Computational Biomaterials: Computational Simulations for Biomedicine

Designing nanotheranostics with machine learning

The inherent limits of traditional diagnoses and therapies have driven the development and application of emerging nanotechnologies for more effective and safer management of diseases, herein referred to as 'nanotheranostics'. Although many important technological successes have been achieved in this field, widespread adoption of nanotheranostics as a new paradigm is hindered by specific obstacles, including time-consuming synthesis of nanoparticles, incomplete understanding of nano-bio interactions, and challenges regarding chemistry, manufacturing and the controls required for clinical translation and commercialization. As a key branch of artificial intelligence, machine learning (ML) provides a set of tools capable of performing time-consuming and result-perception tasks, thus offering unique opportunities for nanotheranostics. This Review summarizes the progress and challenges in this emerging field of ML-aided nanotheranostics, and discusses the opportunities in developing next-generation nanotheranostics with reliable datasets and advanced ML models to offer better clinical benefits to patients.

Programmable biomaterials for bone regeneration

Programmable biomaterials are distinguished by their ability to adjust properties and functions on demand, in a periodic, reversible, or sequential manner. This contrasts with traditional biomaterials, which undergo irreversible, uncontrolled changes. This review synthesizes key advances in programmable biomaterials, examining their design principles, functionalities and applications in bone regeneration. It charts the transition from traditional to programmable biomaterials, emphasizing their enhanced precision, safety and control, which are critical from clinical and biosafety standpoints. We then classify programmable biomaterials into six types: dynamic nucleic acid-based biomaterials, electrically responsive biomaterials, bioactive scaffolds with programmable properties, nanomaterials for targeted bone regeneration, surface-engineered implants for sequential regeneration and stimuli-responsive release materials. Each category is analyzed for its structural properties and its impact on bone tissue engineering. Finally, the review further concludes by highlighting the challenges faced by programmable biomaterials and suggests integrating artificial intelligence and precision medicine to enhance their application in bone regeneration and other biomedical fields.

Synthesis and characterization of fluorenone derivatives with electrical properties explored using density functional theory (DFT)

This study provides thorough computational and experimental assessments of four types of novel synthesized thiosemicarbazones. The compounds were effectively synthesized using a condensation reaction between thiosemicarbazide and fluorenone, producing a remarkable range of 70-88%. Additional chemical structures were examined utilizing spectroscopic methods, including Fourier-transform infrared spectroscopy (FTIR), NMR spectroscopy, and ultraviolet-visible spectroscopy. The computational analyses utilized DFT using the M06/6-311G (d, p) technique. The electrical characteristics, including the stability of orbitals via energy exchange between a donor and acceptor, can be evaluated by natural bond orbital (NBO) analysis. The nonlinear optical (NLO) properties were analyzed to detect any prohibited energy gaps. FTIR and UV-visible data were computed using the identical M06/6-311G (d, p) level of theory. The NBO test has confirmed the occurrence of charge separation due to the efficient transfer of electrons from the donor to the acceptor unit over the π bridge. The molecular chemical softness and hardness are dependable indications of a molecule's chemical stability. A significant magnitude of the absolute value of polarizability and hyper-polarizability indicates considerable dispersion of electric charge. The outcomes derived from Density Functional Theory (DFT) generally align well with experimental findings.

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

Direct and cell-mediated EV-ECM interplay

Extracellular vesicles (EV) are a heterogeneous group of lipid particles excreted by cells. They play an important role in regeneration, development, inflammation, and cancer progression, together with the extracellular matrix (ECM), which they constantly interact with. In this review, we discuss direct and indirect interactions of EVs and the ECM and their impact on different physiological processes. The ECM affects the secretion of EVs, and the properties of the ECM and EVs modulate EVs' diffusion and adhesion. On the other hand, EVs can affect the ECM both directly through enzymes and indirectly through the modulation of the ECM synthesis and remodeling by cells. This review emphasizes recently discovered types of EVs bound to the ECM and isolated by enzymatic digestion, including matrix-bound nanovesicles (MBV) and tissue-derived EV (TiEV). In addition to the experimental studies, computer models of the EV-ECM-cell interactions, from all-atom models to quantitative pharmacology models aiming to improve our understanding of the interaction mechanisms, are also considered. STATEMENT OF SIGNIFICANCE: Application of extracellular vesicles in tissue engineering is an actively developing area. Vesicles not only affect cells themselves but also interact with the matrix and change it. The matrix also influences both cells and vesicles. In this review, different possible types of interactions between vesicles, matrix, and cells are discussed. Furthermore, the united EV-ECM system and its regulation through the cellular activity are presented.

Curing and Cross-Linking Processes in the Poly(3,3-bis-azidomethyl oxetane)-tetrahydrofuran/Toluene Diisocyanate/Trimethylolpropane System: A Density Functional Theory and Accelerated ReaxFF Molecular Dynamics Investigation

The physical and chemical properties of solid propellant are influenced by the composition and structure of the binder, with its network structure being formed through curing and cross-linking reactions. Therefore, understanding the mechanisms of these reactions is crucial. In this study, we investigated the curing and cross-linking mechanisms of poly(3,3-bis-azidomethyl oxetane)-tetrahydrofuran (PBT), toluene diisocyanate (TDI), and trimethylolpropane (TMP) using a combination of density functional theory (DFT) calculations and accelerated ReaxFF molecular dynamics (MD) simulations. DFT calculations revealed that the steric effect of the -CH3 group in TDI exerts a significant influence on the curing reaction between TDI and PBT. Additionally, in the cross-linking process, the energy barrier for TDI reacting with TMP was found to be much lower than that for TDI reacting with the PBT-TDI intermediate. Subsequently, we conducted competing reaction processes of TMP/TDI-PBT-TDI cross-linking and TDI-PBT-TDI self-cross-linking using accelerated MD simulations within the fitted ReaxFF framework. The results showed that the successful frequency of TMP/TDI-PBT-TDI cross-linking was substantially higher than that of TDI-PBT-TDI self-cross-linking, consistent with the energy barrier results from DFT calculations. These findings deepen our understanding of the curing and cross-linking mechanisms of the PBT system, providing valuable insights for the optimization and design of solid propellants.

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

Optical Properties Prediction for Red and Near-Infrared Emitting Carbon Dots Using Machine Learning

Functional nanostructures build up a basis for the future materials and devices, providing a wide variety of functionalities, a possibility of designing bio-compatible nanoprobes, etc. However, development of new nanostructured materials via trial-and-error approach is obviously limited by laborious efforts on their syntheses, and the cost of materials and manpower. This is one of the reasons for an increasing interest in design and development of novel materials with required properties assisted by machine learning approaches. Here, the dataset on synthetic parameters and optical properties of one important class of light-emitting nanomaterials - carbon dots are collected, processed, and analyzed with optical transitions in the red and near-infrared spectral ranges. A model for prediction of spectral characteristics of these carbon dots based on multiple linear regression is established and verified by comparison of the predicted and experimentally observed optical properties of carbon dots synthesized in three different laboratories. Based on the analysis, the open-source code is provided to be used by researchers for the prediction of optical properties of carbon dots and their synthetic procedures.

Progress in the Computer-Aided Analysis in Multiple Aspects of Nanocatalysis Research

Making the utmost of the differences and advantages of multiple disciplines, interdisciplinary integration breaks the science boundaries and accelerates the progress in mutual quests. As an organic connection of material science, enzymology, and biomedicine, nanozyme-related research is further supported by computer technology, which injects in new vitality, and contributes to in-depth understanding, unprecedented insights, and broadened application possibilities. Utilizing computer-aided first-principles method, high-speed and high-throughput mathematic, physic, and chemic models are introduced to perform atomic-level kinetic analysis for nanocatalytic reaction process, and theoretically illustrate the underlying nanozymetic mechanism and structure-function relationship. On this basis, nanozymes with desirable properties can be designed and demand-oriented synthesized without repeated trial-and-error experiments. Besides that, computational analysis and device also play an indispensable role in nanozyme-based detecting methods to realize automatic readouts with improved accuracy and reproducibility. Here, this work focuses on the crossing of nanocatalysis research and computational technology, to inspire the research in computer-aided analysis in nanozyme field to a greater extent.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Hydrogel-Based Growth Factor Delivery Platforms: Strategies and Recent Advances

Growth factors play a crucial role in regulating a broad variety of biological processes and are regarded as powerful therapeutic agents in tissue engineering and regenerative medicine in the past decades. However, their application is limited by their short half-lives and potential side effects in physiological environments. Hydrogels are identified as having the promising potential to prolong the half-lives of growth factors and mitigate their adverse effects by restricting them within the matrix to reduce their rapid proteolysis, burst release, and unwanted diffusion. This review discusses recent progress in the development of growth factor-containing hydrogels for various biomedical applications, including wound healing, brain tissue repair, cartilage and bone regeneration, and spinal cord injury repair. In addition, the review introduces strategies for optimizing growth factor release including affinity-based delivery, carrier-assisted delivery, stimuli-responsive delivery, spatial structure-based delivery, and cellular system-based delivery. Finally, the review presents current limitations and future research directions for growth factor-delivering hydrogels.

Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

Porous organic frameworks (POFs) have become a highly sought-after research domain that offers a promising avenue for developing cutting-edge nanostructured materials, both in their pristine state and when subjected to various chemical and structural modifications. Metal-organic frameworks, covalent organic frameworks, and hydrogen-bonded organic frameworks are examples of these emerging materials that have gained significant attention due to their unique properties, such as high crystallinity, intrinsic porosity, unique structural regularity, diverse functionality, design flexibility, and outstanding stability. This review provides an overview of the state-of-the-art research on base-stable POFs, emphasizing the distinct pros and cons of reticular framework nanoparticles compared to other types of nanocluster materials. Thereafter, the review highlights the unique opportunity to produce multifunctional tailoring nanoparticles to meet specific application requirements. It is recommended that this potential for creating customized nanoparticles should be the driving force behind future synthesis efforts to tap the full potential of this multifaceted material category.

Nanoprobe-based molecular imaging for tumor stratification

The responses of patients to tumor therapies vary due to tumor heterogeneity. Tumor stratification has been attracting increasing attention for accurately distinguishing between responders to treatment and non-responders. Nanoprobes with unique physical and chemical properties have great potential for patient stratification. This review begins by describing the features and design principles of nanoprobes that can visualize specific cell types and biomarkers and release inflammatory factors during or before tumor treatment. Then, we focus on the recent advancements in using nanoprobes to stratify various therapeutic modalities, including chemotherapy, radiotherapy (RT), photothermal therapy (PTT), photodynamic therapy (PDT), chemodynamic therapy (CDT), ferroptosis, and immunotherapy. The main challenges and perspectives of nanoprobes in cancer stratification are also discussed to facilitate probe development and clinical applications.

Functionalization of and through Melanin: Strategies and Bio-Applications

A unique feature of nanoparticles for bio-application is the ease of achieving multi-functionality through covalent and non-covalent functionalization. In this way, multiple therapeutic actions, including chemical, photothermal and photodynamic activity, can be combined with different bio-imaging modalities, such as magnetic resonance, photoacoustic, and fluorescence imaging, in a theragnostic approach. In this context, melanin-related nanomaterials possess unique features since they are intrinsically biocompatible and, due to their optical and electronic properties, are themselves very efficient photothermal agents, efficient antioxidants, and photoacoustic contrast agents. Moreover, these materials present a unique versatility of functionalization, which makes them ideal for the design of multifunctional platforms for nanomedicine integrating new functions such as drug delivery and controlled release, gene therapy, or contrast ability in magnetic resonance and fluorescence imaging. In this review, the most relevant and recent examples of melanin-based multi-functionalized nanosystems are discussed, highlighting the different methods of functionalization and, in particular, distinguishing pre-functionalization and post-functionalization. In the meantime, the properties of melanin coatings employable for the functionalization of a variety of material substrates are also briefly introduced, especially in order to explain the origin of the versatility of melanin functionalization. In the final part, the most relevant critical issues related to melanin functionalization that may arise during the design of multifunctional melanin-like nanoplatforms for nanomedicine and bio-application are listed and discussed.

A Molecular Theory of Polypeptide Adsorption at Inorganic Surfaces

A faithful description of polypeptide adsorption at ionizable surfaces remains a theoretical challenge from a molecular perspective due to the strong coupling of local thermodynamic nonideality and ionizations of both the adsorbate and substrate that are sensitive to the solution condition such as pH, ion valence, and salt concentration. Building upon a recently developed coarse-grained model for natural amino acids in bulk electrolyte solutions, here we report a molecular theory applicable to polypeptide adsorption on ionizable inorganic surfaces over a broad range of inhomogeneous conditions. Our thermodynamic model is able to account for diverse solution effects as well as the amino-acid sequence on polypeptide adsorption and surface association such as hydrogen bonding or bidentate coordination. The theoretical predictions have been validated by extensive comparison with experimental data for the adsorption isotherms of three representative polypeptides at a titanium surface.

Artificial Intelligence in Public Health: Current Trends and Future Possibilities

Artificial intelligence (AI) is a discipline that studies whether and how intelligent computer systems that can simulate the capacity and behaviour of human thought can be created [...]