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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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G Protein–Coupled Receptors (GPCRs) are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to various stimuli. GPCRs regulate critical physiological pathways and are excellent drug targets for treating diseases such as diabetes, cancer, obesity, depression, or Alzheimer's. Nearly 35% of approved drugs implement their therapeutic effects by selectively interacting with specific GPCRs.
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Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.

Sebastian Raschka1, Benjamin Kaufman2

  • 1University of Wisconsin-Madison, Department of Statistics, United States.

Methods (San Diego, Calif.)
|July 10, 2020
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Summary

Artificial intelligence (AI) and deep learning are revolutionizing drug discovery, particularly for G protein-coupled receptor (GPCR) bioactive ligands. This review highlights recent AI advancements and future trends in identifying novel GPCR ligands.

Keywords:
Deep learningDrug discoveryGPCR ligandsGraph convolutional neural networksMachine learningMolecular representations

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Area of Science:

  • Computational chemistry
  • Pharmacology
  • Artificial intelligence

Background:

  • Deep learning has significantly advanced machine learning performance in academic research and industry.
  • Deep learning excels in processing non-tabular data like images and text, outperforming traditional methods.
  • G protein-coupled receptors (GPCRs) are crucial drug targets, making GPCR bioactive ligand discovery a key area of research.

Purpose of the Study:

  • To review AI-based research for GPCR bioactive ligand discovery.
  • To focus on recent achievements and emerging trends in the field.
  • To provide accessible explanations of AI methodologies for a broad computational science audience.

Main Methods:

  • Summarizing state-of-the-art deep learning architectures and molecular feature representations.
  • Highlighting successful AI-driven GPCR bioactive ligand discovery studies.
  • Discussing general machine learning technologies applicable to ligand discovery.

Main Results:

  • AI, particularly deep learning, has demonstrated superior performance in various scientific domains.
  • Recent AI research has successfully identified novel GPCR bioactive ligands.
  • Machine learning technologies show promise for future ligand discovery.

Conclusions:

  • Deep learning is a powerful tool for GPCR bioactive ligand discovery.
  • Emerging trends like active learning and semi-supervised learning hold significant potential.
  • AI integration is poised to accelerate the discovery of new therapeutics.