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International Journal of Bioprinting AI for sustainable bioprinting

enhanced toughness and recyclability under extreme bioprinting. 14,72,73 Furthermore, AI optimized printing
conditions. Ji et al. introduced a salting-out-based parameters in real-time, improving construct fidelity,
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method using poly(N-isopropylacrylamide) hydrogels, reducing waste, and advancing efficiency. 19,74–76 Lastly,
enabling reversible solidification and dissolution without AI-assisted intelligent bioprinting technologies enabled
chemical crosslinkers or post-processing. Collectively, powerful bioprinting capabilities with improved
these approaches maintain mechanical performance sustainability. Together, these innovations demonstrate
while promoting circular material use, offering promising AI’s pivotal role in driving sustainable bioprinting forward,
pathways for integrating sustainability into future aligning with both ecological and functional demands. AI
bioprinting workflows. applications are categorized not only by technical functions
but also by their potential impact on sustainability.
3. Artificial intelligence for
sustainable bioprinting 3.1. Material discovery and development
AI has revolutionized the incorporation of bioactive
Despite advances in sustainable hydrogels for molecules into hydrogels, significantly enhancing their
bioprinting, challenges remain in balancing printability, functionality for bioprinting applications. Tools like
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biocompatibility, biodegradability, and mechanical AlphaFold, a deep learning model for protein structure
strength. 18,55,56 Hydrogels must exhibit optimal rheological prediction, enable precise identification of molecular
properties for smooth deposition while maintaining interactions, facilitating the integration of bioactive
structural integrity. However, materials often excel in components such as growth factors, peptides, and
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one attribute at the expense of another—for example, enzymes into hydrogel matrices for functionalization.
natural hydrogels offer high biocompatibility but lack the AI-driven methods allow the design of hydrogels with
mechanical robustness needed for complex structures. tailored properties.
Beyond material selection, ensuring efficient, reproducible
bioprinting is equally complex. Traditional trial-and- One notable example of AI’s potential in modeling
error methods lead to resource inefficiencies, waste, and molecular interactions is AlphaFold, which has redefined
prolonged development cycles. Variability in bioink protein structure prediction. AlphaFold, developed in
properties, processing parameters, and post-printing three successive versions (AlphaFold 1, 2, and 3), has
conditions further impacts cell viability, tissue functionality, revolutionized protein structure prediction. AlphaFold
and print success, undermining sustainability. 1 introduced neural networks to predict inter-residue
distances, demonstrating significant advancements in
AI offers a transformative solution by enhancing protein modeling by leveraging evolutionary data and
material discovery, process optimization, and print achieving superior accuracy during the Critical Assessment
quality. Coined by John McCarthy, the term AI refers to of Structure Prediction 13. It is used on multiple sequence
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the intelligence demonstrated by machines. Early rule- alignments (MSAs) and gradient descent algorithms to
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based AI relied on predefined “if-then” rules, facilitating model protein structures, achieving notable success in
data analysis and automation. 59,60 Classical AI introduced free modeling domains, particularly for proteins lacking
knowledge-based systems that integrated expert reasoning homologous templates. AlphaFold 2 advanced this by
for problem-solving. 61,62 The emergence of machine incorporating Evoformer blocks to integrate spatial and
learning (ML) revolutionized AI, enabling systems to evolutionary relationships, enabling atomic-level accuracy
learn from large datasets, recognize complex patterns, and in 3D structural predictions, as shown in the Critical
predict material and print outcomes beyond the limitations Assessment of Structure Prediction 14. It achieved near-
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of predefined rules. 63–66 experimental accuracy for most targets, effectively modeling
By combining the structured, efficient automation of long-range residue interactions and folding pathways, but
classical AI with the adaptability and predictive capabilities remained computationally intensive due to its reliance
of ML, AI is currently transforming sustainable bioprinting on MSAs. AlphaFold 3 further enhanced capabilities
across multiple dimensions. 67–71 In hydrogel design, with a diffusion-based architecture, replacing Evoformer
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AI accelerated the discovery of materials that balance with Pairformer (Figure 3). This eliminates MSA
functionality, printability, and sustainability by predicting dependency while enabling accurate modeling of protein
their behavior under diverse conditions and minimizing complexes, including protein–ligand and protein–nucleic
the resource-intensive nature of traditional development. acid interactions. Its experimental results demonstrated
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AI-driven approaches also enhanced material screening superior performance in predicting multi-component
processes, ensuring bioinks meet the specific mechanical, systems, with applications extending to chemically
biological, and environmental requirements for modified residues and novel biomolecular assemblies.

Volume 11 Issue 4 (2025) 137 doi: 10.36922/IJB025170164