Quantum machine learning for image classification

Image recognition and classification are fundamental tasks with diverse practical applications across various industries, making them critical in the modern world. Recently, machine learning models, particularly neural networks, have emerged as powerful tools for solving these problems. However, the utilization of quantum effects through hybrid quantum-classical approaches can further enhance the capabilities of traditional classical models. Here, we propose two hybrid quantum-classical models: a neural network with parallel quantum layers and a neural network with a quanvolutional layer, which address image classification problems. One of our hybrid quantum approaches demonstrates remarkable accuracy of more than 99% on the MNIST dataset. Notably, in the proposed quantum circuits all variational parameters are trainable, and we divide the quantum part into multiple parallel variational quantum circuits for efficient neural network learning. In summary, our study contributes to the ongoing research on improving image recognition and classification using quantum machine learning techniques. Our results provide promising evidence for the potential of hybrid quantum-classical models to further advance these tasks in various fields, including healthcare, security, and marketing.Comment: 9 pages, 8 figure

Integrative Approaches for Predicting In Vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-Term Biological Assays

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity

Chemistry-wide association studies (CWAS) to determine joint toxicity effects of co-occurring chemical features

Individual structural alerts often fail to accurately predict chemical toxicity as they tend to overlook the moderating effects of other co-occurring alerts. Features are said to have statistical interaction effects when one changes or modulates the effect of another on the target property. Here we introduce Chemistry-Wide Association Study (CWAS; by analogy with GWAS in genomics) to systematically elicit the individual and interaction effects of chemical features on the target property

Predictive Modeling of Chemical Hazard by Integrating Numerical Descriptors of Chemical Structures and Short-term Toxicity Assay Data

Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR–like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage

An updated review on drug-induced cholestasis: Mechanisms and investigation of physicochemical properties and pharmacokinetic parameters

Drug-induced cholestasis is an important form of acquired liver disease and is associated with significant morbidity and mortality. Bile acids are key signaling molecules, but they can exert toxic responses when they accumulate in hepatocytes. This review focuses on the physiological mechanisms of drug-induced cholestasis associated with altered bile acid homeostasis due to direct (e.g. bile acid transporter inhibition) or indirect (e.g. activation of nuclear receptors, altered function/expression of bile acid transporters) processes. Mechanistic information about the effects of a drug on bile acid homeostasis is important when evaluating the cholestatic potential of a compound, but experimental data often are not available. The relationship between physicochemical properties, pharmacokinetic parameters, and inhibition of the bile salt export pump (BSEP) among seventy-seven cholestatic drugs with different pathophysiological mechanisms of cholestasis (i.e. impaired formation of bile vs. physical obstruction of bile flow) was investigated. The utility of in silico models to obtain mechanistic information about the impact of compounds on bile acid homeostasis to aid in predicting the cholestatic potential of drugs is highlighted

Quantitative structure - property relationship modeling of remote liposome loading of drugs

Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a dataset including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and five-fold external validation. The external prediction accuracy for binary models was as high as 91–96%; for continuous models the mean coefficient R2 for regression between predicted versus observed values was 0.76–0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments

Integrative Chemical–Biological Read-Across Approach for Chemical Hazard Classification

Traditional read-across approaches typically rely on the chemical similarity principle to predict chemical toxicity; however, the accuracy of such predictions is often inadequate due to the underlying complex mechanisms of toxicity. Here we report on the development of a hazard classification and visualization method that draws upon both chemical structural similarity and comparisons of biological responses to chemicals measured in multiple short-term assays (”biological” similarity). The Chemical-Biological Read-Across (CBRA) approach infers each compound's toxicity from those of both chemical and biological analogs whose similarities are determined by the Tanimoto coefficient. Classification accuracy of CBRA was compared to that of classical RA and other methods using chemical descriptors alone, or in combination with biological data. Different types of adverse effects (hepatotoxicity, hepatocarcinogenicity, mutagenicity, and acute lethality) were classified using several biological data types (gene expression profiling and cytotoxicity screening). CBRA-based hazard classification exhibited consistently high external classification accuracy and applicability to diverse chemicals. Transparency of the CBRA approach is aided by the use of radial plots that show the relative contribution of analogous chemical and biological neighbors. Identification of both chemical and biological features that give rise to the high accuracy of CBRA-based toxicity prediction facilitates mechanistic interpretation of the models

The Use of Pseudo-Equilibrium Constant Affords Improved QSAR Models of Human Plasma Protein Binding

To develop accurate in silico predictors of Plasma Protein Binding (PPB)

Effective Space Confinement by Inverse Miniemulsion for the Controlled Synthesis of Undoped and Eu3+^{3+} -Doped Calcium Molybdate Nanophosphors: A Systematic Comparison with Batch Synthesis

The possibility to precisely control reaction outcomes for pursuing materials with well-defined features is a main endeavor in the development of inorganic materials. Confining reactions within a confined space, such as nanoreactors, is an extremely promising methodology which allows to ensure control over the final properties of the material. An effective room temperature inverse miniemulsion approach for the controlled synthesis of undoped and Eu3+-doped calcium molybdate crystalline nanophosphors was developed. The advantages and the efficiency of confined space in terms of controlling nanoparticle features like size, shape, and functional properties are highlighted by systematically comparing miniemulsion products with calcium molybdate particles obtained without confinement from a typical batch synthesis. A relevant beneficial impact of space confinement by miniemulsion nanodroplets is observed on the control of size and shape of the final nanoparticles, resulting in 12 nm spherical nanoparticles with a narrow size distribution, as compared to the 58 nm irregularly shaped and aggregated particles from the batch approach (assessed by TEM analysis). Further considerable effects of the confined space for the miniemulsion samples are found on the doping effectiveness, leading to a more homogeneous distribution of the Eu$^{3+}$ ions into the molybdate host matrix, without segregation (assessed by PXRD, XAS, and ICP-MS). These findings are finally related to the photoluminescence properties, which are evidenced to be closely dependent on the Eu$^{3+}$ content for the miniemulsion samples, as an increase of the relative intensity of the direct f–f excitation and a shortening of the lifetime (from 0.901 ms for 1 at. % to 0.625 ms for 7 at. % samples) with increasing Eu$^{3+}$ content are observed, whereas no relationship between these parameters and the Eu$^{3+}$ content is evidenced for the batch samples. All these results are ascribed to the uniform and controlled crystallization occurring inside each miniemulsion nanodroplet, as opposed to the less controlled nucleation and growth for a classic nonconfined approach

Acquisition of English Argument Patterns By Russian EFL Students

Foreign (especially English) language learning has witnessed growing popularity in Russia over the last decades due to the enormous change in economic, political, legal, and cultural domains in the current period. The increasing need for good English speaking and writing skills put forward a demand for the accurate use of lexical items and grammatical structures by those who study English as a foreign language (EFL). Lexical and grammatical accuracy acquires a crucial importance in reasoning and argumentation. A slapdash word or syntactic construction in the argument structure may submit the listener to a conclusion, which is completely different from what the speaker implied. Such issues may be particularly frustrating in academic, legal, business, medical, and other types of institutional discourse. The rules of Aristotelian logic, underlying the good majority of reasoning structures, are generic. Therefore, it is a certain difference between the two languages, native (Russian) and foreign (English), that makes Russian students of English misinterprete logical chains and use irrelevant lexical items and grammatical constructions