DiZyme: Open-Access Expandable Resource for Quantitative Prediction of Nanozyme Catalytic Activity

AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity

Nanozymes are unique materials with many valuable properties for applications in biomedicine, biosensing, environmental monitoring, and beyond. In this work, we developed a machine learning (ML) approach to search for new nanozymes and deployed a web platform, DiZyme, featuring a state-of-the-art database of nanozymes containing 1210 experimental samples, catalytic activity prediction, and DiZyme Assistant interface powered by a large language model (LLM). For the first time, we enable the prediction of multiple catalytic activities of nanozymes by training an ensemble learning algorithm achieving R2 = 0.75 for the Michaelis-Menten constant and R2 = 0.77 for the maximum velocity on unseen test data. We envision an accurate prediction of multiple catalytic activities (peroxidase, oxidase, and catalase) promoting novel applications for a wide range of surface-modified inorganic nanozymes. The DiZyme Assistant based on the ChatGPT model provides users with supporting information on experimental samples, such as synthesis procedures, measurement protocols, etc. DiZyme (dizyme.aicidlab.itmo.ru) is now openly available worldwide.

ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities

The design of nanozymes with superior catalytic activities is a prerequisite for broadening their biomedical applications. Previous studies have exerted significant effort in theoretical calculation and experimental trials for enhancing the catalytic activity of nanozyme. Machine learning (ML) provides a forward-looking aid in predicting nanozyme catalytic activity. However, this requires a significant amount of human effort for data collection. In addition, the prediction accuracy urgently needs to be improved. Herein, we demonstrate that ChatGPT can collaborate with humans to efficiently collect data. We establish four qualitative models (random forest (RF), decision tree (DT), adaboost random forest (adaboost-RF), and adaboost decision tree (adaboost-DT)) for predicting nanozyme catalytic types, such as peroxidase, oxidase, catalase, superoxide dismutase, and glutathione peroxidase. Furthermore, we use five quantitative models (random forest (RF), decision tree (DT), Support Vector Regression (SVR), gradient boosting regression (GBR), and fully connected deep neuron network (DNN)) to predict nanozyme catalytic activities. We find that GBR model demonstrates superior prediction performance for nanozyme catalytic activities (R2 = 0.6476 for Km and R2 = 0.95 for Kcat). Moreover, an open-access web resource, AI-ZYMES, with a ChatGPT-based nanozyme copilot is developed for predicting nanozyme catalytic types and activities and guiding the synthesis of nanozyme. The accuracy of the nanozyme copilot's responses reaches more than 90% through the retrieval augmented generation. This study provides a new potential application for ChatGPT in the field of nanozymes.

A novel colorimetric and fluorometric dual-signal identification of organics and Baijiu based on nanozymes with peroxidase-like activity

Nanozymes were nanomaterials with enzymatic properties. They had diverse functions, adjustable catalytic activity, high stability, and easy large-scale production, attracting interest in biosensing. However, nanozymes were scarcely applied in Baijiu identification. Herein, a colorimetric and fluorometric dual-signal determination mediated by a nanozyme-H2O2-TMB system was developed for the first time to identify organics and Baijiu. Since the diverse peroxidase-like activity of nanozymes, resulted in different degrees of oxidized TMB. Based on this, 21 organics were identified qualitatively and quantitatively by colorimetric method with a rapid response (<12 min), broad linearity (0.0005-35 mM), and low detection limits (a minimum of 30 nM for glutaric acids). Furthermore, the fluorometric method exhibited excellent potential for accurate determination of organics, with detection ranges of 2-200 µmol/L (LOD: 0.22 µmol/L) for l-ascorbic acid and 2-300 µmol/L (LOD: 0.59 µmol/L) for guaiacol. Finally, the sensor was successfully applied to identify fake Baijiu and Baijiu from 16 different brands.

Machine learning in nanozymes: from design to application

Nanozymes, a distinctive class of nanomaterials endowed with enzyme-like activity and kinetics akin to enzyme-catalysed reactions, present several advantages over natural enzymes, including cost-effectiveness, heightened stability, and adjustable activity. However, the conventional trial-and-error methodology for developing novel nanozymes encounters growing challenges as research progresses. The advent of artificial intelligence (AI), particularly machine learning (ML), has ushered in innovative design approaches for researchers in this domain. This review delves into the burgeoning role of ML in nanozyme research, elucidating the advancements achieved through ML applications. The review explores successful instances of ML in nanozyme design and implementation, providing a comprehensive overview of the evolving landscape. A roadmap for ML-assisted nanozyme research is outlined, offering a universal guideline for research in this field. In the end, the review concludes with an analysis of challenges encountered and anticipates future directions for ML in nanozyme research. The synthesis of knowledge in this review aims to foster a cross-disciplinary study, propelling the revolutionary field forward.

Current Advances on the Single-Atom Nanozyme and Its Bioapplications

Nanozymes, a class of nanomaterials mimicking the function of enzymes, have aroused much attention as the candidate in diverse fields with the arbitrarily tunable features owing to the diversity of crystalline nanostructures, composition, and surface configurations. However, the uncertainty of their active sites and the lower intrinsic deficiencies of nanomaterial-initiated catalysis compared with the natural enzymes promote the pursuing of alternatives by imitating the biological active centers. Single-atom nanozymes (SAzymes) maximize the atom utilization with the well-defined structure, providing an important bridge to investigate mechanism and the relationship between structure and catalytic activity. They have risen as the new burgeoning alternative to the natural enzyme from in vitro bioanalytical tool to in vivo therapy owing to the flexible atomic engineering structure. Here, focus is mainly on the three parts. First, a detailed overview of single-atom catalyst synthesis strategies including bottom-up and top-down approaches is given. Then, according to the structural feature of single-atom nanocatalysts, the influence factors such as central metal atom, coordination number, heteroatom doping, and the metal-support interaction are discussed and the representative biological applications (including antibacterial/antiviral performance, cancer therapy, and biosensing) are highlighted. In the end, the future perspective and challenge facing are demonstrated.

Reaction Mechanisms and Kinetics of Nanozymes: Insights from Theory and Computation

"Nanozymes" usually refers to inorganic nanomaterials with enzyme-like catalytic activities. The research into nanozymes is one of the hot topics on the horizon of interdisciplinary science involving materials, chemistry, and biology. Although great progress has been made in the design, synthesis, characterization, and application of nanozymes, the study of the underlying microscopic mechanisms and kinetics is still not straightforward. Density functional theory (DFT) calculations compute the potential energy surfaces along the reaction coordinates for chemical reactions, which can give atomistic-level insights into the micro-mechanisms and kinetics for nanozymes. Therefore, DFT calculations have been playing an increasingly important role in exploring the mechanisms and kinetics for nanozymes in the past years. The calculations either predict the microscopic details for the catalytic processes to complement the experiments or further develop theoretical models to depict the physicochemical rules. In this review, the corresponding research progress is summarized. Particularly, the review focuses on the computational studies that closely interplay with the experiments. The relevant experimental results without DFT calculations will be also briefly discussed to offer a historic overview of how the computations promote the understanding of the microscopic mechanisms and kinetics of nanozymes.

Multienzyme-Like Nanozymes: Regulation, Rational Design, and Application

Nanomaterials with more than one enzyme-like activity are termed multienzymic nanozymes, and they have received increasing attention in recent years and hold huge potential to be applied in diverse fields, especially for biosensing and therapeutics. Compared to single enzyme-like nanozymes, multienzymic nanozymes offer various unique advantages, including synergistic effects, cascaded reactions, and environmentally responsive selectivity. Nevertheless, along with these merits, the catalytic mechanism and rational design of multienzymic nanozymes are more complicated and elusive as compared to single-enzymic nanozymes. In this review, the multienzymic nanozymes classification scheme based on the numbers/types of activities, the internal and external factors regulating the multienzymatic activities, the rational design based on chemical, biomimetic, and computer-aided strategies, and recent progress in applications attributed to the advantages of multicatalytic activities are systematically discussed. Finally, current challenges and future perspectives regarding the development and application of multienzymatic nanozymes are suggested. This review aims to deepen the understanding and inspire the research in multienzymic nanozymes to a greater extent.

Machine-Learning-Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes

Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity.

Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles

Nanoparticles (NPs) have been employed as drug delivery systems (DDSs) for several decades, primarily as passive carriers, with limited selectivity. However, recent publications have shed light on the emerging phenomenon of NPs exhibiting selective cytotoxicity against cancer cell lines, attributable to distinct metabolic disparities between healthy and pathological cells. This study revisits the concept of NPs selective cytotoxicity, and for the first time proposes a high-throughput in silico screening approach to massive targeted discovery of selectively cytotoxic inorganic NPs. In the first step, this work trains a gradient boosting regression model to predict viability of NP-treated cell lines. The model achieves mean cross-validation (CV) Q2 = 0.80 and root mean square error (RMSE) of 13.6. In the second step, this work develops a machine learning (ML) reinforced genetic algorithm (GA), capable of screening >14 900 candidates/min, to identify the best-performing selectively cytotoxic NPs. As proof-of-concept, DDS candidates for the treatment of liver cancer are screened on HepG2 and hepatocytes cell lines resulting in Ag NPs with selective toxicity score of 42%. This approach opens the door for clinical translation of NPs, expanding their therapeutic application to a wider range of chemical space of NPs and living organisms such as bacteria and fungi.

Deep Insight of Design, Mechanism, and Cancer Theranostic Strategy of Nanozymes

Since the discovery of enzyme-like activity of Fe3O4 nanoparticles in 2007, nanozymes are becoming the promising substitutes for natural enzymes due to their advantages of high catalytic activity, low cost, mild reaction conditions, good stability, and suitable for large-scale production. Recently, with the cross fusion of nanomedicine and nanocatalysis, nanozyme-based theranostic strategies attract great attention, since the enzymatic reactions can be triggered in the tumor microenvironment to achieve good curative effect with substrate specificity and low side effects. Thus, various nanozymes have been developed and used for tumor therapy. In this review, more than 270 research articles are discussed systematically to present progress in the past five years. First, the discovery and development of nanozymes are summarized. Second, classification and catalytic mechanism of nanozymes are discussed. Third, activity prediction and rational design of nanozymes are focused by highlighting the methods of density functional theory, machine learning, biomimetic and chemical design. Then, synergistic theranostic strategy of nanozymes are introduced. Finally, current challenges and future prospects of nanozymes used for tumor theranostic are outlined, including selectivity, biosafety, repeatability and stability, in-depth catalytic mechanism, predicting and evaluating activities.

Quantifying the Efficacy of Magnetic Nanoparticles for MRI and Hyperthermia Applications via Machine Learning Methods

Magnetic nanoparticles are a prospective class of materials for use in biomedicine as agents for magnetic resonance imagining (MRI) and hyperthermia treatment. However, synthesis of nanoparticles with high efficacy is resource-intensive experimental work. In turn, the use of machine learning (ML) methods is becoming useful in materials design and serves as a great approach to designing nanomagnets for biomedicine. In this work, for the first time, an ML-based approach is developed for the prediction of main parameters of material efficacy, i.e., specific absorption rate (SAR) for hyperthermia and r1 /r2 relaxivities in MRI, with parameters of nanoparticles as well as experimental conditions as descriptors. For that, a unique database with more than 980 magnetic nanoparticles collected from scientific articles is assembled. Using this data, several tree-based ensemble models are trained to predict SAR, r1 and r2 relaxivity. After hyperparameter optimization, models reach performances of R2 = 0.86, R2 = 0.78, and R2 = 0.75, respectively. Testing the models on samples unseen during the training shows no performance drops. Finally, DiMag, an open access resource created to guide synthesis of novel nanosized magnets for MRI and hyperthermia treatment with machine learning and boost development of new biomedical agents, is developed.

Machine Learning Study on Microwave-Assisted Batch Preparation and Oxygen Reduction Performance of Fe-N-C Catalysts

The Fe-N-C catalyst represents one of the most promising candidates for replacing platinum-based catalysts toward the oxygen reduction reaction. The pivotal factor in the successful integration of Fe-N-C catalysts within applications is the attainment of a large-scale production capability. Microwave-assisted pyrolysis offers various advantages, including enhanced energy and time efficiency, uniform heating, and high yield in single-batch processes. These characteristics render it exceptionally suitable for the mass production of catalysts. Through a synergistic approach involving machine learning techniques and microscopic characterization, we discerned performance trends and underlying mechanisms within batch-synthesized Fe-N-C catalysts under microwave-assisted preparation conditions. Machine learning analysis revealed that the precursor mass exerts the most substantial influence on product performance. Furthermore, microscopic characterization unveiled that these influencing factors impact catalyst performance by modulating the degree of agglomeration. Our research introduces an efficacious machine learning model for prognosticating performance and dissecting the influencing factors pertinent to Fe-N-C catalyst synthesis within a microwave system.

An explainable machine-learning approach for revealing the complex synthesis path-property relationships of nanomaterials

Machine learning (ML) models have recently shown important advantages in predicting nanomaterial properties, which avoids many trial-and-error explorations. However, complex variables that control the formation of nanomaterials exhibiting the desired properties still need to be better understood owing to the low interpretability of ML models and the lack of detailed mechanism information on nanomaterial properties. In this study, we developed a methodology for accurately predicting multiple synthesis parameter-property relationships of nanomaterials to improve the interpretability of the nanomaterial property mechanism. As a proof-of-concept, we designed glutathione-gold nanoclusters (GSH-AuNCs) exhibiting an appropriate fluorescence quantum yield (QY). First, we conducted 189 experiments and synthesized different GSH-AuNCs by varying the thiol-to-metal molar ratio and reaction temperature and time in reasonable ranges. The fluorescence QY of GSH-AuNCs could be systematically and independently programmed using different experimental parameters. We used limited GSH-AuNC synthesis parameter data to train an extreme gradient boosting regressor model. Moreover, we improved the interpretability of the ML model by combining individual conditional expectation, double-variable partial dependence, and feature interaction network analyses. The interpretability analyses established the relationship between multiple synthesis parameters and fluorescence QYs of GSH-AuNCs. The results represent an essential step towards revealing the complex fluorescence mechanism of thiolated AuNCs. Finally, we constructed a synthesis phase diagram exceeding 6.0 × 104 prediction variables for accurately predicting the fluorescence QY of GSH-AuNCs. A multidimensional synthesis phase diagram was obtained for the fluorescence QY of GSH-AuNCs by searching the synthesis parameter space in the trained ML model. Our methodology is a general and powerful complementary strategy for application in material informatics.

Revealing the intrinsic peroxidase-like catalytic mechanism of O-doped CoS2 nanoparticles

CoS2 nanoparticles (NPs) have shown promise as potential peroxidase (POD)-like catalysts, but the catalytic molecular mechanisms are largely unknown. Moreover, no study has adequately explored the influence of O-doping induced by the inevitable oxidation of CoS2 on their POD-like activity. Here, O-doped CoS2 NPs were prepared by a one-step method, and their intrinsic POD-like catalytic mechanism was investigated with a combined experimental and theoretical approach. The hydroxyl radical (˙OH) and the superoxide radical (O2˙-) have been found to play significant roles in the POD-like activity, and ˙OH is the major radical. The O-doping could reduce the transition-state energy barrier of H2O2 dissociation, thus promoting the decomposition of H2O2 to ˙OH and inducing the formation of O2˙-. Therefore, O-doping is an effective method for enhancing the catalytic activity of CoS2 NPs. Furthermore, due to the excellent oxidation property of ˙OH and O2˙-, this nanozyme exhibited efficient catalytic activity towards the degradation of organic dyes with H2O2. This manuscript provides a new inspiration for designing more promising anion-defective transition-metal sulfide nanozymes for different applications.

Rational Design Strategies for Nanozymes

Nanozymes constitute an emerging class of nanomaterials with enzyme-like characteristics. Over the past 15 years, more than 1200 nanozymes have been developed, and they have demonstrated promising potentials in broad applications. With the diversification and complexity of its applications, traditional empirical and trial-and-error design strategies no longer meet the requirements for efficient nanozyme design. Thanks to the rapid development of computational chemistry and artificial intelligence technologies, first-principles methods and machine-learning algorithms are gradually being adopted as a more efficient and easier means to assist nanozyme design. This review focuses on the potential elementary reaction mechanisms in the rational design of nanozymes, including peroxidase (POD)-, oxidase (OXD)-, catalase (CAT)-, superoxide dismutase (SOD)-, and hydrolase (HYL)-like nanozymes. The activity descriptors are introduced, with the aim of providing further guidelines for nanozyme active material screening. The computing- and data-driven approaches are thoroughly reviewed to give a proposal on how to proceed with the next-generation paradigm rational design. At the end of this review, personal perspectives on the prospects and challenges of the rational design of nanozymes are put forward, hoping to promote the further development of nanozymes toward superior application performance in the future.

Machine learning facilitating the rational design of nanozymes

As a component substitute for natural enzymes, nanozymes have the advantages of easy synthesis, convenient modification, low cost, and high stability, and are widely used in many fields. However, their application is seriously restricted by the difficulty of rapidly creating high-performance nanozymes. The use of machine learning techniques to guide the rational design of nanozymes holds great promise to overcome this difficulty. In this review, we introduce the recent progress of machine learning in assisting the design of nanozymes. Particular attention is given to the successful strategies of machine learning in predicting the activity, selectivity, catalytic mechanisms, optimal structures and other features of nanozymes. The typical procedures and approaches for conducting machine learning in the study of nanozymes are also highlighted. Moreover, we discuss in detail the difficulties of machine learning methods in dealing with the redundant and chaotic nanozyme data and provide an outlook on the future application of machine learning in the nanozyme field. We hope that this review will serve as a useful handbook for researchers in related fields and promote the utilization of machine learning in nanozyme rational design and related topics.

Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning

Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q²  = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.

Clear-Box Machine Learning for Virtual Screening of 2D Nanozymes to Target Tumor Hydrogen Peroxide

Targeting tumor hydrogen peroxide (H₂ O₂ ) with catalytic materials has provided a novel chemotherapy strategy against solid tumors. Because numerous materials have been fabricated so far, there is an urgent need for an efficient in silico method, which can automatically screen out appropriate candidates from materials libraries for further therapeutic evaluation. In this work, adsorption-energy-based descriptors and criteria are developed for the catalase-like activities of materials surfaces. The result enables a comprehensive prediction of H₂ O₂ -targeted catalytic activities of materials by density functional theory (DFT) calculations. To expedite the prediction, machine learning models, which efficiently calculate the adsorption energies for 2D materials without DFT, are further developed. The finally obtained method takes advantage of both interpretability of physics model and high efficiency of machine learning. It provides an efficient approach for in silico screening of 2D materials toward tumor catalytic therapy, and it will greatly promote the development of catalytic nanomaterials for medical applications.

Machine Learning Guided Discovery of Superoxide Dismutase Nanozymes for Androgenetic Alopecia

Androgenetic alopecia (AGA) is a common form of hair loss, which is mainly caused by oxidative stress induced dysregulation of hair follicles (HF). Herein, a highly efficient manganese thiophosphite (MnPS₃) based superoxide dismutase (SOD) mimic was discovered using machine learning (ML) tools. Remarkably, the IC₅₀ of MnPS₃ is 3.61 μg·mL^-1, up to 12-fold lower than most reported SOD-like nanozymes. Moreover, a MnPS₃ microneedle patch (MnMNP) was constructed to treat AGA that could diffuse into the deep skin where HFs exist and remove excess reactive oxygen species. Compared with the widely used minoxidil, MnMNP exhibits higher ability on hair regeneration, even at a reduced frequency of application. This study not only provides a general guideline for the accelerated discovery of SOD-like nanozymes by ML techniques, but also shows a great potential as a next generation approach for rational design of nanozymes.

Artificial intelligence to bring nanomedicine to life

The technology of drug delivery systems (DDSs) has demonstrated an outstanding performance and effectiveness in production of pharmaceuticals, as it is proved by many FDA-approved nanomedicines that have an enhanced selectivity, manageable drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, the rational design and high-throughput development of nanomaterial-based DDSs for specific purposes is far from a routine practice and is still in its infancy, mainly due to the limitations in scientists' capabilities to effectively acquire, analyze, manage, and comprehend complex and ever-growing sets of experimental data, which is vital to develop DDSs with a set of desired functionalities. At the same time, this task is feasible for the data-driven approaches, high throughput experimentation techniques, process automatization, artificial intelligence (AI) technology, and machine learning (ML) approaches, which is referred to as The Fourth Paradigm of scientific research. Therefore, an integration of these approaches with nanomedicine and nanotechnology can potentially accelerate the rational design and high-throughput development of highly efficient nanoformulated drugs and smart materials with pre-defined functionalities. In this Review, we survey the important results and milestones achieved to date in the application of data science, high throughput, as well as automatization approaches, combined with AI and ML to design and optimize DDSs and related nanomaterials. This manuscript mission is not only to reflect the state-of-art in data-driven nanomedicine, but also show how recent findings in the related fields can transform the nanomedicine's image. We discuss how all these results can be used to boost nanomedicine translation to the clinic, as well as highlight the future directions for the development, data-driven, high throughput experimentation-, and AI-assisted design, as well as the production of nanoformulated drugs and smart materials with pre-defined properties and behavior. This Review will be of high interest to the chemists involved in materials science, nanotechnology, and DDSs development for biomedical applications, although the general nature of the presented approaches enables knowledge translation to many other fields of science.