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Enzymes suffer from high cost, complex purification, and low stability. Development of low-cost artificial enzymes of comparative or higher effectiveness is desired. Given its complexity, it is desired to presume their activities prior to experiments. While computational approaches demonstrate success in modeling nanozyme activities, they require assumptions about the system to be made. Machine learning (ML) is an alternative approach towards data-driven material property prediction achieving high performance even on multicomponent complex systems. Despite the growing demand for customized nanozymes, there is no open access nanozyme database. Here, a user-friendly expandable database of300 existing inorganic nanozymes is developed by data collection from100 articles. Data analysis is performed to reveal the features responsible for catalytic activities of nanozymes, and new descriptors are proposed for its ML-assisted prediction. A random forest regression (RFR) model for evaluation of nanozyme peroxidase activity is developed and optimized by correlation-based feature selection and hyperparameter tuning, achieving performance up to R

Nanomaterials of various morphologies and chemistry have an extensive use as photonic devices, advanced catalysts, sorbents for water purification, agrochemicals, platforms for drug delivery as well as imaging systems to name a few. However, search for synthesis routes giving custom nanomaterials for particular needs with the desired structure, shape, and size remains a challenge and is often implemented by manual research articles screening. Here, we develop for the first time scanning and transmission electron microscopy (SEM/TEM) reverse image search and hand drawing-based search via transfer learning (TL), namely, VGG16 convolutional neural network (CNN) repurposing for image features extraction and subsequent image similarity determination. Moreover, we demonstrate case use of this platform on calcium carbonate system, where sufficient amount of data was acquired by random high throughput multiparametric synthesis, as well as on Au nanoparticles (NPs) data extracted from the articles. This approach can be not only used for advanced nanomaterials search and synthesis procedure verification, but also can be further combined with machine learning (ML) solutions to provide data-driven novel nanomaterials discovery.

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.

A cluster containing copper(I), l-cysteine and chloride ions was synthesized and characterized by X-ray diffraction and FTIR spectroscopy., The title hydrated copper(I)–l-cysteine–chloride complex has a polymeric structure of composition {[Cu16(CysH2)6Cl16]·xH2O}n [CysH2 = HO2CCH(NH3 +)CH2S− or C3H7NO2S], namely, poly[[tetra-μ3-chlorido-deca-μ2-chlorido-di­chlorido­hexa­kis­(μ4-l-cysteinato)hexa­deca­copper] polyhydrate]. The copper atoms are linked by thiol­ate groups to form Cu12S6 nanoclusters that take the form of a tetra­kis cubocta­hedron, made up of a Cu12 cubo-octa­hedral subunit that is augmented by six sulfur atoms that are located symmetrically atop of each of the Cu4 square units of the Cu12 cubo-octa­hedron. The six S atoms thus form an octa­hedral subunit themselves. The exterior of the Cu12S6 sphere is decorated by chloride ions and trichlorocuprate units. Three chloride ions are coordinated in an irregular fashion to trigonal Cu3 subunits of the nanocluster, and four trigonal CuCl3 units are bonded via each of their chloride ions to a copper ion on the Cu12S6 sphere. The trigonal CuCl3 units are linked via Cu2Cl2 bridges covalently connected to equivalent units in neighboring nanoclusters. Four such connections are arranged in a tetra­hedral fashion, thus creating an infinite diamond-like net of Cu12S6Cl4(CuCl3)4 nanoclusters. The network thus formed results in large channels occupied by solvent mol­ecules that are mostly too ill-defined to model. The content of the voids, believed to be water mol­ecules, was accounted for via reverse Fourier-transform methods using the SQUEEZE algorithm [Spek (2015 ▸). Acta Cryst. C71, 9–18]. The protonated amino groups of the cysteine ligands are directed away from the sphere, forming N—H⋯Cl hydrogen bonds with chloride-ion acceptors of their cluster. The protonated carb­oxy groups point outwards and presumably form O—H⋯O hydrogen bonds with the unresolved water mol­ecules of the solvent channels. Disorder is observed in one of the two crystallographically unique [Cu16(CysH2)6Cl16] segments for three of the six cysteine anions.

A major obstacle in the introduction of nanoformulated drugs has been the fact that the shape of the drug delivery systems (DDSs) - the most important parameter driven by the nature of viruses and bacteria - remains almost out-of-scope in artificial systems. Here we propose a potential solution for this problem by developing a template-free approach for the formulation of hollow bacteria-like CaCO