machine learning methods gained substantial momentum
recently triggered by the success of deep learning in many application areas. 1 A wide …

In this Viewpoint, we discuss the current progress in applications of machine learning (ML)
and artificial intelligence (AI) to meet the challenges of computational drug discovery. We …

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

Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The
widespread adoption of machine learning, in particular deep learning, in multiple scientific …