… of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) …
We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. …

… space between today's chemical libraries and PPI physico-… libraries enriched in putative
PPI inhibitors. Here, we show how chemoinformatics can assist library design by learning …

… They are used as current standard benchmark data for comparing the performance of machine
learning algorithms for drug-target interaction. We use these datasets as they are without …

… Researchers have been at work trying to determine new Drug Target Interactions (DTI) that
… to a target. Machine learning (ML) techniques help establish the interaction between drugs …

… , target sequence similarity and the drug–target interaction network … unknown drug–target
interaction networks from chemical … drug–target interaction inference as a supervised learning …

… In this paper, we modeled drug-target interaction prediction … a deep-learning-based model
to predict potential interactions. … predicted more credible drug-target interactions. What’s more, …

… machine learning method that uses the drug–target network … new interactions in a drug–target
interaction network. Formally… 1 , t 2 ,…, t n t } of target proteins. There is also a set of known …

… target interaction is a prominent research area in the field of drug discovery. It refers to the
recognition of interactions … Wet lab experiments to identify these interactions are expensive as …

… Furthermore, target proteins need also to be predicted for some new drugs without any known
target interaction information. In this paper, a semi-supervised learning method NetCBP is …

… To predict the drug-target interactions, we have designed a set of in silico tools by … All
these further suggest that the learning algorithms based on the general chemicals and …