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Spasmolytic Agents: Chemical Classification01:29

Spasmolytic Agents: Chemical Classification

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Spasmolytic agents are drugs used to alleviate muscle spasms and spasticity. They can be categorized into different chemical groups based on their mechanisms of action. Centrally acting spasmolytics primarily affect the spinal cord, while others directly target skeletal muscle cells.
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Retrieval01:12

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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
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Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
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Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Related Experiment Video

Updated: Jan 31, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Retrieval-Augmented Language Models Enable Scalable Chemical Source Classification in Metabolomics Workflows.

Prajit Rajkumar1,2, Runbang Tang1,2, Harshada Sapre1,2

  • 1Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, California 92093, United States.

Analytical Chemistry
|January 29, 2026
PubMed
Summary
This summary is machine-generated.

Automating chemical classification for exposomics and metabolomics is crucial. The new chemsource tool uses large language models (LLMs) and retrieval-augmented generation (RAG) to classify chemicals by exposure, improving data interpretation.

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Area of Science:

  • Environmental chemistry
  • Computational chemistry
  • Bioinformatics

Background:

  • Interpreting exposomics and metabolomics data requires scalable chemical classification.
  • Current functional and exposure-based chemical labeling is manual and labor-intensive.
  • Automating chemical classification is essential for advancing these fields.

Purpose of the Study:

  • To develop and present chemsource, a framework for automated chemical classification.
  • To integrate large language models (LLMs) with retrieval-augmented generation (RAG) for this task.
  • To enable user-defined classification categories for diverse applications.

Main Methods:

  • chemsource retrieves chemical descriptions from Wikipedia or PubMed abstracts.
  • It then prompts LLMs to assign user-defined categories based on retrieved content.
  • Classification was demonstrated across five exposure categories: endogenous metabolites, food molecules, drugs, personal care products, and industrial chemicals.

Main Results:

  • Benchmarking against 4,953 manually curated labels showed 75% overall agreement.
  • Category-level recall exceeded 75% across all tested classes.
  • Application to eight metabolomics datasets revealed distinct exposure patterns in various biospecimens and environmental samples.

Conclusions:

  • chemsource offers a flexible and customizable solution for automated chemical classification.
  • The framework demonstrates utility in metabolomics workflows and identifying exposure patterns.
  • It is available as a Python package, enabling diverse classification tasks without coding expertise.