A kernel matrix dimension reduction method for predicting drug-target interaction
Sign Up to like & getrecommendations! Published in 2017 at "Chemometrics and Intelligent Laboratory Systems"
DOI: 10.1016/j.chemolab.2017.01.016
Abstract: Abstract The prediction of drug-target interactions plays an important role in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are… read more here.
Keywords: predicting drug; kernel matrix; target interaction; drug ... See more keywords
Predicting drug-induced liver injury: The importance of data curation
Sign Up to like & getrecommendations! Published in 2017 at "Toxicology"
DOI: 10.1016/j.tox.2017.06.003
Abstract: Drug-induced liver injury (DILI) is a major issue for both patients and pharmaceutical industry due to insufficient means of prevention/prediction. In the current work we present a 2-class classification model for DILI, generated with Random… read more here.
Keywords: induced liver; predicting drug; liver injury; drug induced ... See more keywords
NRBdMF: A recommendation algorithm for predicting drug effects considering directionality
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c01210
Abstract: Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems, and various algorithms have been devised… read more here.
Keywords: matrix; predicting drug; recommendation; drug effects ... See more keywords
Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation
Sign Up to like & getrecommendations! Published in 2019 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.9b00387
Abstract: We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extract the graph… read more here.
Keywords: predicting drug; drug target; target interaction;
A heterogeneous network-based method with attentive meta-path extraction for predicting drug-target interactions
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac184
Abstract: Predicting drug-target interactions (DTIs) is crucial at many phases of drug discovery and repositioning. Many computational methods based on heterogeneous networks (HNs) have proved their potential to predict DTIs by capturing extensive biological knowledge and… read more here.
Keywords: path; meta paths; predicting drug; meta path ... See more keywords
Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks
Sign Up to like & getrecommendations! Published in 2019 at "BMC Bioinformatics"
DOI: 10.1186/s12859-019-3263-x
Abstract: Accurate identification of potential interactions between drugs and protein targets is a critical step to accelerate drug discovery. Despite many relative experimental researches have been done in the past decades, detecting drug-target interactions (DTIs) remains… read more here.
Keywords: predicting drug; drug; drug target; interactions drug ... See more keywords
Using drug exposure for predicting drug resistance – A data-driven genotypic interpretation tool
Sign Up to like & getrecommendations! Published in 2017 at "PLoS ONE"
DOI: 10.1371/journal.pone.0174992
Abstract: Antiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines… read more here.
Keywords: predicting drug; hiv; drug exposure; drug ... See more keywords
A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Pharmacology"
DOI: 10.3389/fphar.2021.814858
Abstract: Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to… read more here.
Keywords: predicting drug; drug interactions; drug drug; machine learning ... See more keywords
A Systematic Review of Polygenic Models for Predicting Drug Outcomes
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Personalized Medicine"
DOI: 10.3390/jpm12091394
Abstract: Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove… read more here.
Keywords: predicting drug; drug outcomes; systematic review; prediction ... See more keywords
Predicting Drug Resistance Using Deep Mutational Scanning
Sign Up to like & getrecommendations! Published in 2020 at "Molecules"
DOI: 10.3390/molecules25092265
Abstract: Drug resistance is a major healthcare challenge, resulting in a continuous need to develop new inhibitors. The development of these inhibitors requires an understanding of the mechanisms of resistance for a critical mass of occurrences.… read more here.
Keywords: drug resistance; predicting drug; using deep; drug ... See more keywords