Drug discovery is a cost and time-intensive process that is often assisted by computational
methods, such as virtual screening, to speed up and guide the design of new compounds …

Inspired by natural language processing techniques, we here introduce Mol2vec, which is
an unsupervised machine learning approach to learn vector representations of molecular …

Kinases are a prime target of drug development efforts with> 60 drug approvals in the past
two decades. Due to the research into this protein family, a wealth of data has been …

Proteochemometrics is a machine learning based modeling approach relying on a
combination of ligand and protein descriptors. With ongoing developments in machine …

Identifying drug–target interactions (DTIs) is important for drug discovery. However,
searching all drug–target spaces poses a major bottleneck. Therefore, recently many deep …

Motivation Many inference problems in bioinformatics, including drug bioactivity prediction,
can be formulated as pairwise learning problems, in which one is interested in making …

The interpretation of high-dimensional structure–activity data sets in drug discovery to
predict ligand–protein interaction landscapes is a challenging task. Here we present Drug …

Machine learning techniques are being applied in quantifying structure–property
relationships for a wide variety of materials, where the properly represented materials play …

Abstract Development of novel anti-cancer treatments requires not only a comprehensive
knowledge of cancer processes and drug mechanisms of action, but also the ability to …

Background Computer-aided drug development can alleviate limitations in drug research
and development processes, such as long cycles, high costs, and limited targeting. Deep …