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Toward an AI Era: Application of Artificial Intelligence in Inclusion Complex Screening.

Naixuan Deng1, Yeqi Huang1, Yue Gao1

  • 1State Key Laboratory of Bioactive Molecules and Druggability Assessment, Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs, College of Pharmacy, Jinan University, Guangzhou 511443, China.

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Summary

Artificial intelligence (AI) accelerates the discovery of supramolecular inclusion complexes for drug delivery. AI offers efficient prediction, optimization, and design, overcoming limitations of traditional experimental methods.

Keywords:
artificial intelligencedeep learninginclusion complexesmachine learningrational designvirtual screening

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Area of Science:

  • Supramolecular Chemistry
  • Computational Chemistry
  • Drug Delivery Systems

Background:

  • Supramolecular inclusion complexes offer advantages in drug delivery, including stability and controlled release.
  • Traditional screening methods are time-consuming, costly, and inefficient.
  • Artificial intelligence (AI) presents a novel approach to overcome these limitations.

Purpose of the Study:

  • To systematically review the application of AI in screening supramolecular inclusion complexes.
  • To highlight AI's role in predicting properties and designing host molecules.
  • To identify challenges and future directions for AI in this field.

Main Methods:

  • Review of AI-driven methods for supramolecular inclusion complex screening.
  • Analysis of AI applications in property prediction and host molecule design.
  • Discussion of current challenges and future research avenues.

Main Results:

  • AI effectively predicts and optimizes key properties of inclusion complexes.
  • AI facilitates the rational design of host molecules for targeted applications.
  • Progress in AI applications for supramolecular complex screening is summarized.

Conclusions:

  • AI significantly enhances the efficiency and throughput of inclusion complex screening.
  • Addressing data scarcity and model interpretability is crucial for AI advancement.
  • Integrating AI with physicochemical principles and closed-loop platforms will drive future innovation.