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 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 …
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 …
SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines
Computational prediction of the interaction between drugs and targets is a standing
challenge in the field of drug discovery. A number of rather accurate predictions were …
challenge in the field of drug discovery. A number of rather accurate predictions were …
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 …
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 …
Biomedical data and computational models for drug repositioning: a comprehensive review
Drug repositioning can drastically decrease the cost and duration taken by traditional drug
research and development while avoiding the occurrence of unforeseen adverse events …
research and development while avoiding the occurrence of unforeseen adverse events …
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 …