Design of self-assembly dipeptide hydrogels and machine learning via their chemical features

Dynamic Borate Esterification for Evolved Supramolecular Chirality and Chiral Optics

Topological chemical reactions in confined environments offer unique opportunities for constructing dynamically tunable crystalline materials and architecturally defined polymers. However, their potential within functional supramolecular systems and chiral materials remains largely untapped. In this work, we introduce, for the first time, a borate esterification reaction to achieve dynamic modulation of supramolecular chirality and chiroptical properties under aggregation conditions. Pyrene-phenylalanine derivatives, following functionalization with phenylboronic acid groups, coassemble with catechol-functionalized pyrene derivatives. This coassembly undergoes spontaneous and highly efficient borate esterification under ambient conditions, inducing nanoscale morphological evolution, and an inversion of supramolecular chirality. Both experimental results and DFT-based computations reveal that the supramolecular chirality inversion is primarily driven by a transition from π-π stacking to CH-π interactions between pyrene moieties. This coassembly-borate esterification process represents a powerful integration of noncovalent assembly and covalent chemistry, providing a versatile platform for the design of soft materials and chiral functional systems. Moreover, the introduction of alizarin derivatives containing catechol motifs enables the transfer of circularly polarized luminescence (CPL), resulting in tunable emission shifts from blue and cyan to red. This work broadens the functional scope of chiral luminescent materials and opens new avenues for their application.

Smart hydrogel: A new platform for cancer therapy

Cancer is a significant contributor to mortality worldwide, posing a significant threat to human life and health. The unique bioactivity, ability to precisely control drug release, and minimally invasive properties of hydrogels are indispensable attributes that facilitate optimal performance in cancer therapy. However, conventional hydrogels lack the ability to dynamically respond to changes in the surrounding environment, withstand drastic changes in the microenvironment, and trigger drug release on demand. Therefore, this review focuses on smart-responsive hydrogels that are capable of adapting and responding to external stimuli. We comprehensively summarize the raw materials, preparation, and cross-linking mechanisms of smart hydrogels derived from natural and synthetic materials, elucidate the response principles of various smart-responsive hydrogels according to different stimulation sources. Further, we systematically illustrate the important role played by hydrogels in modern cancer therapies within the context of therapeutic principles. Meanwhile, the smart hydrogel that uses machine learning to design precise drug delivery has shown great prospects in cancer therapy. Finally, we present the outlook on future developments and make suggestions for future related work. It is anticipated that this review will promote the practical application of smart hydrogels in cancer therapy and contribute to the advancement of medical treatment.

Evolving biomaterials design from trial and error to intelligent innovation

The design and exploration of biomaterials plays a pivotal role in many fields, including medical and engineering. The prevailing approach to biomaterials discovery relies on orthogonal experiments, with repeated attempts to optimize experimental conditions. This method has proven invaluable in gaining experience, but it is also inefficient and challenging to predict the behavior of complex systems. The advent of high-throughput screening (HTS) techniques has led to a notable enhancement in the efficiency of biomaterials development, enabling researchers to assess a vast array of material combinations within a relatively short timeframe. Nevertheless, the emergence of artificial intelligence (AI) has been the catalyst for a new era in biomaterials design. AI has markedly accelerated the development of new materials by enabling the prediction of material properties through machine learning (ML) and deep learning models, as well as optimizing the design pipeline. This review will present a systematic overview of the development of biomaterials design technology. It will also explore the integration of AI with HTS technology and envisage the potential of AI-driven materials design in biomaterials for the future. STATEMENT OF SIGNIFICANCE: The design and synthesis of biomaterials have undergone substantial shifts, reflecting evolving research paradigms. High-throughput screening has emerged as a broad and efficient alternative to traditional free-form combination methods in biomaterial design. The advent of artificial intelligence (AI) enables personalized biomaterial design and, as a transformative tool in biomaterial development, is poised to redefine the field and offer long-term solutions for its advancement. Building on these advancements, this review systematically summarizes the evolution of biomaterial design, offering insights into the future trajectory of the field.

Poly(propylene fumarate) Composite Scaffolds for Bone Tissue Engineering: Innovation in Fabrication Techniques and Artificial Intelligence Integration

Over the past three decades, the biodegradable polymer known as poly(propylene fumarate) (PPF) has been the subject of numerous research due to its unique properties. Its biocompatibility and controllable mechanical properties have encouraged numerous scientists to manufacture and produce a wide range of PPF-based materials for biomedical purposes. Additionally, the ability to tailor the degradation rate of the scaffold material to match the rate of new bone tissue formation is particularly relevant in bone tissue engineering, where synchronized degradation and tissue regeneration are critical for effective healing. This review thoroughly summarizes the advancements in different approaches for PPF and PPF-based composite scaffold preparation for bone tissue engineering. Additionally, the challenges faced by each approach, such as biocompatibility, degradation, mechanical features, and crosslinking, were emphasized, and the noteworthy benefits of the most pertinent synthesis strategies were highlighted. Furthermore, the synergistic outcome between tissue engineering and artificial intelligence (AI) was addressed, along with the advantages brought by the implication of machine learning (ML) as well as the revolutionary impact on regenerative medicines. Future advances in bone tissue engineering could be facilitated by the enormous potential for individualized and successful regenerative treatments that arise from the combination of tissue engineering and artificial intelligence. By assessing a patient's reaction to a certain drug and choosing the best course of action depending on the patient's genetic and clinical characteristics, AI can also assist in the treatment of illnesses. AI is also used in drug research and discovery, target identification, clinical trial design, and predicting the safety and effectiveness of novel medications. Still, there are ethical issues including data protection and the requirement for reliable data management systems. AI adoption in the healthcare sector is expensive, involving staff and facility investments as well as training healthcare professionals on its application.

Design of Multi-Cancer VOCs Profiling Platform via a Deep Learning-Assisted Sensing Library Screening Strategy

The efficiency of sensor arrays in parallel discrimination of multianalytes is fundamentally influenced by the quantity and performance of the sensor elements. The advent of combinational design has notably accelerated the generation of chemical libraries, offering numerous candidates for the development of robust sensor arrays. However, screening elements with superior cross-responsiveness remains challenging, impeding the development of high-performance sensor arrays. Herein, we propose a new deep learning-assisted, two-step screening strategy to identify the optimal combination of minimal sensor elements, using a designed volatile organic compounds (VOCs)-targeted sensor library. 400 sensing elements constructed by pairing 20 ionizable cationic elements and 20 anionic dyes in the sensor library were employed for various VOCs, generating plentiful color variation data. By employing a feedforward neural network─random forest-recursive feature elimination (FRR) algorithm, sensing elements were effectively screened, resulting in the rapidly producing 8-element and 10-element arrays for two VOC models, both achieving 100% discrimination accuracy. Furthermore, a smartphone-based point-of-care testing (POCT) platform achieved cancer discrimination in a simulated cancer VOC model, using image-based deep learning, demonstrating the rationality and practicality of deep learning in the assembly of sensor elements for parallel sensing platforms.

AI-driven 3D bioprinting for regenerative medicine: From bench to bedside

In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.

Understanding multicomponent low molecular weight gels from gelators to networks

BACKGROUND

The construction of gels from low molecular weight gelators (LMWG) has been extensively studied in the fields of bio-nanotechnology and other fields. However, the understanding gaps still prevent the prediction of LMWG from the full design of those gel systems. Gels with multicomponent become even more complicated because of the multiple interference effects coexist in the composite gel systems.

AIM OF REVIEW

This review emphasizes systems view on the understanding of multicomponent low molecular weight gels (MLMWGs), and summarizes recent progress on the construction of desired networks of MLMWGs, including self-sorting and co-assembly, as well as the challenges and approaches to understanding MLMWGs, with the hope that the opportunities from natural products and peptides can speed up the understanding process and close the gaps between the design and prediction of structures.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review is focused on three key concepts. Firstly, understanding the complicated multicomponent gels systems requires a systems perspective on MLMWGs. Secondly, several protocols can be applied to control self-sorting and co-assembly behaviors in those multicomponent gels system, including the certain complementary structures, chirality inducing and dynamic control. Thirdly, the discussion is anchored in challenges and strategies of understanding MLMWGs, and some examples are provided for the understanding of multicomponent gels constructed from small natural products and subtle designed short peptides.

Combinatorial Library-Screened Array for Rapid Clinical Sperm Quality Assessment

Nonspecific interactions are ubiquitous and important in biology; however, they are rarely valued or employed in the field of clinical disease diagnosis. Relying on nonspecific cross-interactions, sensor arrays have shown great potential in distinguishing mixtures or nuanced compounds. However, developing generic strategies for constructing effective arrays tailored to compositionally complicated and highly individualized clinical biospecimens remains challenging. Here, we introduce a combinatorial chemistry-screened array strategy, leveraging four-component Ugi reactions to achieve the rapid synthesis of hundreds of structurally diverse sensing elements. Moreover, effective sensor arrays can thus be built by rapidly screening sensors against diverse analytes. Next, we demonstrate the practical applicability of this array by clinical sperm quality assessment, given the current lack of well-established clinical rapid detection techniques. A library of 192 structurally diverse sensor elements was synthesized. Following screening, a pruned 14-element array was successfully constructed, achieving 94.2% accuracy in distinguishing between healthy individuals and four types of abnormal sperm samples from patients within 1 min. This universal strategy avoids complex sensor design and greatly improves the efficiency of sensor array construction, offering a new way of designing effective arrays.

Predicting the Mechanical Properties of Supramolecular Gels

The prediction of gelation is an important target, yet current models do not predict any post-gel properties. Gels can be formed through the self-assembly of many molecules, but close analogs often do not form gels. There has been success using a number of computational approaches to understand and predict gelation from molecular structures. However, these approaches focus on whether or not a gel will form, not on the properties of the resulting gels. Critically, it is the properties of the gels that are important for a specific application, not simply whether a gel will be formed. Supramolecular gels are often kinetically trapped, meaning that predicting gel properties is inherently a difficult challenge. Here, the first successful a priori prediction of gel properties for such self-assembled, supramolecular systems is reported.

Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes

The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting the processes, structures and environmental effects of NOM self-assembly. This review seeks to provide a tutorial-like compilation of data source determination, algorithm selection, model construction, interpretability analyses, applications and challenges for big-data-based ML aiming at elucidating NOM self-assembly mechanisms in environments. The results from advanced nano-submicron-scale spatial chemical analytical technologies are suggested as input data which provide the combined information of molecular interactions and structural visualization. The existing ML algorithms need to handle multi-scale and multi-modal data, necessitating the development of new algorithmic frameworks. Interpretable supervised models are crucial owing to their strong capacity of quantifying the structure-property-effect relationships and bridging the gap between simply data-driven ML and complicated NOM assembly practice. Then, the necessity and challenges are discussed and emphasized on adopting ML to understand the geochemical behaviors and bioavailability of pollutants as well as the elemental cycling processes in environments resulting from the NOM self-assembly patterns. Finally, a research framework integrating ML, experiments and theoretical simulation is proposed for comprehensively and efficiently understanding the NOM self-assembly-involved environmental issues.

A Universal Method for Fingerprinting Multiplexed Bacteria: Evolving Pruned Sensor Arrays via Machine Learning-Driven Combinatorial Group-Specificity Strategy

Array-based sensing technology holds immense potential for discerning the intricacies of biological systems. Nevertheless, developing a universal strategy for simultaneous identification of diverse types of multianalytes and meeting the diagnostic needs of a range of multiclassified clinical diseases poses substantial challenges. Herein, we introduce a combination method for constructing sensor arrays by assembling two types of group-specific elements. Such a method enables the rapid generation of a library of 100 sensing units, each with dual bacterial targeting capabilities. By employing a three-step screening strategy optimized by machine learning algorithms, various optimal five-element arrays were rapidly obtained for diverse clinical infectious models. Moreover, the pruned arrays successfully identified disparate mixing ratios and quantitative detection of clinically prevalent bacterial strains. Optimized through nine multiclassification algorithms, the top-performing multilayer perceptron (MLP) model demonstrated impressive recognition capabilities, achieving 100% accuracy for diagnosing clinical urinary tract infection (UTI) and 99.4% accuracy for clinical sepsis detection in the test models we collected. Such a combinatorial library construction and screening process should be standard and provides insights into successfully generating powerful high-recognition sensor elements and configuring them into highly discriminative mini-sensor arrays.

Harnessing the power of machine learning into tissue engineering: current progress and future prospects

Tissue engineering is a discipline based on cell biology and materials science with the primary goal of rebuilding and regenerating lost and damaged tissues and organs. Tissue engineering has developed rapidly in recent years, while scaffolds, growth factors, and stem cells have been successfully used for the reconstruction of various tissues and organs. However, time-consuming production, high cost, and unpredictable tissue growth still need to be addressed. Machine learning is an emerging interdisciplinary discipline that combines computer science and powerful data sets, with great potential to accelerate scientific discovery and enhance clinical practice. The convergence of machine learning and tissue engineering, while in its infancy, promises transformative progress. This paper will review the latest progress in the application of machine learning to tissue engineering, summarize the latest applications in biomaterials design, scaffold fabrication, tissue regeneration, and organ transplantation, and discuss the challenges and future prospects of interdisciplinary collaboration, with a view to providing scientific references for researchers to make greater progress in tissue engineering and machine learning.

AI-Based solutions for current challenges in regenerative medicine

The emergence of Artificial Intelligence (AI) and its usage in regenerative medicine represents a significant opportunity that holds the promise of tackling critical challenges and improving therapeutic outcomes. This article examines the ways in which AI, including machine learning and data fusion techniques, can contribute to regenerative medicine, particularly in gene therapy, stem cell therapy, and tissue engineering. In gene therapy, AI tools can boost the accuracy and safety of treatments by analyzing extensive genomic datasets to target and modify genetic material in a precise manner. In cell therapy, AI improves the characterization and optimization of cell products like mesenchymal stem cells (MSCs) by predicting their function and potency. Additionally, AI enhances advanced microscopy techniques, enabling accurate, non-invasive and quantitative analyses of live cell cultures. AI enhances tissue engineering by optimizing biomaterial and scaffold designs, predicting interactions with tissues, and streamlining development. This leads to faster and more cost-effective innovations by decreasing trial and error. The convergence of AI and regenerative medicine holds great transformative potential, promising effective treatments and innovative therapeutic strategies. This review highlights the importance of interdisciplinary collaboration and the continued integration of AI-based technologies, such as data fusion methods, to overcome current challenges and advance regenerative medicine.

Harnessing the power of artificial intelligence for human living organoid research

As a powerful paradigm, artificial intelligence (AI) is rapidly impacting every aspect of our day-to-day life and scientific research through interdisciplinary transformations. Living human organoids (LOs) have a great potential for in vitro reshaping many aspects of in vivo true human organs, including organ development, disease occurrence, and drug responses. To date, AI has driven the revolutionary advances of human organoids in life science, precision medicine and pharmaceutical science in an unprecedented way. Herein, we provide a forward-looking review, the frontiers of LOs, covering the engineered construction strategies and multidisciplinary technologies for developing LOs, highlighting the cutting-edge achievements and the prospective applications of AI in LOs, particularly in biological study, disease occurrence, disease diagnosis and prediction and drug screening in preclinical assay. Moreover, we shed light on the new research trends harnessing the power of AI for LO research in the context of multidisciplinary technologies. The aim of this paper is to motivate researchers to explore organ function throughout the human life cycle, narrow the gap between in vitro microphysiological models and the real human body, accurately predict human-related responses to external stimuli (cues and drugs), accelerate the preclinical-to-clinical transformation, and ultimately enhance the health and well-being of patients.

Recent Strategies and Advances in Hydrogel-Based Delivery Platforms for Bone Regeneration

Bioactive molecules have shown great promise for effectively regulating various bone formation processes, rendering them attractive therapeutics for bone regeneration. However, the widespread application of bioactive molecules is limited by their low accumulation and short half-lives in vivo. Hydrogels have emerged as ideal carriers to address these challenges, offering the potential to prolong retention times at lesion sites, extend half-lives in vivo and mitigate side effects, avoid burst release, and promote adsorption under physiological conditions. This review systematically summarizes the recent advances in the development of bioactive molecule-loaded hydrogels for bone regeneration, encompassing applications in cranial defect repair, femoral defect repair, periodontal bone regeneration, and bone regeneration with underlying diseases. Additionally, this review discusses the current strategies aimed at improving the release profiles of bioactive molecules through stimuli-responsive delivery, carrier-assisted delivery, and sequential delivery. Finally, this review elucidates the existing challenges and future directions of hydrogel encapsulated bioactive molecules in the field of bone regeneration.

Personalized cancer vaccine design using AI-powered technologies

Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. While current cancer vaccines are primarily prophylactic, advancements in targeting tumor-associated antigens (TAAs) and neoantigens have paved the way for therapeutic vaccines. The integration of artificial intelligence (AI) into cancer vaccine development is revolutionizing the field by enhancing various aspect of design and delivery. This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses. By utilizing AI technologies, researchers can navigate complex biological datasets and uncover novel therapeutic targets, thereby improving the precision and efficacy of cancer vaccines. Despite the promise of AI-powered cancer vaccines, significant challenges remain, such as tumor heterogeneity and genetic variability, which can limit the effectiveness of neoantigen prediction. Moreover, ethical and regulatory concerns surrounding data privacy and algorithmic bias must be addressed to ensure responsible AI deployment. The future of cancer vaccine development lies in the seamless integration of AI to create personalized immunotherapies that offer targeted and effective cancer treatments. This review underscores the importance of interdisciplinary collaboration and innovation in overcoming these challenges and advancing cancer vaccine development.

Artificial intelligence and machine learning applications for cultured meat

Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time-and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. In addition, we have included a survey of datasets relevant to CM research. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.

Machine Learning-Aided Design of Highly Conductive Anion Exchange Membranes for Fuel Cells and Water Electrolyzers

Alkaline anion exchange membrane (AEM)-based fuel cells (AEMFCs) and water electrolyzers (AEMWEs) are vital for enabling the efficient and large-scale utilization of hydrogen energy. However, the performance of such energy devices is impeded by the relatively low conductivity of AEMs. The conventional trial-and-error approach to designing membrane structures has proven to be both inefficient and costly. To address this challenge, a fully connected neural network (FCNN) model is developed based on acid-catalyzed AEMs to analyze the relationship between structure and conductivity among 180,000 AEM variations. Under machine learning guidance, anilinium cation-type membranes are designed and synthesized. Molecular dynamics simulations and Mulliken charge population analysis validated that the presence of a large anilinium cation domain is a result of the inductive effect of N+ and benzene rings. The interconnected anilinium cation domains facilitated the formation of a continuous ion transport channel within the AEMs. Additionally, the incorporation of the benzyl electron-withdrawing group heightened the inductive effect, leading to high conductivity AEM variant as screened by the machine learning model. Furthermore, based on the highly active and low-cost monomers given by machine learning, the large-scale synthesis of anilinium-based AEMs confirms the potential for commercial applications.

Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine

BACKGROUND

Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role.

METHODS

The "artificial intelligence," "machine learning," "tissue engineering," "clinical evaluation," and "scaffold" keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated.

RESULTS

The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM.

CONCLUSION

The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside.

HIGHLIGHTS

The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).

A Foundation Model Identifies Broad-Spectrum Antimicrobial Peptides against Drug-Resistant Bacterial Infection

Development of potent and broad-spectrum antimicrobial peptides (AMPs) could help overcome the antimicrobial resistance crisis. We develop a peptide language-based deep generative framework (deepAMP) for identifying potent, broad-spectrum AMPs. Using deepAMP to reduce antimicrobial resistance and enhance the membrane-disrupting abilities of AMPs, we identify, synthesize, and experimentally test 18 T1-AMP (Tier 1) and 11 T2-AMP (Tier 2) candidates in a two-round design and by employing cross-optimization-validation. More than 90% of the designed AMPs show a better inhibition than penetratin in both Gram-positive (i.e., S. aureus) and Gram-negative bacteria (i.e., K. pneumoniae and P. aeruginosa). T2-9 shows the strongest antibacterial activity, comparable to FDA-approved antibiotics. We show that three AMPs (T1-2, T1-5 and T2-10) significantly reduce resistance to S. aureus compared to ciprofloxacin and are effective against skin wound infection in a female wound mouse model infected with P. aeruginosa. In summary, deepAMP expedites discovery of effective, broad-spectrum AMPs against drug-resistant bacteria.

QSPR study of viscoplastic properties of peptide-based hydrogels

In this study, the power of machine learning was harnessed to probe the link between molecular structures of peptide-based hydrogels and their viscoplastic properties. The selection of compounds was attempted in accordance with the prescribed full list of peptide-based materials exhibiting hydrogel functionality in the literature. In this pursuit, a complete set of molecular descriptors and fingerprints was considered - accounting for an entry of size 17,968 for each peptide-based structure analyzed. The elastic and viscous moduli response of materials were mapped over a wide frequency spectrum in the range [0.1-100] (rad/s). In general, the results indicate that the frequency-dependent mechanical response of peptide-based hydrogels is statistically correlated with its (inter)molecular attributes, such as charge, first ionization potential (or equivalently electronegativity), surface area, number of chemical substrates, bond type, and intermolecular interactions. The performance of several (supervised) soft computing techniques was measured, for our quantitative structure property relationships model. In addition, the hypothesis of mapping our databank to a new system of principal components was tested, by using an unsupervised methodology, which resulted in enhancement of the prediction accuracy. In terms of significance, the present article provides the first report of frequency-dependent elastic and viscous moduli, for a set of 70 peptide-based formulations with hydrogel functionality.Communicated by Ramaswamy H. Sarma.

HydrogelFinder: A Foundation Model for Efficient Self-Assembling Peptide Discovery Guided by Non-Peptidal Small Molecules

Self-assembling peptides have numerous applications in medicine, food chemistry, and nanotechnology. However, their discovery has traditionally been serendipitous rather than driven by rational design. Here, HydrogelFinder, a foundation model is developed for the rational design of self-assembling peptides from scratch. This model explores the self-assembly properties by molecular structure, leveraging 1,377 self-assembling non-peptidal small molecules to navigate chemical space and improve structural diversity. Utilizing HydrogelFinder, 111 peptide candidates are generated and synthesized 17 peptides, subsequently experimentally validating the self-assembly and biophysical characteristics of nine peptides ranging from 1-10 amino acids-all achieved within a 19-day workflow. Notably, the two de novo-designed self-assembling peptides demonstrated low cytotoxicity and biocompatibility, as confirmed by live/dead assays. This work highlights the capacity of HydrogelFinder to diversify the design of self-assembling peptides through non-peptidal small molecules, offering a powerful toolkit and paradigm for future peptide discovery endeavors.

Engineered Bio-Based Hydrogels for Cancer Immunotherapy

Immunotherapy represents a revolutionary paradigm in cancer management, showcasing its potential to impede tumor metastasis and recurrence. Nonetheless, challenges including limited therapeutic efficacy and severe immune-related side effects are frequently encountered, especially in solid tumors. Hydrogels, a class of versatile materials featuring well-hydrated structures widely used in biomedicine, offer a promising platform for encapsulating and releasing small molecule drugs, biomacromolecules, and cells in a controlled manner. Immunomodulatory hydrogels present a unique capability for augmenting immune activation and mitigating systemic toxicity through encapsulation of multiple components and localized administration. Notably, hydrogels based on biopolymers have gained significant interest owing to their biocompatibility, environmental friendliness, and ease of production. This review delves into the recent advances in bio-based hydrogels in cancer immunotherapy and synergistic combinatorial approaches, highlighting their diverse applications. It is anticipated that this review will guide the rational design of hydrogels in the field of cancer immunotherapy, fostering clinical translation and ultimately benefiting patients.

Self-assembly of heterochiral, aliphatic dipeptides with Leu

This work describes the self-assembly behavior of heterochiral, aliphatic dipeptides, l-Leu-d-Xaa (Xaa = Ala, Val, Ile, Leu), in green solvents such as acetonitrile (MeCN) and buffered water at neutral pH. Interestingly, water plays a structuring role because at 1% v/v, it enables dipeptide self-assembly in MeCN to yield organogels, which then undergo transition towards crystals. Other organic solvents and oils were tested for gelation, and metastable gels were formed in tetrahydrofuran, although at high peptide concentration (80 mM). Single-crystal X-ray diffraction revealed the dipeptides' supramolecular packing modes in amphipathic layers, as opposed to water channels reported for the homochiral Leu-Leu, or hydrophobic columns reported for homochiral Leu-Val and Leu-Ile.

Combinatorial discovery of antibacterials via a feature-fusion based machine learning workflow

The discovery of new antibacterials within the vast chemical space is crucial in combating drug-resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA). However, the traditional approach of screening the entire chemical library in an ergodic manner can be laborious and time-consuming. Machine learning-assisted screening of antibacterials alleviates the exploration effort but suffers from the lack of reliable and related datasets. To address these challenges, we devised a combinatorial library comprising over 110 000 candidates based on the Ugi reaction. A focused library was subsequently generated through uniform sampling of the entire library to narrow down the preliminary screening scale. A novel feature-fusion architecture called the latent space constraint neural network was developed which incorporated both fingerprint and physicochemical molecular descriptors to predict the antibacterial properties. This integration allowed the model to leverage the complementary information provided by these descriptors and improve the accuracy of predictions. Three lead compounds that demonstrated excellent efficacy against MRSA while alleviating drug resistance were identified. This workflow highlights the integration of machine learning with the combinatorial chemical library to expedite high-quality data collection and extensive data mining for antibacterial screening.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

Applications of peptide-based nanomaterials in targeting cancer therapy

To meet the demand for precision medicine, researchers are committed to developing novel strategies to reduce systemic toxicity and side effects in cancer treatment. Targeting peptides are widely applied due to their affinity and specificity, and their ability to be high-throughput screened, chemically synthesized and modified. More importantly, peptides can form ordered self-assembled structures through non-covalent supramolecular interactions, which can form nanostructures with different morphologies and functions, playing crucial roles in targeted diagnosis and treatment. Among them, in targeted immunotherapy, utilizing targeting peptides to block the binding between immune checkpoints and ligands, thereby activating the immune system to eliminate cancer cells, is an advanced therapeutic strategy. In this mini-review, we summarize the screening, self-assembly, and biomedical applications of targeting peptide-based nanomaterials. Furthermore, this mini-review summarizes the potential and optimization strategies of targeting peptides.

Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors

Supramolecular hydrogels derived from nucleosides have been gaining significant attention in the biomedical field due to their unique properties and excellent biocompatibility. However, a major challenge in this field is that there is no model for predicting whether nucleoside derivative will form a hydrogel. Here, we successfully develop a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives. The optimal model with a 71% (95% Confidence Interval, 0.69-0.73) accuracy is established based on a dataset of 71 reported nucleoside derivatives. 24 molecules are selected via the optimal model external application and the hydrogel-forming ability is experimentally verified. Among these, two rarely reported cation-independent nucleoside hydrogels are found. Based on their self-assemble mechanisms, the cation-independent hydrogel is found to have potential applications in rapid visual detection of Ag+ and cysteine. Here, we show the machine learning model may provide a tool to predict nucleoside derivatives with hydrogel-forming ability.

Blood-brain barrier disruption in dementia: Nano-solutions as new treatment options

Candidate drugs targeting the central nervous system (CNS) demonstrate extremely low clinical success rates, with more than 98% of potential treatments being discontinued due to poor blood-brain barrier (BBB) permeability. Neurological conditions were shown to be the second leading cause of death globally in 2016, with the number of people currently affected by neurological disorders increasing rapidly. This increasing trend, along with an inability to develop BBB permeating drugs, is presenting a major hurdle in the treatment of CNS-related disorders, like dementia. To overcome this, it is necessary to understand the structure and function of the BBB, including the transport of molecules across its interface in both healthy and pathological conditions. The use of CNS drug carriers is rapidly gaining popularity in CNS research due to their ability to target BBB transport systems. Further research and development of drug delivery vehicles could provide essential information that can be used to develop novel treatments for neurological conditions. This review discusses the BBB and its transport systems and evaluates the potential of using nanoparticle-based delivery systems as drug carriers for CNS disease with a focus on dementia.

Extracellular Vesicles and Hydrogels: An Innovative Approach to Tissue Regeneration

Extracellular vesicles have emerged as promising tools in regenerative medicine due to their inherent ability to facilitate intercellular communication and modulate cellular functions. These nanosized vesicles transport bioactive molecules, such as proteins, lipids, and nucleic acids, which can affect the behavior of recipient cells and promote tissue regeneration. However, the therapeutic application of these vesicles is frequently constrained by their rapid clearance from the body and inability to maintain a sustained presence at the injury site. In order to overcome these obstacles, hydrogels have been used as extracellular vesicle delivery vehicles, providing a localized and controlled release system that improves their therapeutic efficacy. This Review will examine the role of extracellular vesicle-loaded hydrogels in tissue regeneration, discussing potential applications, current challenges, and future directions. We will investigate the origins, composition, and characterization techniques of extracellular vesicles, focusing on recent advances in exosome profiling and the role of machine learning in this field. In addition, we will investigate the properties of hydrogels that make them ideal extracellular vesicle carriers. Recent studies utilizing this combination for tissue regeneration will be highlighted, providing a comprehensive overview of the current research landscape and potential future directions.

Theoretical trends in the dynamics simulations of molecular machines across multiple scales

Over the past few decades, molecular machines have been extensively studied, since they are composed of single molecules for functional materials capable of responding to external stimuli, enabling motion at scales ranging from the microscopic to the macroscopic level within molecular aggregates. This advancement holds the potential to efficiently transform external resources into mechanical movement, achieved through precise control of conformational changes in stimuli-responsive materials. However, the underlying mechanism that links microscopic and macroscopic motions remains unclear, demanding computational development associated with simulating the construction of molecular machines from single molecules. This bottleneck has impeded the design of more efficient functional materials. Advancements in theoretical simulations have successfully been developed in various computational models to unveil the operational mechanisms of stimulus-responsive molecular machines, which could help us reduce the costs in experimental trial-and-error procedures. It opens doors to the computer-aided design of innovative functional materials. In this perspective, we have reviewed theoretical approaches employed in simulating dynamic processes involving conformational changes in molecular machines, spanning different scales and environmental conditions. In addition, we have highlighted current challenges and anticipated future trends in the collective control of aggregates within molecular machines. Our goal is to provide a comprehensive overview of recent theoretical advancements in the field of molecular machines, offering valuable insights for the design of novel smart materials.

Computational Design of Peptide Assemblies

With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expanding exponentially. Consequently, attempting to explore all functional sequences comprehensively through experience and experiments alone has become impractical. By utilizing computational methods, especially artificial intelligence enhanced molecular dynamics (MD) simulation and de novo peptide design, there has been a significant expansion in the exploration of sequence space. Through these methods, a variety of supramolecular functional materials, including fibers, two-dimensional arrays, nanocages, etc., have been designed by meticulously controlling the inter- and intramolecular interactions. In this review, we first provide a brief overview of the current main computational methods and then focus on the computational design methods for various self-assembled peptide materials. Additionally, we introduce some representative protein self-assemblies to offer guidance for the design of self-assembling peptides.

Dynamic covalent synthesis

Dynamic covalent synthesis aims to precisely control the assembly of simple building blocks linked by reversible covalent bonds to generate a single, structurally complex, product. In recent years, considerable progress in the programmability of dynamic covalent systems has enabled easy access to a broad range of assemblies, including macrocycles, shape-persistent cages, unconventional foldamers and mechanically-interlocked species (catenanes, knots, etc.). The reversibility of the covalent linkages can be either switched off to yield stable, isolable products or activated by specific physico-chemical stimuli, allowing the assemblies to adapt and respond to environmental changes in a controlled manner. This activatable dynamic property makes dynamic covalent assemblies particularly attractive for the design of complex matter, smart chemical systems, out-of-equilibrium systems, and molecular devices.

Structural and vibrational analysis of glycyl-L-phenylalanine and phase transition under high-pressure

The structural and vibrational properties of the glycyl-L-phenylalanine dipeptide were investigated using vibrational spectroscopy (Raman and infrared) and first-principle calculations. Raman spectroscopy measurements were performed between 100 and 3200 cm-1 and infrared spectroscopy from 100 and 3200 cm-1 under ambient conditions. The conformational analysis of the zwitterionic form of the dipeptide was performed using the B3LYP functional, the 6-311++ base set and the Polarizable Continuum Model of solvation, determining the lowest energy conformation and assigning the vibrational modes. The effect of pressure on the glycyl-1-phenylalanine crystal was investigated using the Raman spectroscopy between 0.0 and -7.1 GPa in the spectral region of 100 - 3200 cm-1. As a result, conformational changes around 1.0 GPa were observed in the lattice modes and in some internal modes, showing a reorganization of the molecule in the crystal. In the decompression process, it was observed that the conformational change is reversible and the original Raman spectrum is recoverd.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.

Exploring the Potential of Artificial Intelligence for Hydrogel Development-A Short Review

AI and ML have emerged as transformative tools in various scientific domains, including hydrogel design. This work explores the integration of AI and ML techniques in the realm of hydrogel development, highlighting their significance in enhancing the design, characterisation, and optimisation of hydrogels for diverse applications. We introduced the concept of AI train hydrogel design, underscoring its potential to decode intricate relationships between hydrogel compositions, structures, and properties from complex data sets. In this work, we outlined classical physical and chemical techniques in hydrogel design, setting the stage for AI/ML advancements. These methods provide a foundational understanding for the subsequent AI-driven innovations. Numerical and analytical methods empowered by AI/ML were also included. These computational tools enable predictive simulations of hydrogel behaviour under varying conditions, aiding in property customisation. We also emphasised AI's impact, elucidating its role in rapid material discovery, precise property predictions, and optimal design. ML techniques like neural networks and support vector machines that expedite pattern recognition and predictive modelling using vast datasets, advancing hydrogel formulation discovery are also presented. AI and ML's have a transformative influence on hydrogel design. AI and ML have revolutionised hydrogel design by expediting material discovery, optimising properties, reducing costs, and enabling precise customisation. These technologies have the potential to address pressing healthcare and biomedical challenges, offering innovative solutions for drug delivery, tissue engineering, wound healing, and more. By harmonising computational insights with classical techniques, researchers can unlock unprecedented hydrogel potentials, tailoring solutions for diverse applications.

Diverging conformations guide dipeptide self-assembly into crystals or hydrogels

The prediction of dipeptide assembly into crystals or gels is challenging. This work reveals the diverging conformational landscape that guides self-organization towards different outcomes. In silico and experimental data enabled deciphering of the electronic circular dichroism (ECD) spectra of self-assembling dipeptides to reveal folded or extended conformers as key players.

Short Peptide Nanofiber Biomaterials Ameliorate Local Hemostatic Capacity of Surgical Materials and Intraoperative Hemostatic Applications in Clinics

Short designer self-assembling peptide (dSAP) biomaterials are a new addition to the hemostat group. It may provide a diverse and robust toolbox for surgeons to integrate wound microenvironment with much safer and stronger hemostatic capacity than conventional materials and hemostatic agents. Especially in noncompressible torso hemorrhage (NCTH), diffuse mucosal surface bleeding, and internal medical bleeding (IMB), with respect to the optimal hemostatic formulation, dSAP biomaterials are the ingenious nanofiber alternatives to make bioactive neural scaffold, nasal packing, large mucosal surface coverage in gastrointestinal surgery (esophagus, gastric lesion, duodenum, and lower digestive tract), epicardiac cell-delivery carrier, transparent matrix barrier, and so on. Herein, in multiple surgical specialties, dSAP-biomaterial-based nano-hemostats achieve safe, effective, and immediate hemostasis, facile wound healing, and potentially reduce the risks in delayed bleeding, rebleeding, post-operative bleeding, or related complications. The biosafety in vivo, bleeding indications, tissue-sealing quality, surgical feasibility, and local usability are addressed comprehensively and sequentially and pursued to develop useful surgical techniques with better hemostatic performance. Here, the state of the art and all-round advancements of nano-hemostatic approaches in surgery are provided. Relevant critical insights will inspire exciting investigations on peptide nanotechnology, next-generation biomaterials, and better promising prospects in clinics.

Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop

The amino acid sequences of peptides determine their self-assembling properties. Accurate prediction of peptidic hydrogel formation, however, remains a challenging task. This work describes an interactive approach involving the mutual information exchange between experiment and machine learning for robust prediction and design of (tetra)peptide hydrogels. We chemically synthesize more than 160 natural tetrapeptides and evaluate their hydrogel-forming ability, and then employ machine learning-experiment iterative loops to improve the accuracy of the gelation prediction. We construct a score function coupling the aggregation propensity, hydrophobicity, and gelation corrector Cg, and generate an 8,000-sequence library, within which the success rate of predicting hydrogel formation reaches 87.1%. Notably, the de novo-designed peptide hydrogel selected from this work boosts the immune response of the receptor binding domain of SARS-CoV-2 in the mice model. Our approach taps into the potential of machine learning for predicting peptide hydrogelator and significantly expands the scope of natural peptide hydrogels.

AMPFinder: A computational model to identify antimicrobial peptides and their functions based on sequence-derived information

Antimicrobial peptides (AMPs) called host defense peptides have existed among all classes of life with 5-100 amino acids generally and can kill mycobacteria, envelop viruses, bacteria, fungi, cancerous cells and so on. Owing to the non-drug resistance of AMP, it has been a wonderful agent to find novel therapies. Therefore, it is urgent to identify AMPs and predict their function in a high-throughput way. In this paper, we propose a cascaded computational model to identify AMPs and their functional type based on sequence-derived and life language embedding, called AMPFinder. Compared with other state-of-the-art methods, AMPFinder obtains higher performance both on AMP identification and AMP function prediction. AMPFinder shows better performance with improvement of F1-score (1.45%-6.13%), MCC (2.92%-12.86%) and AUC (5.13%-8.56%) and AP (9.20%-21.07%) on an independent test dataset. And AMPFinder achieve lower bias of R² on a public dataset by 10-fold cross-validation with an improvement of (18.82%-19.46%). The comparison with other state-of-the-art methods shows that AMP can accurately identify AMP and its function types. The datasets, source code and user-friendly application are available at https://github.com/abcair/AMPFinder.

Role of nanotechnology in the prolonged release of drugs by the subcutaneous route

INTRODUCTION

Subcutaneous physiology is distinct from other parenteral routes that benefit the administration of prolonged-release formulations. A prolonged-release effect is particularly convenient for treating chronic diseases because it is associated with complex and often prolonged posologies. Therefore, drug-delivery systems focused on nanotechnology are proposed as alternatives that can overcome the limitations of current therapeutic regimens and improve therapeutic efficacy.

AREAS COVERED

This review presents an updated systematization of nanosystems, focusing on their applications in highly prevalent chronic diseases. Subcutaneous-delivered nanosystem-based therapies comprehensively summarize nanosystems, drugs, and diseases and their advantages, limitations, and strategies to increase their translation into clinical applications. An outline of the potential contribution of quality-by-design (QbD) and artificial intelligence (AI) to the pharmaceutical development of nanosystems is presented.

EXPERT OPINION

Although recent academic research and development (R&D) advances in the subcutaneous delivery of nanosystems have exhibited promising results, pharmaceutical industries and regulatory agencies need to catch up. The lack of standardized methodologies for analyzing in vitro data from nanosystems for subcutaneous administration and subsequent in vivo correlation limits their access to clinical trials. There is an urgent need for regulatory agencies to develop methods that faithfully mimic subcutaneous administration and specific guidelines for evaluating nanosystems.

A User's Guide to Machine Learning for Polymeric Biomaterials

The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult a priori rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group's research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab.

Towards machine learning for hydrogel drug delivery systems

Hydrogel drug delivery system development is complex and laborious, and machine learning (ML) techniques hold great promise in accelerating the process. We highlight recent advances and strategies for data collection and ML, and we discuss the potential for and barriers to the broader use of ML for hydrogel drug delivery systems.

A simple array integrating machine learning for identification of flavonoids in red wines

Bioactive flavonoids, the major ingredients of red wines, have been proven to prevent atherosclerosis and cardiovascular disease due to their anti-inflammatory and anti-oxidant activity. However, flavonoids have proven challenging to identify, even when multiple approaches are combined. Hereby, a simple array was constructed to detect flavonoids by employing phenylboronic acid modified perylene diimide derivatives (PDIs). Through multiple non-specific interactions (hydrophilic, hydrophobic, charged, aromatic, hydrogen-bonded and reversible covalent interactions) with flavonoids, the fluorescence of PDIs can be modulated, and variations in intensity can be used to create fingerprints of flavonoids. This array successfully discriminated 14 flavonoids of diverse structures and concentrations with 100% accuracy, based on patterns in fluorescence intensity modulation, via optimized machine learning algorithms. As a result, this array demonstrated the parallel detection of 8 different types and origins of red wines with a high accuracy, revealing the excellent potential of the sensor array in food mixtures detection.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

Perspectives on development of biomedical polymer materials in artificial intelligence age

Polymer materials are widely used in biomedicine, chemistry and material science, whose traditional preparations are mainly based on experience, intuition and conceptual insight, having been applied to the development of many new materials, but facing great challenges due to the vast design space for biomedical polymers. So far, the best way to solve these problems is to accelerate material design through artificial intelligence, especially machine learning. Herein, this paper will introduce several successful cases, and analyze the latest progress of machine learning in the field of biomedical polymers, then discuss the opportunities of this novel method. In particular, this paper summarizes the material database, open-source determination tools, molecular generation methods and machine learning models that have been used for biopolymer synthesis and property prediction. Overall, machine learning could be more effectively deployed on the material design of biomedical polymers, and it is expected to become an extensive driving force to meet the huge demand for customized designs.

Emerging Role of Injectable Dipeptide Hydrogels in Biomedical Applications

Owing to their properties such as biocompatibility, tunable mechanical properties, permeability toward oxygen, nutrients, and the ability to hold a significant amount of water, hydrogels have wide applications in biomedical research. They have been engaged in drug delivery systems, 3D cell culture, imaging, and extracellular matrix (ECM) mimetics. Injectable hydrogels represent a major subset of hydrogels possessing advantages of site-specific conformation with minimal invasive techniques. It preserves the inherent properties of drug/biomolecules and is devoid of any side effects associated with surgery. Various polymeric materials utilized in developing injectable hydrogels are associated with the limitations of toxicity, immunogenicity, tedious manufacturing processes, and lack of easy synthetic tunability. Peptides are an important class of biomaterials that have interesting properties such as biocompatibility, stimuli responsiveness, shear thinning, self-healing, and biosignaling. They lack immunogenicity and toxicity. Therefore, numerous peptide-based injectable hydrogels have been explored in the past, and a few of them have reached the market. In recent years, minimalistic dipeptides have shown their ability to form stable hydrogels through cooperative noncovalent interactions. In addition to inherent properties of lengthy peptide-based injectable hydrogels, dipeptides have the unique advantages of low production cost, high synthetic accessibility, and higher stability. Given the instances of expanding significance of injectable peptide hydrogels in biomedical research and an emerging recent trend of dipeptide-based injectable hydrogels, a timely review on dipeptide-based injectable hydrogels shall highlight various aspects of this interesting class of biomaterials. This concise review that focuses on the dipeptide injectable hydrogel may stimulate the current trends of research on this class of biomaterial to translate its significance as interesting products for biomedical applications.

Nucleoside-Based Supramolecular Hydrogels: From Synthesis and Structural Properties to Biomedical and Tissue Engineering Applications

Supramolecular hydrogels are of great interest in tissue scaffolding, diagnostics, and drug delivery due to their biocompatibility and stimuli-responsive properties. In particular, nucleosides are promising candidates as building blocks due to their manifold noncovalent interactions and ease of chemical modification. Significant progress in the field has been made over recent years to allow the use of nucleoside-based supramolecular hydrogels in the biomedical field, namely drug delivery and 3D bioprinting. For example, their long-term stability, printability, functionality, and bioactivity have been greatly improved by employing more than one gelator, incorporating different cations, including silver for antibacterial activity, or using additives such as boric acid or even biomolecules. This now permits their use as bioinks for 3D printing to produce cell-laden scaffolds with specified geometries and pore sizes as well as a homogeneous distribution of living cells and bioactive molecules. We have summarized the latest advances in nucleoside-based supramolecular hydrogels. Additionally, we discuss their synthesis, structural properties, and potential applications in tissue engineering and provide an outlook and future perspective on ongoing developments in the field.

Machine Learning in Tissue Engineering

Machine learning (ML) and artificial intelligence have accelerated scientific discovery, augmented clinical practice, and deepened fundamental understanding of many biological phenomena. ML technologies have now been applied to diverse areas of tissue engineering research, including biomaterial design, scaffold fabrication, and cell/tissue modeling. Emerging ML-empowered strategies include machine-optimized polymer synthesis, predictive modeling of scaffold fabrication processes, complex analyses of structure-function relationships, and deep learning of spatialized cell phenotypes and tissue composition. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex and multivariate analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research. Impact statement Machine learning (ML) has accelerated scientific discovery and augmented clinical practice across multiple fields. Now, ML has driven exciting new paradigms in tissue engineering research, including machine-optimized biomaterial design, predictive modeling of scaffold fabrication, and spatiotemporal analysis of cell and tissue systems. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research.