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Drug Discovery: Overview01:26

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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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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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Agonists are drugs that interact with specific receptors in the body to produce a biological response. When an agonist binds to a receptor, it activates or enhances the receptor's function, leading to physiological effects. The interaction between agonist drugs and receptors is crucial for their therapeutic action in various medical treatments.
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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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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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  11. Large Language Model-based Natural Language Encoding Could Be All You Need For Drug Biomedical Association Prediction

Large Language Model-Based Natural Language Encoding Could Be All You Need for Drug Biomedical Association Prediction

Hanyu Zhang1,2, Yuan Zhou3, Zhichao Zhang3

  • 1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China.

Analytical Chemistry
|July 16, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces LEDAP, a novel AI tool using large language models (LLMs) for analyzing drug associations. LEDAP enhances drug discovery by improving predictions of drug-disease, drug-drug, and drug-side effect relationships.

Area of Science:

  • Biomedicine
  • Artificial Intelligence
  • Drug Discovery

Background:

  • Analyzing drug-related interactions is crucial for drug discovery and development.
  • Existing AI tools for drug biomedical associations (DBAs) lack comprehensive feature encoding for biomedical functions and semantic concepts.
  • Large language models (LLMs) show promise due to their advanced natural language understanding.

Purpose of the Study:

  • To introduce LEDAP, a novel method leveraging LLM-based biotext feature encoding for predicting drug associations.
  • To evaluate LEDAP's performance in analyzing drug-disease associations, drug-drug interactions, and drug-side effect associations.
  • To demonstrate the potential of LLMs in advancing drug development analysis.

Main Methods:

  • Developed LEDAP, a system utilizing LLM-based feature encoding for DBA prediction.

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  • Employed LLMs for their holistic understanding of natural language and biomedical topics.
  • Integrated LLM-based feature representations with classical machine learning methods.

    • Main Results:

    • LEDAP demonstrated competitive performance compared to existing DBA analysis tools.
    • LLM-based feature representations achieved satisfactory performance across various DBA tasks, including binary classification, multiclass classification, and regression.
    • The approach showed consistent effectiveness even with simple machine learning models.

    Conclusions:

    • LLMs possess considerable potential for drug development research.
    • LEDAP's approach offers a significant advancement in analyzing drug biomedical associations.
    • The findings suggest LLMs can act as a catalyst for future progress in drug discovery and development.