Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various
areas, such as virtual screening, drug repurposing and identification of potential drug side …

Activity and property prediction models are the central workhorses in drug discovery and
materials sciences, but currently, they have to be trained or fine-tuned for new tasks. Without …

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 …

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 …

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 increase of publicly available bioactivity data in recent years has fueled and catalyzed
research in chemogenomics, data mining, and modeling approaches. As a direct result, over …

Virtual screening (VS) is a powerful technique for identifying hit molecules as starting points
for medicinal chemistry. The number of methods and softwares which use the ligand and …

Background Molecular descriptors and fingerprints have been routinely used in QSAR/SAR
analysis, virtual drug screening, compound search/ranking, drug ADME/T prediction and …