α-Umpolung/Michael Addition/Quaternization Tandem Reaction to provide α-Imido-ß-phosphonium Propanoates with Broad Spectrum of Biological Activity.

This paper has been supported by the Kazan Federal University Strategic Academic Leadership Program ('PRIORITY-2030'). HRMS data were obtained in the CSF-SAC FRC KSC RAS by support of the State Assignment of the Federal Research Center "Kazan Scientific Center", Russian Academy of Sciences. A.D.V, conducted studies of anticancer activity with financial support form the government assignment for FRC Kazan Scientific Center of RAS.

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.

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.

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.

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.

Inverse Material Search and Synthesis Verification by Hand Drawings via Transfer Learning and Contour Detection.

Nano- and micromaterials of various morphologies and compositions have extensive use in many different areas. However, the search for procedures giving custom nanomaterials with the desired structure, shape, and size remains a challenge and is often implemented by manual article screening. Here, for the first time, scanning and transmission electron microscopy inverse image search and hand drawing-based search via transfer learning are developed, namely, VGG16 convolution neural network repurposing for image features extraction and image similarity determination. Moreover, the case use of this platform is demonstrated on the calcium carbonate system, where the data are acquired by random high throughput experimental synthesis, and on Au nanoparticles data extracted from the articles. This approach can be used for advanced nanomaterials search, synthesis procedure verification, and can be further combined with machine learning solutions to provide data-driven nanomaterials discovery.

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

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 of >300 existing inorganic nanozymes is developed by data collection from >100 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 R2  = 0.796 for Kcat and R2  = 0.627 for Km . Experiment-confirmed unknown nanozyme activity prediction is also demonstrated. Moreover, the DiZyme expandable, open-access resource containing the database, predictive algorithm, and visualization tool is developed to boost novel nanozyme discovery worldwide (https://dizyme.net).

Nanomaterial Shape Influence on Cell Behavior.

Nanomaterials are proven to affect the biological activity of mammalian and microbial cells profoundly. Despite this fact, only surface chemistry, charge, and area are often linked to these phenomena. Moreover, most attention in this field is directed exclusively at nanomaterial cytotoxicity. At the same time, there is a large body of studies showing the influence of nanomaterials on cellular metabolism, proliferation, differentiation, reprogramming, gene transfer, and many other processes. Furthermore, it has been revealed that in all these cases, the shape of the nanomaterial plays a crucial role. In this paper, the mechanisms of nanomaterials shape control, approaches toward its synthesis, and the influence of nanomaterial shape on various biological activities of mammalian and microbial cells, such as proliferation, differentiation, and metabolism, as well as the prospects of this emerging field, are reviewed.

Synthesis and structure of a complex of copper(I) with l-cysteine and chloride ions containing Cu12S6 nanoclusters.

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-di-chlorido-hexa-kis-(µ4-l-cysteinato)hexa-deca-copper] polyhydrate]. The copper atoms are linked by thiol-ate groups to form Cu12S6 nanoclusters that take the form of a tetra-kis cubocta-hedron, made up of a Cu12 cubo-octa-hedral subunit that is augmented by six sulfur atoms that are located symmetrically atop of each of the Cu4 square units of the Cu12 cubo-octa-hedron. The six S atoms thus form an octa-hedral 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 tetra-hedral fashion, thus creating an infinite diamond-like net of Cu12S6Cl4(CuCl3)4 nanoclusters. The network thus formed results in large channels occupied by solvent mol-ecules that are mostly too ill-defined to model. The content of the voids, believed to be water mol-ecules, 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 carb-oxy groups point outwards and presumably form O-H⋯O hydrogen bonds with the unresolved water mol-ecules 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.

Nanoparticle-Based Approaches towards the Treatment of Atherosclerosis.

Atherosclerosis, being an inflammation-associated disease, represents a considerable healthcare problem. Its origin remains poorly understood, and at the same time, it is associated with extensive morbidity and mortality worldwide due to myocardial infarctions and strokes. Unfortunately, drugs are unable to effectively prevent plaque formation. Systemic administration of pharmaceuticals for the inhibition of plaque destabilization bears the risk of adverse effects. At present, nanoscience and, in particular, nanomedicine has made significant progress in both imaging and treatment of atherosclerosis. In this review, we focus on recent advances in this area, discussing subjects such as nanocarriers-based drug targeting principles, approaches towards the treatment of atherosclerosis, utilization of theranostic agents, and future prospects of nanoformulated therapeutics against atherosclerosis and inflammatory diseases. The focus is placed on articles published since 2015 with additional attention to research completed in 2019-2020.

One-pot synthesis of template-free hollow anisotropic CaCO3 structures: towards inorganic shape-mimicking drug delivery systems.

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 CaCO3-based pH-sensitive DDSs with controllable anisotropy and click-release behavior.

The kinetics and mechanisms of reactions in the flow systems glycine-sodium trimetaphosphate-imidazoles: the crucial role of imidazoles in prebiotic peptide syntheses.

The kinetics of oligopeptides formation in the flow systems glycine-sodium trimetaphosphate-imidazole/N-methylimidazole at thermocyclic regime has been investigated by HPLC and 31P NMR methods in the ranges of temperature from 45 to 90 °C and pH from 8.5 to 11.5. Detailed reaction mechanisms have been proposed and justified by quantum chemical calculations using DFT method at the CAM-B3LYP/TZVP level with accounting solvent effect by the C-PCM model. A new imidazole catalysis mechanism by which imidazole reacts with cyclic N,O-phosphoryl glycine giving N-imidazolyl-O-glycyl phosphate as a key intermediate was proposed and validated. It is emphasized that while in the absence of imidazoles, prebiotic activation of amino acids occurs at the N-terminus, in the presence of imidazoles it shifts to the O-terminus. This means that in the peptide elongation N-imidazolyl-O-aminoacyl phosphates play in prebiotic systems the outstanding role similar to that of aminoacyl adenylates formed at the ATP and aminoacyl-tRNA synthetases presence in biosystems. The new crucial role of imidazoles in prebiotic evolution has been noticed. The systems used and modes of their conversion can be good models for prebiotic peptide syntheses in a flow thermocyclic regime.

Prebiotic Syntheses Under Shock in the Water - Formamide - Potassium Bicarbonate - Sodium Hydroxide System.

Syntheses under shock in nitrogen bubbled samples of the water - formamide - bicarbonate - sodium hydroxide system at pH 8.63, 9.46 and 10.44 were performed in the stainless steel preservation capsules. The maximum temperature and pressure in the capsules reached 545 K and 12.5 GPa respectively. Using the LC-MS-MS analysis, the 21 synthesis products have been identified, including amines and polyamines, carboxamide, acetamide and urea derivatives, compounds containing aniline, pyrrolidine, pyrrole, imidazole, as well as alcohol groups. It was found that the Fischer-Tropsch-type syntheses with catalysis on the surface of the stainless steel of the conservation capsule associated with the adsorbed hydrogen cyanide reactions and transamidation processes play the main role in the shock syntheses. Formation reactions of all the above-mentioned compounds have been suggested. It was proposed that hydrogen cyanide, ammonia, isocyanic acid, aminonitrile, aminoacetonitrile, as well as adsorbed species H(a), CH(a), CH2(a), CHOH(a), NH2(a) and H2CNH(a) are especially important for the formation of the products. A reduction reaction of adsorbed bicarbonate with hydrogen to formaldehyde has been first postulated. In the studied system also classical reactions take place - Wöhler's synthesis of urea and Butlerov's synthesis of methenamine. It was suggest that material of meteorites may be an effective catalyst in the Fischer-Tropsch-type syntheses at falling of the iron-nickel meteorites in the water - formamide regions on the early Earth. It was concluded that life could have originated due to the impact of meteorites on alkaline water-formamide lakes located near volcanoes on the early Earth.

Structure of copper(II) complexes grown from ionic liquids - 1-ethyl-3-methyl-imidazolium acetate or chloride.

Crystals of four new copper(II) complexes have been grown from copper(II) acetate/chloride-1-ethyl-3-methyl-imidazolium acetate/chloride-water systems and characterized by X-ray analysis. The first complex, bis-(1-ethyl-3-methyl-imidazolium) tetra-µ-acetato-bis[chloridocuprate(II)], [Emim]2[Cu2(C2H3O2)4Cl2] (1) (Emim is 1-ethyl-3-methyl-imidazolium, C6H11N2), contains [Cu2(C2H3O2)4Cl2]2- coordination anions with a paddle-wheel structure and ionic liquid cations. Two of the synthesized complexes are one-dimensional polymers, namely catena-poly[1-ethyl-3-methyl-imidazolium [[tetra-µ-acetato-dicuprate(II)]-µ-chlorido] monohydrate], {[Emim][Cu2(C2H3O2)4Cl]·H2O} n (2), and catena-poly[1-ethyl-3-methyl-imidazolium [[tetra-µ-acetato-dicuprate(II)]-µ-acetato]], {[Emim][Cu2(C2H3O2)5]} n (3). In these compounds, the Cu2(C2H3O2)4 units with a paddle-wheel structure are connected to each other through chloride (in 2) or acetate (in 3) anions to form parallel chains, between which cations of ionic liquid are situated. The last compound, bis-(1-ethyl-3-methyl-imidazolium) tetra-µ-acetato-bis[aquacopper(II)] tetra-µ-acetato-bis[acetatocuprate(II)] dihydrate, [Emim]2[Cu2(C2H3O2)4(H2O)2][Cu2(C2H3O2)6]·2H2O (4), contains two different binuclear coordination units (neutral and anionic), connected through hydrogen bonds between water mol-ecules and acetate ions.

Hydration of copper(II) amino acids complexes.

Hydration of the copper(II) bis-complexes with glycine, serine, lysine, and aspartic acid was studied by DFT and MD simulation methods. The distances between copper(II) and water molecules in the 1st and 2nd coordination shells, the average number of water molecules and their mean residence times in the hydration shells were calculated. Good agreement was observed between the values obtained and those found by DFT and NMR relaxation methods. Influence of the functional groups of the ligands and the cis-trans isomerism of the complexes on the structural and dynamical parameters of the hydration shells was displayed and explained. Analysis of the MD trajectories reveals the competition for a copper(II) axial position between water molecules or water molecules and the functional chain groups of the ligands and confirms the suggestion on the pentacoordination of copper(II) in such complexes. MD simulations show that only one axial position of Cu(II) is basically occupied at each time step while in average the coordination number more than 5 is observed. © 2017 Wiley Periodicals, Inc.

Structure and Dynamics of Solvation Shells of Copper(II) Complexes with N,O-Containing Ligands.

EPR, NMR relaxation methods, and DFT calculations were jointly used to investigate the structural and dynamical characteristics of solvation shells of copper(II) complexes with iminodiacetic acid, glycylglycine, and glycyglycylglycine in comparison with the copper(II) bis-glycinate studied previously. A strong trans influence of deprotonated peptide nitrogen was revealed in EPR spectra parameters of copper(II) complexes with oligopeptides. With models of the experimental NMRD data and literature X-ray structural information, it was suggested that only one water molecule coordinates in axial position of copper(II) complexes with glycine and di- and triglycine (Cu(Gly)2, Cu(GGH(-1)), and Cu(GGGH(-2))(-)), and the copper ion in these complexes is pentacoordinated, while in the iminodiacetate complex, Cu(IDA), both apical positions can be occupied by solute molecules. The obtained structural results were confirmed by DFT calculations of structures of studied compounds using different functionals and basis sets. It was shown that the donor ability of equatorial ligands and trans influence have an effect on the characteristics of the axial water bond. With increasing donor strength of equatorial ligands, pentacoordination of copper(II) complexes in water solutions becomes more preferable.

Study of structural and dynamic characteristics of copper(II) amino acid complexes in solutions by combined EPR and NMR relaxation methods.

Structural features and dynamical behaviour of the copper(ii) bis-complexes with glycine, d-alanine, d-valine, l-serine, l-aspartic acid, l-glutamic acid, l-lysine, l-proline, and sarcosine were studied by combined EPR and NMR relaxation methods. The cis and trans isomers were unambiguously assigned and characterized by EPR data. It was found that addition of a salt background has an influence on the cis-trans isomer equilibrium in favour of the formation of the cis isomer. By comparison of NMRD, DFT computations, and structural data it was shown that only one water molecule is coordinated in the axial position of these complexes. The increased exchange rates of this molecule found for Cu(l-Asp)2(2-), Cu(l-Glu)2(2-), Cu(l-LysH)2(2+), and Cu(l-Pro)2 were attributed to its pushing out by side chain groups of the ligands. By simulation of NMRD profiles an increase of lifetimes of the copper(ii) 2nd coordination sphere water molecules was revealed in the presence of additional carboxylic, alcoholic, or ammonium groups of the ligands, as well as the pyrrolidine ring of proline. The very short lifetimes of the 2nd coordination sphere water molecules (4-13 ps at 298 K) were explained in terms of the Frank-Wen structural model by the existence of cavities which draw in quickly enough water molecules from the 2nd coordination sphere.

The C-H bond activation in 1-ethyl-3-methylimidazolium acetate-copper(II) acetate-water-air (dioxygen) systems.

Ionic liquid (1-ethyl-3-methylimidazolium acetate, [C2C1im][AcO])-copper(ii) diacetate monohydrate-water-air (O2) systems have been investigated by (13)C NMR, EPR, spectrophotometry, HPLC, and synthetic chemistry methods at different temperatures. The C-H bond activation of [C2C1im](+) with the formation of the unusual dication 1,1'-diethyl-3,3'-dimethyl-2,2'-biimidazolium ([(C2C1im)2](2+)) at 50 °C and 1-ethyl-3-methyl-1H-imidazol-2(3H)-one (C2C1imO) at 50-85 °C was revealed. Two new complexes with the above compounds, [(C2C1im)2][Cu(AcO)4] and Cu2(AcO)4(C2C1imO)2, were isolated from the systems and characterized by X-ray structural analysis. Catalytic cycles with the participation of copper(ii) acetate and dioxygen and the production of [(C2C1im)2](2+) and C2C1imO have been proposed. The catalysis presumably includes the formation of the Cu(II)(O2)Cu(II) active centre with µ-η(2):η(2)-peroxide bridging in analogy with tyrosinase and catechol oxidase activity.