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 …
DeepTraSynergy: drug combinations using multimodal deep learning with transformers
Motivation Screening bioactive compounds in cancer cell lines receive more attention.
Multidisciplinary drugs or drug combinations have a more effective role in treatments and …
Multidisciplinary drugs or drug combinations have a more effective role in treatments and …
Understanding membrane protein drug targets in computational perspective
J Gong, Y Chen, F Pu, P Sun, F He, L Zhang… - Current Drug …, 2019 - ingentaconnect.com
Membrane proteins play crucial physiological roles in vivo and are the major category of
drug targets for pharmaceuticals. The research on membrane protein is a significant part in …
drug targets for pharmaceuticals. The research on membrane protein is a significant part in …
Identification of drug–target interactions via dual laplacian regularized least squares with multiple kernel fusion
Abstract Detection of Drug–Target Interactions (DTIs) is the time-consuming and laborious
experiment via biochemical approaches. Machine learning based methods have been …
experiment via biochemical approaches. Machine learning based methods have been …
DeepCDA: deep cross-domain compound–protein affinity prediction through LSTM and convolutional neural networks
Motivation An essential part of drug discovery is the accurate prediction of the binding affinity
of new compound–protein pairs. Most of the standard computational methods assume that …
of new compound–protein pairs. Most of the standard computational methods assume that …
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 …
Identification of drug–target interactions via multiple kernel-based triple collaborative matrix factorization
Targeted drugs have been applied to the treatment of cancer on a large scale, and some
patients have certain therapeutic effects. It is a time-consuming task to detect drug–target …
patients have certain therapeutic effects. It is a time-consuming task to detect drug–target …
Identification of drug-target interactions via multiple information integration
Abstract Identifying Drug-Target Interactions (DTIs) is an important process in drug
discovery. Traditional experimental methods are expensive and time-consuming for …
discovery. Traditional experimental methods are expensive and time-consuming for …
ML-DTI: mutual learning mechanism for interpretable drug–target interaction prediction
Deep learning (DL) provides opportunities for the identification of drug–target interactions
(DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most …
(DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most …