Artificial Intelligence in Biomaterials Design and Development.

Format:

Book

Author/Creator:

Yazdi, Mohsen Khodadadi.

Series:

Woodhead Publishing Series in Biomaterials Series

Language:

English

Subjects (All):

Artificial intelligence.

Biomimetics.

Physical Description:

1 online resource (698 pages)

Edition:

1st ed.

Place of Publication:

Chantilly : Elsevier Science & Technology, 2025.

Summary:

Artificial Intelligence in Biomaterials Design and Development delves into the transformative role of artificial intelligence, particularly machine learning, in creating new biomaterials.

Contents:

Front Cover

Artificial Intelligence in Biomaterials Design and Development

Copyright Page

Contents

List of contributors

Editors' Note

Preface

Acknowledgment

1 The critical role of artificial intelligence in biomaterials design and development

1.1 Introduction

1.2 Foundations of artificial intelligence and machine learning in biomaterials research

1.3 Current artificial intelligence integration

1.4 How to integrate artificial intelligence?

1.5 Artificial intelligence application in biomaterial design

1.6 Concluding remarks and future direction

References

2 Datasets and tools in materials science

2.1 Introduction

2.2 Importance of datasets in materials science

2.2.1 The significance of datasets in materials science

2.2.2 Types of datasets commonly used (experimental, theoretical, and computational)

2.3 Experimental datasets

2.3.1 Experimental techniques for data generation

2.4 Theoretical datasets

2.4.1 Theoretical modeling and simulations in materials science

2.5 Computational datasets

2.5.1 Computational methods and algorithms in materials science

2.6 Modeling and simulation tools

2.6.1 Software and platforms for theoretical modeling and simulations

2.7 Data analysis tools

2.8 Challenges and opportunities in dataset development

2.8.1 Potential of artificial intelligence in biomaterials

2.8.2 Aartificial intelligence-driven discovery and design of bio-materials

2.8.3 Enhancing biomaterials through artificial intelligence-driven simulation and modeling

2.8.4 Smart biomaterials and artificial intelligence-driven customization

2.8.5 Artificial intelligence in biomaterials manufacturing and scale-up

2.8.6 Ethical and regulatory considerations

2.8.7 The future of artificial intelligence in biomaterials research.

2.8.8 Predictions for the future of datasets and tools in materials science

2.9 Conclusion

3 Intelligent tools for biomaterials design

3.1 Introduction

3.2 Artificial Intelligence in healthcare

3.3 Biomaterials design

3.4 Intelligent tools

3.5 Intelligent tools in biomaterials and tissue engineering

3.5.1 Role of intelligent tools in zirconia biomaterials

3.5.2 Role of intelligent tools in tissue engineering

3.5.3 Intelligent tools in biomolecular and microbial design

3.5.4 Intelligent tools in hydrogel design

3.6 Limitations and future perspectives

3.7 Conclusion

4 Artificial neural networks for biomaterials development

4.1 Introduction

4.2 Classical biomaterials and complexity of biological systems

4.3 Strong and weak artificial intelligence

4.4 Artificial neural network basics

4.4.1 Convolutional neural networks

4.4.2 Recurrent neural networks

4.4.3 Spiking neural networks

4.5 Artificial neural network regression models

4.6 Applications of artificial neural networks in medicine and biomaterials development

4.6.1 Polymers

4.6.2 Metals

4.6.3 Ceramics

4.7 Conclusion

5 From human genome to materials genome

5.1 Introduction

5.2 High-throughput experiments and computational design

5.3 Big data technologies and artificial intelligence in materials research

5.4 Current advancements in the biomaterial genome

5.4.1 Applications of polymeric biomaterials

5.4.2 Applications of metallic biomaterials

5.4.3 Ceramic biomaterial applications

5.4.4 Nanoengineered biomaterials applications

5.4.5 Applications of 3D printed biomaterials

5.5 Conclusion

6 Artificial intelligence-assisted biomaterials synthesis

6.1 Introduction.

6.2 Overview of biomaterials synthesis and its challenges

6.3 Importance of artificial intelligence in advancing biomaterials synthesis

6.4 Role of artificial intelligence in predicting optimal synthesis conditions

6.5 Benefits and limitations of artificial intelligence-assisted synthesis prediction

6.6 Case studies showcasing successful artificial intelligence-assisted biomaterials synthesis prediction

6.7 Future prospects and potential advancements in the field

6.8 Conclusion

7 Artificial intelligence-based biomaterials characterization

7.1 Introduction

7.2 Artificial intelligence in biomaterials science

7.2.1 Machine learning

7.2.2 Artificial neural network

7.2.3 Deep learning

7.2.4 Random forest

7.2.5 Support vector machines

7.2.6 Generative approach

7.3 Biomaterials and characterization

7.4 Artificial intelligence in biomaterials characterization

7.4.1 Metallic biomaterials

7.4.2 Ceramic biomaterials

7.4.3 Polymeric biomaterials

7.4.4 Composite biomaterials

7.5 Artificial intelligence in biomedical applications

7.6 Limitations and future directions

7.7 Conclusion

8 Artificial intelligence in drug delivery system development

8.1 Introduction

8.2 Drug delivery systems

8.2.1 Nano-based drug delivery systems

8.2.1.1 Organic nanocarriers

Liposomes

Solid lipid nanoparticles and nanostructured lipid carriers

Hydrogel

Protein nanoparticles

Polymeric nanoparticles

Polymeric micelles

Dendrimers

8.2.1.2 Inorganic nanoparticles

Quantum dots

Carbon nanomaterials

Silica nanoparticles

Metal and metal-oxide nanoparticles

Nanocrystals

Metal-organic frameworks

8.3 Artificial intelligence in drug delivery systems.

8.4 Artificial intelligence in drug delivery system design and fabrication

8.5 Artificial intelligence in the prediction of drug delivery system characteristics

8.5.1 Size

8.5.2 Morphology

8.5.3 Toxicity

8.5.4 Loading capacity

8.5.5 Cellular uptake

8.5.6 Drug release

8.6 Artificial intelligence-assisted drug delivery for effective diagnosis

8.7 Artificial intelligence-assisted drug delivery for effective treatment

8.8 Conclusion and future perspective

Abbreviations

9 Artificial intelligence in vaccine development

Acronyms

9.1 Introduction

9.1.1 Vaccines - a brief Introduction

9.1.2 Artificial intelligence in vaccine development

9.1.3 Biomaterials in vaccine development and delivery

9.2 Advancements in vaccine development through artificial intelligence

9.2.1 Target identification

9.2.2 Predicting Antibody Response and Immune Profiles

9.2.3 Designing vaccine formulations and adjuvants

9.2.4 Predicting immune evasion and variant resilience

9.2.5 Personalized medicine

9.2.6 Improving vaccine manufacturing and delivery

9.2.7 Predicting safety and adverse effects

9.3 Systems vaccinology

9.4 Vaccinomics: the new "omics" and artificial intelligence

9.5 Advancements in biomaterials aided by artificial intelligence for vaccine development

9.6 Future scope

Acknowledgments

Funding information

Further reading

10 Artificial intelligence in biopolymer design and engineering

10.1 Introduction

10.2 The role and applications of artificial intelligence in advancing biopolymer design and usage

10.3 Drug delivery systems

10.4 Tissue engineering

10.5 Bioprinting

10.6 Biopolymeric sensors

10.7 Future perspective and challenges

10.8 Conclusion

References.

11 Artificial intelligence in protein science and engineering

11.1 Introduction

11.1.1 Algorithms helping to model protein structures

11.1.2 Sectorial applications of artificial intelligence predictions of protein structures

11.2 Artificial intelligence in protein designing

11.3 Artificial intelligence to detect language of the molecules of life

11.4 Artificial intelligence predictions of the sites of drug targets in protein structures

11.5 Artificial intelligence predictions of drug molecules to target mutated sites in protein structures

11.6 Artificial intelligence enhanced engineering of proteins

11.7 Concluding remarks

12 Artificial intelligence-assisted biomaterials for hard tissue implants

12.1 Introduction

12.2 Biomaterial design and optimization

12.2.1 Biomaterial design

12.2.1.1 Data-driven design

12.2.2 Optimization

12.2.2.1 Classification of optimization methods

12.2.2.2 Metaheuristic optimization

12.2.2.3 Genetic algorithm

12.3 Formulation of the objective function

12.3.1 Artificial neural network

12.3.2 Neuro-Fuzzy interface system

12.4 Case studies

12.4.1 Titanium alloys for hard tissue implants

12.4.2 Designing ultra-high molecular weight polyethylene hybrid composites using machine learning and metaheuristic algorithms

12.4.3 Design of patient-specific dental implant using finite element analysis and computational intelligence techniques

12.5 Conclusions

13 Biomaterials education through artificial intelligence-enabled product-based learning

13.1 Introduction

13.1.1 The evolution and significance of biorefineries within the chemical industry

13.1.2 Artificial intelligence and education

13.1.2.1 Historical context of artificial intelligence related to education.

13.1.2.2 Educational theories.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

0-323-95465-0

9780323954655

OCLC:

1558293567