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 …

Driven by rapid advances in computer hardware and publicly available datasets over the
past decade, deep learning has achieved tremendous success in the transformation of many …

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 …

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 …

In recent years, the development of high-throughput screening (HTS) technologies and their
establishment in an industrialized environment have given scientists the possibility to test …

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 …

The main problem of small molecule-based drug discovery is to find a candidate molecule
with increased pharmacological activity, proper ADME, and low toxicity. Recently, machine …

Background Drug discovery processes, such as new drug development, drug synergy, and
drug repurposing, consume significant yearly resources. Computer-aided drug discovery …

Deep learning is an artificial intelligence technique in which models express geometric
transformations over multiple levels. This method has shown great promise in various fields …

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 …