AI-Enabled Discovery of Novel Biomaterials for Healthcare and Sustainable Engineering Applications

Nahrizul Adib Kadri

AI-Enabled Discovery of Novel Biomaterials for Healthcare and Sustainable Engineering Applications

Author(s)	Dr. Nahrizul Adib Kadri
Country	United States
Abstract	The discovery of biomaterials for healthcare and sustainable engineering traditionally depends on iterative synthesis, characterization, biological evaluation, mechanical testing, and application-specific optimization. This process is scientifically rigorous but can be slow, expensive, resource-intensive, and poorly suited to exploring the extensive design spaces created by polymers, peptides, proteins, polysaccharides, ceramics, composites, nanostructures, and biologically derived feedstocks. Artificial intelligence provides new capabilities for predicting structure–property relationships, identifying candidate compositions, extracting evidence from scientific literature, prioritizing experiments, and conducting multi-objective optimization. Nevertheless, predictive accuracy alone cannot establish whether a material is biocompatible, clinically useful, environmentally sustainable, manufacturable, stable during sterilization, or safe across its lifecycle. This conceptual and simulation-based study develops an AI-enabled biomaterial-discovery framework integrating curated materials data, molecular and structural representations, property-prediction models, generative design, uncertainty estimation, active learning, automated experimentation, biological validation, and lifecycle assessment. The framework addresses healthcare applications such as tissue scaffolds, wound-care materials, implant coatings, drug-delivery systems, and bioresorbable devices, together with sustainable engineering applications including biodegradable packaging, filtration membranes, renewable composites, protective coatings, and low-impact structural materials. Six hypothetical biomaterial families are evaluated through an illustrative multi-criteria analysis covering biocompatibility, biodegradability, mechanical suitability, manufacturability, renewable feedstock use, and sterilization stability. The simulated scores do not represent laboratory observations or clinical evidence. The analysis demonstrates that no candidate dominates every criterion. Healthcare-oriented materials may achieve strong biological performance while presenting manufacturing or sterilization limitations, whereas engineering-oriented materials may offer mechanical and production advantages but require additional biological evaluation. The paper concludes that AI can accelerate biomaterial discovery most responsibly when it is used to select informative experiments, quantify uncertainty, preserve negative results, incorporate lifecycle constraints early, and maintain human scientific oversight.
Keywords	Artificial intelligence; biomaterial discovery; machine learning; sustainable materials; healthcare engineering; active learning; biodegradable polymers; multi-objective optimization.
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-01

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

AI-Enabled Discovery of Novel Biomaterials for Healthcare and Sustainable Engineering Applications

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