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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Biotechnology

Background:

  • Natural polymers offer biocompatibility and sustainability for drug delivery.
  • Inconsistent performance and scalability hinder natural polymer formulations.
  • Artificial intelligence (AI) and machine learning (ML) are emerging solutions.

Purpose of the Study:

  • To review AI-assisted modeling and optimization of natural polymer drug delivery systems.
  • To highlight AI's role in overcoming formulation challenges.
  • To discuss pathways toward data-driven, sustainable pharmaceutics.

Main Methods:

  • Synthesis of current advances in AI/ML for natural polymer formulation.
  • Analysis of predictive algorithms (neural networks, Bayesian optimization).
  • Discussion of hybrid mechanistic-AI and physics-informed neural networks.
  • Review of data-generation workflows, FAIR standards, and polymer informatics databases.

Main Results:

  • AI models accurately predict encapsulation efficiency and dissolution profiles.
  • Hybrid AI approaches improve model interpretability by integrating physical laws.
  • Data infrastructure (workflows, standards, databases) enables reproducible modeling.

Conclusions:

  • AI integration offers a pathway to data-driven, sustainable pharmaceutics.
  • Predictive and eco-designed formulations can replace empirical methods.
  • Addressing challenges in standardization, transparency, and validation is crucial for translation.