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 …
Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey
Computational prediction of drug–target interactions (DTIs) has become an essential task in
the drug discovery process. It narrows down the search space for interactions by suggesting …
the drug discovery process. It narrows down the search space for interactions by suggesting …
Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Motivation In bioinformatics, machine learning-based methods that predict the compound–
protein interactions (CPIs) play an important role in the virtual screening for drug discovery …
protein interactions (CPIs) play an important role in the virtual screening for drug discovery …
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high
cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the …
cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the …
DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks
Motivation Drug discovery demands rapid quantification of compound–protein interaction
(CPI). However, there is a lack of methods that can predict compound–protein affinity from …
(CPI). However, there is a lack of methods that can predict compound–protein affinity from …
Predicting drug–protein interaction using quasi-visual question answering system
Identifying novel drug–protein interactions is crucial for drug discovery. For this purpose,
many machine learning-based methods have been developed based on drug descriptors …
many machine learning-based methods have been developed based on drug descriptors …
Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure
H Shi, S Liu, J Chen, X Li, Q Ma, B Yu - Genomics, 2019 - Elsevier
The identification of drug-target interactions has great significance for pharmaceutical
scientific research. Since traditional experimental methods identifying drug-target …
scientific research. Since traditional experimental methods identifying drug-target …
DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features
Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and
development. Computational prediction of DTIs can effectively complement experimental …
development. Computational prediction of DTIs can effectively complement experimental …
Improving compound–protein interaction prediction by building up highly credible negative samples
Motivation: Computational prediction of compound–protein interactions (CPIs) is of great
importance for drug design and development, as genome-scale experimental validation of …
importance for drug design and development, as genome-scale experimental validation of …
[HTML][HTML] A comprehensive review of feature based methods for drug target interaction prediction
Drug target interaction is a prominent research area in the field of drug discovery. It refers to
the recognition of interactions between chemical compounds and the protein targets in the …
the recognition of interactions between chemical compounds and the protein targets in the …