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Artificial intelligence for drug discovery: are we there yet?
Drug discovery is adapting to novel technologies such as data science, informatics, and
artificial intelligence (AI) to accelerate effective treatment development while reducing costs …
artificial intelligence (AI) to accelerate effective treatment development while reducing costs …
[HTML][HTML] A review on machine learning approaches and trends in drug discovery
Drug discovery aims at finding new compounds with specific chemical properties for the
treatment of diseases. In the last years, the approach used in this search presents an …
treatment of diseases. In the last years, the approach used in this search presents an …
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
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 …
process of drug discovery. There is a need to develop novel and efficient prediction …
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
New technologies have enabled the investigation of biology and human health at an
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
Identifying drug–target interactions based on graph convolutional network and deep neural network
Identification of new drug–target interactions (DTIs) is an important but a time-consuming
and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers …
and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers …
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
The emergence of large-scale genomic, chemical and pharmacological data provides new
opportunities for drug discovery and repositioning. In this work, we develop a computational …
opportunities for drug discovery and repositioning. In this work, we develop a computational …
Machine learning for drug-target interaction prediction
Identifying drug-target interactions will greatly narrow down the scope of search of candidate
medications, and thus can serve as the vital first step in drug discovery. Considering that in …
medications, and thus can serve as the vital first step in drug discovery. Considering that in …
An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction
Accurately identifying potential drug–target interactions (DTIs) is a key step in drug
discovery. Although many related experimental studies have been carried out for identifying …
discovery. Although many related experimental studies have been carried out for identifying …
Supervised graph co-contrastive learning for drug–target interaction prediction
Abstract Motivation Identification of Drug–Target Interactions (DTIs) is an essential step in
drug discovery and repositioning. DTI prediction based on biological experiments is time …
drug discovery and repositioning. DTI prediction based on biological experiments is time …
Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning
Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual
drug screening. Most DTI prediction methods cast the problem as a binary classification task …
drug screening. Most DTI prediction methods cast the problem as a binary classification task …