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Drug Nomenclature01:17

Drug Nomenclature

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During the development of a new pharmaceutical, the manufacturer initially assigns a code name to the drug. Once approved, the drug receives a United States Adopted Name (USAN)—a generic, nonproprietary designation. Upon being listed in the United States Pharmacopeia, this nonproprietary name becomes the drug's official name. Additionally, the manufacturer assigns a proprietary name or trademark, which serves as the brand name under which the drug is marketed. It is worth noting that...
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Drug Biotransformation: Overview01:16

Drug Biotransformation: Overview

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Pharmaceutical substances known as xenobiotics are predominantly lipophilic and nonionized. This enables them to permeate lipid bilayers, such as cell membranes, and interact with intracellular target receptors. Lipophilic drugs have an advantage in crossing biological barriers and reaching their intended sites of action. However, lipophilic drugs often have a restricted capacity for renal expulsion or elimination from the body. When these drugs enter the kidneys and undergo glomerular...
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Drug Biotransformation: Overview01:28

Drug Biotransformation: Overview

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Biotransformation, also known as drug metabolism, is a vital physiological process that chemically alters drugs, facilitating their elimination from the body and terminating their action. This process involves two main phases: phase I and phase II reactions. Phase I reactions, including oxidation, reduction, and hydrolysis, introduce or unmask polar functional groups on the drug molecule, thereby increasing its water solubility. By enhancing water solubility, the drug becomes more hydrophilic...
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Drug Discovery: Overview01:26

Drug Discovery: Overview

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Drug Classes and Categories01:25

Drug Classes and Categories

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Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
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Drug Dosage Regimen: Overview01:15

Drug Dosage Regimen: Overview

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A drug dosage regimen describes the specific instructions and schedule for administering a drug to a patient. It considers factors such as drug dosage, frequency, route of administration, and duration of treatment. Designing an appropriate dosage regimen for a patient aims to achieve a target drug concentration at the site of action.
Typically, the starting dose and dosing interval are guided by the manufacturer's recommendations based on clinical trials conducted during and after drug...
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Related Experiment Video

Updated: Mar 12, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text.

Mujiono Sadikin1, Mohamad Ivan Fanany2, T Basaruddin2

  • 1Faculty of Computer Science, Universitas Mercu Buana, l. Meruya Selatan No. 1, Kembangan, Jakarta Barat 11650, Indonesia; Machine Learning and Computer Vision Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok, West Java 16424, Indonesia.

Computational Intelligence and Neuroscience
|November 16, 2016
PubMed
Summary

This study introduces novel data representation techniques for improved drug name recognition in medical texts. The proposed methods enhance entity extraction, achieving a superior F-score of 0.8645.

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Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Machine Learning

Background:

  • Drug name recognition is crucial for medical information extraction.
  • Medical text mining faces challenges like unstructured text, evolving terminology, name variations, and limited labeled data.
  • Existing approaches often yield suboptimal F-scores below 0.75.

Purpose of the Study:

  • To develop advanced data representation techniques for drug name recognition.
  • To address the limitations of current methods in handling medical text complexities.
  • To improve the accuracy and performance of drug entity extraction.

Main Methods:

  • Proposed three data representation techniques leveraging word distribution and similarity from word embeddings.
  • Evaluated techniques using standard Neural Network (NN) models: Multilayer Perceptron (MLP), Deep Belief Network (DBN), and Stacked Autoencoder (SAE).
  • Utilized a recurrent NN model, Long Short-Term Memory (LSTM), for sequence-based sentence representation.

Main Results:

  • The third technique, representing sentences as sequences evaluated with LSTM, achieved the highest performance.
  • This approach yielded an average F-score of 0.8645, surpassing state-of-the-art methods.
  • Demonstrated significant improvement in drug name entity extraction accuracy.

Conclusions:

  • The proposed sequence-based data representation technique using LSTM is highly effective for drug name recognition.
  • This method successfully overcomes several challenges inherent in medical text mining.
  • The findings offer a promising advancement for information extraction in the biomedical domain.