Artificial Intelligence in Biomaterials Design and Development
- 1st Edition - August 1, 2025
- Imprint: Woodhead Publishing
- Editors: Mohsen Khodadadi Yazdi, Payam Zarrintaj, Mohammad Reza Saeb, Masoud Mozafari, Sidi A. Bencherif
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 5 4 6 4 - 8
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 5 4 6 5 - 5
Artificial Intelligence in Biomaterials Design and Development delves into the transformative role of artificial intelligence, particularly machine learning, in creating new bioma… Read more
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Request a sales quoteArtificial Intelligence in Biomaterials Design and Development delves into the transformative role of artificial intelligence, particularly machine learning, in creating new biomaterials. Traditional challenges in this field, such as chemical waste, spatial constraints, and inadequate tools, have hindered the swift design and synthesis of versatile biomaterials. Machine learning methods address these barriers by enhancing discovery and development processes, reducing time, costs, and wastage. Generative models now enable the creation of novel molecular structures with desired properties, making inverse materials design a reality. This book is essential for those in materials science, machine learning, and biomedical engineering.
Additionally, this comprehensive resource explores the application of AI in various aspects of biomaterials science, from computational engineering to data science. The book provides insights into how novel machine learning models can expedite materials discovery and improve accuracy. It is an invaluable guide for academics and industry professionals alike, seeking to leverage AI for innovative biomaterials research and development.
- Introduces the reader to core concepts in AI and machine learning in the context of biomaterials, as well as providing practical examples to aid understanding
- Thoroughly reviews the role of AI and machine learning in the synthesis, characterization, and applications of novel biomaterials
- Delivers in-depth coverage of discriminative and generative models for properties prediction and de novo materials design/discovery
Researchers and postgraduate students in the fields of materials science, computational engineering and biology, and biomedical engineering. Researchers and academics working in the fields of machine learning, artificial intelligence and data science, with an interest in biomaterials design and development
1: Introduction to artificial intelligence, machine learning, and deep learning
- AI, ML, and DL: clarifying the concepts, definitions, and applicability
- Machine learning: basics
- Classification: supervised and unsupervised VS. Discrete and continuous
- Active and passive learning
- Discriminative VS. Generative models
- Classification, regression, clustering
- A brief introduction to SVM, k-mean clustering, regression and logistic regression, random forest, etc.
- Optimization (backpropagation): Gradient descent and Newton methods
- Multi-objective optimization
- Multi-fidelity optimization
- Bayesian optimization
- Dynamic programming
- Markov decision process
- Dimensionality reduction
- Multi-information source optimization
2: Useful tools and datasets for materials science and engineering
- Datasets: ZINC, ChEMBL, MOSES, AFLOW, Reaxys, SciFinder, OQMD, ICSD, NOMAD, etc.
- Software packages: Anaconda, Jupyter notebook, TensorFlow, scikit learn, RDkit, OpenChem, Chematica, Pytorch, Keras, matminer (data mining), Pymatgen, biopython JARVIS, etc.
- cloud platforms/computing: FloydHub, google cloud
3: Artificial neural networks
- Human brain inspiration
- Basic concepts: composite functions, matrix operation, backpropagation
- Recurrent neural networks (RNN)
- Long short-term memory (LSTM)
- Convolutional neural networks (CNNs)
- Graph based deep learning (graph CNN and message passing neural network)
- Graph CNNs, Weisfeiler-Lehman network
- Variational Autoencoders (VAEs)
- Generative adversarial network (GAN) and its derivatives
- multiple GANs
- Reinforcement learning (RL) and deep RL, Q-learning
- Ensemble learning
4: From human genome to materials genome
- The analogy between human and materials genome
- Chemical space, drug-like small molecules
- combinatorial chemistry
- Materials genome initiative (MGI), MI2I, NOMAD
- Genetic algorithm and evolutionary approaches for materials discovery/optimization
- Materials genotype and phenotype, mutation (inspiration from nature)
- Molecular representation: SMILE, InChI, graphs, 3D
- Simplex representation of molecular structure (SiRMS)
- Latent space and continuous representation of molecules (molecular mapping)
- Polymer genome
5: Biomaterials properties-prediction based on discriminative models
- Materials properties datasets: experimentation VS. computational quantum mechanical modelling (e.g., DFT)
- Materials descriptors and fingerprints
- Properties prediction
- Quantitative structure-activity or structure-property relationship (QSAR or QSPR)
- Training on small and big datasets
- Multi-objective learning
6: de novo materials design based on generative models
- De novo design and AI imagination
- VAE in new materials discovery
- GAN and derivatives in new materials discovery and chemical entities
- Multiple-GAN and RL in materials discovery
- Molecular structures: proposing, scoring, and optimization
- Synthesizability of molecular structures from available building blocks
7: AI-assisted synthesis planning and optimization of biomaterials
- computer-assisted synthetic planning (CASP) and computer-aided retrosynthesis
- Deep Reinforcement Learning
- Monte Carlo tree search (MCTS)
- symbolic artificial intelligence (Symbolic AI)
- Reaction condition recommendation (solvent, temperature, …)
- Prediction of reaction mechanism and reaction products
- Multiple target optimization
- Reaction products prediction (template, template-free approaches)
- chemical reactivity prediction
- Synthetic feasibility estimation
8: AI-assisted characterization of biomaterials
- In situ characterization
- AI in spectroscopic analysis (NMR, IR, XRD, …)
- AI in thermal analysis (TGA, DSC)
- AI in electrochemical analysis
9: AI-assisted evaluation of biomaterials
- Cytotoxicity and biocompatibility assessment
- Biodegradation evaluations
- Bioactivity and bioavailability
- Cell-biomaterials interactions
- Water absorption and retention
- Mechanical properties
10: AI and biomaterials in drug and vaccine development
- Traditional drug discovery
- Molecular docking
- High throughput virtual screening
- ML algorithms for drug design/discovery
- Datasets for drug design/discovery
- ML algorithms vaccine development
- ML-assisted personalized medicine (the intersection between human genome and materials genome)
- AI –based autonomous researchers/agents
11: AI and biomaterials in protein engineering
- Conformational space of protein folding (alphafold)
- Protein structure prediction
- Protein descriptors
- ML algorithms for protein engineering
- Datasets for protein engineering
12: AI in biopolymer design/discovery/engineering
- Polymer descriptors
- ML algorithms for biopolymer engineering
- Datasets for polymer design/discovery
13: AI in designing/discovery of other biomaterials
- Introduction to medicinal chemistry
- Human genome-materials genome confluence
- Bioactivities prediction
- Antimicrobial peptides
- Anticancer biomaterials
- Biomarkers development
- Immunomodulatory biomaterials
- Cell instructive biomaterials
- Crystalline biomaterials
- Electroactive biomaterials
- Stimuli-responsive biomaterials
14: AI-assisted biomaterials structures at scales
- Molecular structures prediction using AI/machine learning
- Supramolecular structure prediction using AI/machine learning
- Macroscale structure prediction using AI/machine learning
15: AI-assisted materials/scientific discoveries: Beyond pure machine learning
- Search, reasoning, and knowledge representation
- Probabilistic reasoning
- Planning
- Active learning and autonomous experiments
- Autonomous Molecular Design
- Reaction Discovery
- Autonomous researchers and robotic chemists (Bayesian experimental autonomous researcher)
- Flow chemistry, microfluidic systems, microreactors
- Human machine collaborations in materials/scientific discoveries
16: State-of-the-art and future perspectives on ML-assisted biomaterials design/discovery
- Edition: 1
- Published: August 1, 2025
- Imprint: Woodhead Publishing
- No. of pages: 500
- Language: English
- Paperback ISBN: 9780323954648
- eBook ISBN: 9780323954655
MY
Mohsen Khodadadi Yazdi
Mohsen Khodadadi Yazdi is an experienced chemical/polymer engineer who received his BSc degree (chemical engineering) from the Ferdowsi University of Mashhad, Iran, followed by a MSc (2010) from the Sharif University of Technology, Tehran, Iran. He received his PhD (2017) in polymer engineering from the University of Tehran, Iran. He is currently a Senior Researcher at the Center of Excellence in Electrochemistry, University of Tehran, Iran. His research interests include smart hydrogel/biopolymers, carbon-based nanomaterial, conducting polymers, and machine learning for utilization in biomedical applications. He has contributed to >20 scientific papers and book chapters.
Affiliations and expertise
Senior Researcher, Centre of Excellence in Electrochemistry, University of Tehran, IranPZ
Payam Zarrintaj
Payam Zarrintaj is an experienced polymer engineer; he received his BSc degree from Amirkabir University of Technology, Iran, followed by a MSc (2013) and PhD (2018) from Tehran University, Iran. He is currently a postdoctoral research fellow at the School of Chemical Engineering, Oklahoma State University, USA. His research interests include smart hydrogel/polymers and nanoparticles with well-controlled microstructures and properties for biomedical applications. He has contributed to >90 scientific papers and book chapters.
Affiliations and expertise
Postdoctoral Research Fellow, School of Chemical Engineering, Oklahoma State University, Stillwater, OK, USAMS
Mohammad Reza Saeb
Dr. Mohammad Reza Saeb received his PhD in 2008 from Amirkabir University of Technology (Iran) and is currently a Professor at the Department of Pharmaceutical Chemistry, Medical University of Gdańsk (Poland). His research focuses on advanced materials and manufacturing processes, including polymer blends, composites, and nanocomposites, with particular emphasis on biomaterials and flame-retardant polymers, as well as the recycling and upcycling of polymer and biowastes. He has authored or co-authored more than 500 articles in high-impact journals and is currently serving as the Editor-in-Chief of Polymers from Renewable Resources, published by SAGE.
Affiliations and expertise
Professor, Department of Pharmaceutical Chemistry, Medical University of Gdańsk, Poland.MM
Masoud Mozafari
Dr. Masoud Mozafari is a Fellow at Lunenfeld Tanenbaum Research Institute, Mount Sinai Health Hospital, University of Toronto. He was previously Assistant Professor and Director of the Bioengineering Lab, at the Nanotechnology and Advanced Materials Department, Materials and Energy Research Center, Cellular and Molecular Research Center, and Department of Tissue Engineering and Regenerative Medicine of the Iran University of Medical Sciences (IUMS), Tehran, Iran. Dr. Mozafari’s research interests range across biomaterials, nanotechnology, and tissue engineering, and he is known for the development of strategies for the treatment of damaged tissues and organs, and controlling biological substances for targeted delivery into the human body. Dr. Mozafari has received several awards, including the Khwarizmi Award and the Julia Polak European Doctorate Award for outstanding translational research contributions to the field of biomaterials. He has also received the WIPO Medal for Inventors from The World Intellectual Property Organization (WIPO), in recognition of his contributions to economic and technological development. Dr. Mozafari is currently working on the editorial board of several journals.
Affiliations and expertise
Research Fellow, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, CanadaSB
Sidi A. Bencherif
Dr. Sidi A. Bencherif earned a Ph.D. in Chemistry from Carnegie Mellon University in 2009 and is a Senior Researcher at CNRS. A world-renowned expert in biomaterials, he is recognized among the top 2% of scientists worldwide by Stanford University and Elsevier. His research focuses on the design of advanced biomaterials for therapeutic applications, with an emphasis on regenerative medicine, drug delivery, and cancer immunotherapy. Dr. Bencherif’s work bridges cutting-edge materials science with clinical outcomes, addressing key medical challenges by integrating innovative biomaterials into clinical practice.
Affiliations and expertise
CNRS, PBS, UMR, University of Rouen Normandie, Rouen, France