In Silico Approaches to Predict Drug-Transporter Interaction Profiles: Data Mining, Model Generation, and Link to Cholestasis.
Sign Up to like & getrecommendations! Published in 2019 at "Methods in molecular biology"
DOI: 10.1007/978-1-4939-9420-5_26
Abstract: Transport proteins play a crucial role in drug distribution, disposition, and clearance by mediating cellular drug influx and efflux. Inhibition of these transporters may lead to drug-drug interactions or even drug-induced liver injury, such as… read more here.
Keywords: predict drug; approaches predict; drug; silico approaches ... See more keywords
Machine Learning Derived Quantitative Structure Property Relationship (QSPR) to Predict Drug Solubility in Binary Solvent Systems
Sign Up to like & getrecommendations! Published in 2019 at "Industrial & Engineering Chemistry Research"
DOI: 10.1021/acs.iecr.8b04584
Abstract: Prediction of drug solubility is a crucial problem in pharmaceutical industries for both drug delivery and discovery purposes. Several theoretical approaches have been proposed to predict drug solubility in mixed solvent systems when the solubility… read more here.
Keywords: drug solubility; quantitative structure; drug; predict drug ... See more keywords
FL-DTD: an integrated pipeline to predict the drug interacting targets by feedback loop-based network analysis
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac263
Abstract: Drug target discovery is an essential step to reveal the mechanism of action (MoA) underlying drug therapeutic effects and/or side effects. Most of the approaches are usually labor-intensive while unable to identify the tissue-specific interacting… read more here.
Keywords: integrated pipeline; interacting targets; predict drug; drug perturbation ... See more keywords
L2,1-GRMF: an improved graph regularized matrix factorization method to predict drug-target interactions
Sign Up to like & getrecommendations! Published in 2019 at "BMC Bioinformatics"
DOI: 10.1186/s12859-019-2768-7
Abstract: BackgroundPredicting drug-target interactions is time-consuming and expensive. It is important to present the accuracy of the calculation method. There are many algorithms to predict global interactions, some of which use drug-target networks for prediction (ie,… read more here.
Keywords: predict drug; method; drug; drug target ... See more keywords
Predict drug sensitivity of cancer cells with pathway activity inference
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DOI: 10.1186/s12920-018-0449-4
Abstract: BackgroundPredicting cellular responses to drugs has been a major challenge for personalized drug therapy regimen. Recent pharmacogenomic studies measured the sensitivities of heterogeneous cell lines to numerous drugs, and provided valuable data resources to develop… read more here.
Keywords: drug; predict drug; pathway activity; cancer cells ... See more keywords
Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2
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DOI: 10.3390/molecules27175413
Abstract: Viral resistance is a worldwide problem mitigating the effectiveness of antiviral drugs. Mutations in the drug-targeting proteins are the primary mechanism for the emergence of drug resistance. It is essential to identify the drug resistance… read more here.
Keywords: resistance mutations; predict drug; resistance; drug ... See more keywords
Fine-tuning of BERT Model to Accurately Predict Drug–Target Interactions
Sign Up to like & getrecommendations! Published in 2022 at "Pharmaceutics"
DOI: 10.3390/pharmaceutics14081710
Abstract: The identification of optimal drug candidates is very important in drug discovery. Researchers in biology and computational sciences have sought to use machine learning (ML) to efficiently predict drug–target interactions (DTIs). In recent years, according… read more here.
Keywords: target interactions; drug target; predict drug; model ... See more keywords