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A Novel Perspective on Using Artificial Intelligence and Nanoinformatics to Develop Nanomedicines.

Nandita Tyagi1, Sneha Singh1, Seema Dagar2

  • 1Guru Jambheshwar University of Science and Technology, a University in Hisar, Haryana.

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|January 22, 2026
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Summary

Artificial intelligence (AI) and machine learning (ML) accelerate nanomedicine development by optimizing physicochemical properties. Nanoinformatics and physiologically based pharmacokinetic (PBPK) models enhance drug design, predict distribution, and assess toxicity for safer, effective therapies.

Keywords:
Artificial intelligenceComputational approachesMachine learningModelsNano deliveriesNanoinformatics.

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Area of Science:

  • Biomedical Engineering
  • Computational Chemistry
  • Drug Discovery

Background:

  • Drug development is costly and time-consuming.
  • Artificial intelligence (AI) and machine learning (ML) offer solutions for designing translational nanomedicines.
  • Nanomedicine efficacy depends on physicochemical properties like size, shape, and charge.

Purpose of the Study:

  • To review the implementation of nanoinformatics and AI in translating nanomedicines.
  • To highlight computational strategies for nanodelivery system design.
  • To examine AI/ML contributions, challenges, and future directions in nanomedicine development.

Main Methods:

  • Quantitative structure-activity/property relationship (QSAR/QSPR) models for property optimization.
  • Physiologically based pharmacokinetic (PBPK) models for predicting biodistribution and toxicity.
  • AI/ML tools for designing nanodelivery systems, assessing nanotoxicity, and developing simulation models.

Main Results:

  • Nanoinformatics enables systematic optimization of nanomedicine properties for enhanced functionality.
  • PBPK models predict drug and nanomedicine distribution, aiding toxicity assessment.
  • AI/ML facilitates the selection of nanomaterials and the development of in vitro/in vivo simulation models.

Conclusions:

  • AI and nanoinformatics significantly accelerate nanomedicine translation from bench to clinic.
  • Computational approaches minimize health and environmental risks during nanomedicine development.
  • Methodologies discussed have broad applicability across multiple scientific disciplines, including drug discovery and nanotechnology.