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Computational Biomaterials: Computational Simulations for Biomedicine.

Xinyue Dai¹, Yu Chen^1,2

¹Materdicine Lab, School of Life Sciences, Shanghai University, Shanghai, 200444, P. R. China.

Advanced Materials (Deerfield Beach, Fla.)

|August 2, 2022

Summary

Computational biomaterials leverage advanced simulations and computing to predict properties and biological effects, accelerating disease prevention, diagnostics, and therapeutics. These methods offer new pathways for versatile biomaterial development and clinical use.

Keywords:

biomaterials computational biomaterials computational simulations disease treatments nanomedicine

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Area of Science:

Multidisciplinary field integrating materials science, computational chemistry, physics, and biomedical engineering.
Focuses on the intersection of computational methods and biomaterial science for healthcare applications.

Background:

Advancements in material simulation (quantum chemistry, molecular dynamics) and computing (AI, machine learning) enable detailed biomaterial analysis.
High-performance computing facilitates exploration of complex physicochemical and biological interactions of biomaterials.

Purpose of the Study:

To introduce and define the concept of 'computational biomaterials'.
To summarize and discuss computational methods for exploring biomaterial properties (optical, magnetic, electronic, acoustic) and biological effects.
To present applications in disease diagnosis, drug delivery, therapeutics, biomimetic materials, and biosafety evaluations.

Main Methods:

Utilizes quantum chemistry methods, molecular dynamics, Monte Carlo simulations, and phase field modeling.
Employs high-throughput screening, artificial intelligence, and machine learning approaches.
Focuses on theoretical calculations of physiochemical properties and biological performance.

Main Results:

Demonstrates the capability of computational simulations to predict diverse physicochemical properties and biological effects of biomaterials.
Highlights the application of these methods in understanding biomaterial behavior for disease diagnosis, drug delivery, and therapeutics.
Presents the utility of theoretical simulations for biosafety evaluation of biomaterials.

Conclusions:

Computational biomaterials offer a powerful framework for fundamental mechanism-level exploration and property prediction.
These simulations are crucial for advancing biomaterial design, development, and clinical translation.
Future prospects include enhanced methodologies for versatile biomaterial development and utilization.