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Drug-Receptor Interactions

Drug-Receptor Interactions

Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue.

Drug-Receptor Interaction: Antagonist

Drug-Receptor Interaction: Antagonist

An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

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Published on: May 27, 2021

Using a shallow linguistic kernel for drug-drug interaction extraction.

Isabel Segura-Bedmar¹, Paloma Martínez, Cesar de Pablo-Sánchez

¹Computer Science Department, Carlos III University of Madrid, Leganés, Spain. isegura@inf.uc3m.es

Journal of Biomedical Informatics

|May 7, 2011

Summary

This study introduces a machine learning approach for extracting drug-drug interactions (DDIs) from biomedical texts. The proposed shallow linguistic kernel method outperforms previous approaches and introduces the first DDI corpus for research.

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Area of Science:

Biomedical Informatics
Natural Language Processing
Pharmacology

Background:

Drug-drug interactions (DDIs) are critical in healthcare, necessitating efficient literature review methods.
Current information extraction (IE) techniques lack a dedicated approach for DDI extraction from biomedical texts.

Purpose of the Study:

To evaluate a machine learning-based method for automated DDI extraction.
To compare the performance of a shallow linguistic kernel against a pattern-based approach for DDI identification.
To introduce the first benchmark corpus for DDI extraction research.

Main Methods:

Developed a supervised machine learning model using a shallow linguistic kernel.
Created and annotated the DrugDDI corpus, containing 3169 DDIs.
Conducted experiments to optimize the shallow linguistic kernel's configuration parameters.

Main Results:

The shallow linguistic kernel achieved a precision of 51.03%, recall of 72.82%, and F-measure of 60.01% on the DrugDDI corpus.
The machine learning approach demonstrated superior performance compared to the previously proposed pattern-based method.
The DrugDDI corpus provides a valuable resource for future DDI extraction research.

Conclusions:

The shallow linguistic kernel is an effective method for automated DDI extraction from biomedical literature.
This study presents the first comprehensive solution for DDI extraction, supported by a novel annotated corpus.
The developed corpus is expected to foster further advancements in DDI extraction methodologies.