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Pharmaceutical Preparations

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Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data.

Journal of integrative bioinformatics
We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables th...

Finding medication doses in the liteature.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Medication doses, one of the determining factors in medication safety and effectiveness, are present in the literature, but only in free-text form. We set out to determine if the systems developed for extracting drug prescription information from cli...

Smart systems for determination of drug's solubility.

Drug development and industrial pharmacy
The solubility of drugs is a crucial physicochemical property in the drug discovery or development process and for improving the bioavailability of drugs. There are various methods for evaluating the solubility of drugs including manual measurement m...

DrugMetab: An Integrated Machine Learning and Lexicon Mapping Named Entity Recognition Method for Drug Metabolite.

CPT: pharmacometrics & systems pharmacology
Drug metabolites (DMs) are critical in pharmacology research areas, such as drug metabolism pathways and drug-drug interactions. However, there is no terminology dictionary containing comprehensive drug metabolite names, and there is no named entity ...

Methodological variations in lagged regression for detecting physiologic drug effects in EHR data.

Journal of biomedical informatics
We studied how lagged linear regression can be used to detect the physiologic effects of drugs from data in the electronic health record (EHR). We systematically examined the effect of methodological variations ((i) time series construction, (ii) tem...

In Silico Prediction of Major Clearance Pathways of Drugs among 9 Routes with Two-Step Support Vector Machines.

Pharmaceutical research
PURPOSE: The clearance pathways of drugs are critical elements for understanding the pharmacokinetics of drugs. We previously developed in silico systems to predict the five clearance pathway using a rectangular method and a support vector machine (S...

Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

Journal of chemical information and modeling
The majority of computational methods for predicting toxicity of chemicals are typically based on "nonmechanistic" cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically ...

Prediction of bioconcentration factors in fish and invertebrates using machine learning.

The Science of the total environment
The application of machine learning has recently gained interest from ecotoxicological fields for its ability to model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However, comparison of different mode...

End-to-End Representation Learning for Chemical-Chemical Interaction Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Chemical-chemical interaction (CCI) plays a major role in predicting candidate drugs, toxicities, therapeutic effects, and biological functions. CCI is typically inferred from a variety of information; however, CCI has yet not been predicted using a ...