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Pharmaceutical Preparations

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Predicting Drug-Target Interactions With Multi-Information Fusion.

IEEE journal of biomedical and health informatics
Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most ...

Characterization of the Context of Drug Concepts in Research Protocols: An Empiric Study to Guide Ontology Development.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We examined a large body of research study documents (protocols) to identify mentions of drug concepts and established base concepts and roles needed to characterize the semantics of these instances. We found these concepts in three general situation...

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

Pharmaceutical research
PURPOSE: Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of m...

Computational probing protein-protein interactions targeting small molecules.

Bioinformatics (Oxford, England)
MOTIVATION: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug target...

Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME.

Database : the journal of biological databases and curation
Today's large, public databases of protein-small molecule interaction data are creating important new opportunities for data mining and integration. At the same time, new graphical user interface-based workflow tools offer facile alternatives to cust...

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions.

Journal of biomedical informatics
The advances achieved in Natural Language Processing make it possible to automatically mine information from electronically created documents. Many Natural Language Processing methods that extract information from texts make use of annotated corpora,...

Boosting drug named entity recognition using an aggregate classifier.

Artificial intelligence in medicine
OBJECTIVE: Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost al...

Prediction of drug-induced eosinophilia adverse effect by using SVM and naïve Bayesian approaches.

Medical & biological engineering & computing
Drug-induced eosinophilia is a potentially life-threatening adverse effect; clinical manifestations, eosinophilia-myalgia syndrome, mainly include severe skin eruption, fever, hematologic abnormalities, and organ system dysfunction. Using experimenta...

Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools.

Advanced drug delivery reviews
In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanis...

Greedy and Linear Ensembles of Machine Learning Methods Outperform Single Approaches for QSPR Regression Problems.

Molecular informatics
The application of Machine Learning to cheminformatics is a large and active field of research, but there exist few papers which discuss whether ensembles of different Machine Learning methods can improve upon the performance of their component metho...