The European Physical Journal E最新文献

This research investigates the anticipated physicochemical and topological properties of compounds such as drug complexity (C), molecular weight (MW), and topological polar surface area (TPSA) using quantitative structure–activity relationship (QSAR) analysis. Several machine learning models, including Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and Gradient Boosting, were developed to improve prediction accuracy using topological indices. The datasets were combined with appropriate topological indices for individual compounds. Model performance was evaluated using Mean Squared Error (MSE) and (R^2) score after hyperparameter tuning via GridSearchCV. Ridge and Lasso Regression models stood out due to their lowest Test MSE averages (3617.74 and 3540.23, respectively) and highest (R^2) scores (0.9322 and 0.9374, respectively), demonstrating their effectiveness in handling multicollinearity and preventing overfitting. Linear Regression also performed robustly, achieving an MSE of 5249.97 and an (R^2) of 0.8563, highlighting the suitability of simpler models for datasets with inherent linear relationships. While Random Forest and Gradient Boosting Regression are capable of capturing nonlinear relationships, their performance varied. Random Forest Regression achieved an MSE of 6485.45 and an (R^2) of 0.6643, while Gradient Boosting initially performed poorly with an MSE of 4488.04 and an (R^2) of 0.5659. After fine-tuning Gradient Boosting with an expanded hyperparameter grid, its performance improved significantly, achieving a Test MSE of 1494.74 and an (R^2) of 0.9171. However, it still ranked fourth, suggesting that simpler models like Linear, Ridge, and Lasso Regression may be better suited for this dataset. This work emphasizes the significance of accurate model selection and optimization in QSAR analysis, demonstrating how these approaches can be used to develop dependable predictive models in computational drug discovery and cheminformatics.

A machine learning pipeline for predicting physicochemical and topological properties of chemical compounds using QSAR analysis. The process begins with compound data collection from PubChem, followed by data preprocessing, feature engineering, and feature selection. The selected features are used to train various regression models-including Linear, Ridge, Lasso, Random Forest, and Gradient Boosting Regression-evaluated using MSE and (R^2) metrics for performance comparison.caption for the graphical abstract: Caption for Graphical Abstract: A machine learning pipeline for predicting physicochemical and topological properties of chemical compounds using QSAR analysis. The process begins with compound data collection from PubChem, followed by data preprocessing, feature engineering, and feature selection. The selected features are used to train various regression models-incl

Suspension of particles in a fluid solvent are ubiquitous in nature, for example water mixed with sugar or bacteria self-propelling through mucus. Particles create local flow perturbations that can modify drastically the effective (homogenized) bulk properties of the fluid. Understanding the link between the properties of particles and the fluid solvent, and the effective properties of the medium is a classical problem in fluid mechanics. Here we study a special case of a two-dimensional model of a suspension of undeformable particles in a liquid crystal solvent. In the dilute regime, we calculate asymptotic solutions of the perturbations of the velocity and director fields and derive an explicit formula for an effective shear viscosity of the liquid crystal medium. Such effective shear viscosity increases linearly with the area fraction of particles, similar to Einstein formula but with a different prefactor. We provide explicit asymptotic formulas for the dependence of this prefactor on the material parameters of the solvent. Finally, we identify a case of decreasing the effective viscosity by increasing the magnitude of the shear-flow alignment coefficient of the liquid crystal solvent.

Collective behavior is all around us, from flocks of birds to schools of fish. These systems are immensely complex, which makes it pertinent to study their behavior through minimal models. We introduce such a minimal model for cohesive and aligning self-propelled particles in which group cohesion is established through additive, non-reciprocal torques. These torques cause a particle’s orientation vector to turn toward its neighbor so that it aligns with the separation vector. We additionally incorporate an alignment torque, which competes with the cohesive torque in the same spatial range. By changing the strength and range of these torque interactions, we uncover six states which we distinguish via their static and dynamic properties: a disperse state, a multiple worm state, a line state, a persistent worm state, a rotary worm state, and an aster state. Their occurrence strongly depends on initial conditions and stochasticity, so the model exhibits multistabilities. A number of the states exhibit collective dynamics which are reminiscent of those seen in nature.

Supervised machine learning methods like random forests and extreme gradient boosting plays an important role in drug development for predicting bioactivity and resolving structure-activity correlations. These approaches use topological descriptors in the study of polycyclic aromatic hydrocarbons that represent molecular structural characteristics to enhance the prediction capacity of quantitative structure–property relationships (QSPR). The objective is to identify the physoichemical properties such as density, boiling point, flash point, enthalpy, polarizability, surface tension, molar volume, molecular weight and complexity that significantly impact physicochemical attributes. The combination of machine learning and QSPR also demonstrates the potential of computational techniques in drug development. Then effective algorithms are constructed to express the link between the eccentricity-based topological indices and the physicochemical characteristics of each of the polycyclic aromatic hydrocarbons, which grows our understanding of their behavior and paves the way for future development of environmental forecasting techniques and toxicological evaluations of polycyclic aromatic hydrocarbons.

We investigate the 2D contact problem of sliding a non-cylindrical punch on a viscoelastic halfplane, assuming a power law shape (left| xright| ^{k}) with (k>2). We find with a full boundary element numerical solution that the Persson analytical solution for friction, which works well for the cylindrical punch case assuming the pressure remains identical in form to the elastic case, in this case leads to significant qualitative errors. However, we find that the friction coefficient follows a much simpler trend; namely, we can use as a first approximation the solution for the cylinder, provided we normalize friction coefficient with the modulus and mean pressure at zero speed, despite that we show the complex behaviour of the pressure distribution in the viscoelastic regime. We are unable to numerically solve satisfactorily the ill-defined limit of sharp flat punch, for which Persson’s solution predicts finite friction even at zero speed.

The effects of fluid inertia on a self-propelling inextensible waving sheet in an Oldroyd-B fluid are examined. The swimming velocity of the sheet is calculated in the limit in which the amplitude of the waves propagating along the sheet is small relative to the wavelength of the waves. The rate of work done by the sheet is also calculated. It is found that the swimming speed decreases monotonically approaching a limiting value with increasing Reynolds number (R) for a Newtonian fluid. For an Oldroyd-B fluid, the swimming speed increases to a maximum and then decreases asymptotically to a limiting value with increasing R. In contrast, it increases monotonically to a limiting value with increasing R for a Maxwell fluid. The limiting value is highest for the Maxwell fluid and lowest for the Oldroyd-B fluid. The corresponding value for the Newtonian fluid lies in between. The rate of work done by the sheet increases with increasing Reynolds number for all Deborah numbers. However, the energy consumed at a fixed swimming speed is lesser for an Oldroyd-B fluid than that of a Newtonian fluid. These results suggest that contrary to the Newtonian case, the fluid inertia supports the swimming sheet motion in a complex fluid. At a particular Deborah number, the oscillation frequency of the sheet could be adjusted to achieve the maximum speed. Similarly, at a particular frequency of oscillation, the Deborah numbers could be adjusted to achieve the maximum speed. These observations are in sharp contrast with the previous results reported for Newtonian and second-order fluids.

Molecular descriptors encapsulate the key structural information of molecules, which is crucial for elucidating molecular behaviors. They have proven invaluable in quantitative structure–property relationship (QSPR) analysis. Such studies involve rigorous scientific investigations into the relationship between molecular structure and diverse physicochemical properties, revealing the underlying principles governing structure–property correlations. This facilitates predictive modeling and rational design across a wide range of scientific disciplines. Cancer is a lethal disease characterized by the uncontrolled growth and spread of abnormal cells. This study aims to develop regression models for predicting physicochemical properties of novel anti-cancer drugs targeting blood and skin cancers. Utilizing distance-based indices, we construct models based on the structural properties of drug compounds. Comparative analysis with existing QSPR models employing degree and reverse degree parameters demonstrates significantly enhanced predictive capabilities of our proposed models.

A compact analytical form is derived through an integration approach for the interaction between a sphere and a thin rod of finite and infinite lengths, with each object treated as a continuous medium of material points interacting by the Lennard-Jones 12-6 potential and the total interaction potential as a summation of the pairwise potential between material points on the two objects. Expressions for the resultant force and torque are obtained. Various asymptotic limits of the analytical sphere–rod potential are discussed. The integrated potential is applied to investigate the adhesion between a sphere and a thin rod. When the rod is sufficiently long and the sphere sufficiently large, the equilibrium separation between the two (defined as the distance from the center of the sphere to the axis of the rod) is found to be well approximated as (a+0.787sigma ), where a is the radius of the sphere and (sigma ) is the unit of length of the Lennard–Jones potential. Furthermore, the adhesion between the two is found to scale with (sqrt{a}).