Evaluating the Physicochemical Properties-Activity Relationship and Discovering New 1,2-Dihydropyridine Derivatives as Promising Inhibitors for PIM1-Kinase: Evidence from Principal Component Analysis, Molecular Docking, and Molecular Dynamics Studies.

In this study, we evaluated the physicochemical properties related to the previously reported anticancer activity of a dataset comprising thirty 1,2-dihydropyridine derivatives. We utilized Principal Component Analysis (PCA) to identify the most significant influencing factors. The PCA analysis showed that the first two principal components accounted for 59.91% of the total variance, indicating a strong correlation between the molecules and specific descriptors. Among the 239 descriptors analyzed, 18 were positively correlated with anticancer activity, clustering with the 12 most active compounds based on their IC50 values. Six of these variables-LogP, Csp3, b_1rotN, LogS, TPSA, and lip_don-are related to drug-likeness potential. Thus, we then ranked the 12 compounds according to these six variables and excluded those violating the drug-likeness criteria, resulting in a shortlist of nine compounds. Next, we investigated the binding affinity of these nine shortlisted compounds with the use of molecular docking towards the PIM-1 Kinase enzyme (PDB: 2OBJ), which is overexpressed in various cancer cells. Compound 6 exhibited the best docking score among the docked compounds, with a docking score of -11.77 kcal/mol, compared to -12.08 kcal/mol for the reference PIM-1 kinase inhibitor, 6-(5-bromo-2-hydroxyphenyl)-2-oxo-4-phenyl-1,2-dihydropyridine-3-carbonitrile. To discover new PIM-1 kinase inhibitors, we designed nine novel compounds featuring hybrid structures of compound 6 and the reference inhibitor. Among these, compound 31 displayed the best binding affinity, with a docking score of -13.11 kcal/mol. Additionally, we performed PubChem database mining using the structure of compound 6 and the similarity search tool, identifying 16 structurally related compounds with various reported biological properties. Among these, compound 52 exhibited the best binding affinity, with a docking score of -13.03 kcal/mol. Finally, molecular dynamics (MD) studies were conducted to confirm the stability of the protein-ligand complexes obtained from docking the studied compounds to PIM-1 kinase, validating the potential of these compounds as PIM-1 kinase inhibitors.

Application of Unsupervised Machine Learning for the Evaluation of Aerogels' Efficiency towards Ion Removal-A Principal Component Analysis (PCA) Approach.

Water scarcity is a global problem affecting millions of people. It can lead to severe economic, social, and environmental consequences. It can also have several impacts on agriculture, industry, and households, leading to a decrease in human quality of life. To address water scarcity, governments, communities, and individuals must work in synergy for the sake of water resources conservation and the implementation of sustainable water management practices. Following this urge, the enhancement of water treatment processes and the development of novel ones is a must. Here, we have investigated the potential of the applicability of "Green Aerogels" in water treatment's ion removal section. Three families of aerogels originating from nanocellulose (NC), chitosan (CS), and graphene (G) are investigated. In order to reveal the difference between aerogel samples in-hand, a "Principal Component Analysis" (PCA) has been performed on the physical/chemical properties of aerogels, from one side, and the adsorption features, from another side. Several approaches and data pre-treatments have been considered to overcome any bias of the statistical method. Following the different followed approaches, the aerogel samples were located in the center of the biplot and were surrounded by different physical/chemical and adsorption properties. This would probably indicate a similar efficiency in the ion removal of the aerogels in-hand, whether they were nanocellulose-based, chitosan-based, or even graphene-based. In brief, PCA has shown a similar efficiency of all the investigated aerogels towards ion removal. The advantage of this method is its capacity to engage and seek similarities/dissimilarities between multiple factors, with the elimination of the shortcomings for the tedious and time-consuming bidimensional data visualization.

Application of Unsupervised Learning for the Evaluation of Aerogels' Efficiency towards Dye Removal-A Principal Component Analysis (PCA) Approach.

Water scarcity is a growing global issue, particularly in areas with limited freshwater sources, urging for sustainable water management practices to insure equitable access for all people. One way to address this problem is to implement advanced methods for treating existing contaminated water to offer more clean water. Adsorption through membranes technology is an important water treatment technique, and nanocellulose (NC)-, chitosan (CS)-, and graphene (G)- based aerogels are considered good adsorbents. To estimate the efficiency of dye removal for the mentioned aerogels, we intend to use an unsupervised machine learning approach known as "Principal Component Analysis". PCA showed that the chitosan-based ones have the lowest regeneration efficiencies, along with a moderate number of regenerations. NC2, NC9, and G5 are preferred where there is high adsorption energy to the membrane, and high porosities could be tolerated, but this allows lower removal efficiencies of dye contaminants. NC3, NC5, NC6, and NC11 have high removal efficiencies even with low porosities and surface area. In brief, PCA presents a powerful tool to unravel the efficiency of aerogels towards dye removal. Hence, several conditions need to be considered when employing or even manufacturing the investigated aerogels.

Metal Oxide Hydrogel Composites for Remediation of Dye-Contaminated Wastewater: Principal Component Analysis.

Water pollution is caused by multiple factors, such as industrial dye wastewater. Dye-contaminated water can be treated using hydrogels as adsorbent materials. Recently, composite hydrogels containing metal oxide nanoparticles (MONPs) have been used extensively in wastewater remediation. In this study, we use a statistical and artificial intelligence method, based on principal component analysis (PCA) with different applied parameters, to evaluate the adsorption efficiency of 27 different MONP composite hydrogels for wastewater dye treatment. PCA showed that the hydrogel composites CTS@Fe3O4, PAAm/TiO2, and PEGDMA-rGO/Fe3O4@cellulose should be used in situations involving high pH, time to reach equilibrium, and adsorption capacity. However, as the composites PAAm-co-AAc/TiO2, PVPA/Fe3O4@SiO2, PMOA/ATP/Fe3O4, and PVPA/Fe3O4@SiO2, are preferred when all physical and chemical properties investigated have low magnitudes. To conclude, PCA is a strong method for highlighting the essential factors affecting hydrogel composite selection for dye-contaminated water treatment.

Entropy Generation and Statistical Analysis of MHD Hybrid Nanofluid Unsteady Squeezing Flow between Two Parallel Rotating Plates with Activation Energy.

Squeezing flow is a flow where the material is squeezed out or disfigured within two parallel plates. Such flow is beneficial in various fields, for instance, in welding engineering and rheometry. The current study investigates the squeezing flow of a hybrid nanofluid (propylene glycol-water mixture combined with paraffin wax-sand) between two parallel plates with activation energy and entropy generation. The governing equations are converted into ordinary differential equations using appropriate similarity transformations. The shooting strategy (combined with Runge-Kutta fourth order method) is applied to solve these transformed equations. The results of the conducted parametric study are explained and revealed in graphs. This study uses a statistical tool (correlation coefficient) to illustrate the impact of the relevant parameters on the engineering parameters of interest, such as the surface friction factor at both plates. This study concludes that the squeezing number intensifies the velocity profiles, and the rotating parameter decreases the fluid velocity. In addition, the magnetic field, rotation parameter, and nanoparticle volumetric parameter have a strong negative relationship with the friction factor at the lower plate. Furthermore, heat source has a strong negative relationship with heat transfer rate near the lower plate, and a strong positive correlation with the same phenomena near the upper plate. In conclusion, the current study reveals that the entropy generation is increased with the Brinkman number and reduced with the squeezing parameter. Moreover, the results of the current study verify and show a decent agreement with the data from earlier published research outcomes.

Evaluation of the Adsorption Efficiency of Graphene Oxide Hydrogels in Wastewater Dye Removal: Application of Principal Component Analysis.

Industrial dye wastewater is one of the major water pollution problems. Adsorbent materials are promising strategies for the removal of water dye contaminants. Herein, we provide a statistical and artificial intelligence study to evaluate the adsorption efficiency of graphene oxide-based hydrogels in wastewater dye removal by applying Principal Component Analysis (PCA). This study aims to assess the adsorption quality of 35 different hydrogels. We adopted different approaches and showed the pros and cons of each one of them. PCA showed that alginate graphene oxide-based hydrogel (without polyvinyl alcohol) had better tolerance in a basic medium and provided higher adsorption capacity. Polyvinyl alcohol sulfonated graphene oxide-based hydrogels are suitable when higher adsorbent doses are required. In conclusion, PCA represents a robust way to delineate factors affecting hydrogel selection for pollutant removal from aqueous solutions.

Changing role of carrier gas in formation of ethanol clusters by adiabatic expansion.

Adiabatic expansion of molecular vapors is a celebrated method for producing pure and mixed clusters of relevance in both applied and fundamental studies. The present understanding of the relationship between experimental conditions and the structure of the clusters formed is incomplete. We explore the role of the backing/carrier gas during adiabatic expansion of ethanol vapors with regard to cluster production and composition. Single-component clusters of ethanol were produced over a wide size-range by varying the rare gas (He, Ar) backing pressure, with Ar being more efficient than He in promoting the formation of pure ethanol clusters. However, at stagnation pressures Ps>1.34(4) bar and temperature 49(2) °C, synchrotron-based valence and inner-shell photoelectron spectroscopy reveals condensation of Ar carrier gas on the clusters. Theoretical calculations of cluster geometries as well as chemical shifts in carbon 1s ionization energies confirm that the experimental observations are consistent with an ethanol core covered by an outer shell of argon. Experiments on the 1-propanol/Ar system display a similar pattern as described for ethanol/Ar, indicating a broader range of validity of the results.

HCl dissociation in methanol clusters from ab initio molecular dynamics simulations and inner-shell photoelectron spectroscopy.

HCl dissociation in methanol clusters is studied by ab initio molecular dynamics simulations and experimentally by X-ray photoelectron spectroscopy. From theoretical simulations of HCl in oligomers and medium-sized clusters of methanol, two states of solvation are identified for HCl: an intermediate proton-sharing (ion pair) state and a fully dissociated state. Lowering the temperature from 150 to 100 K is found to promote full dissociation over the proton-sharing state. The dissociation of HCl is well reflected in the experimental chlorine 2p photoelectron spectrum recorded for a beam of clusters formed by adiabatic expansion of the vapor over a solution of HCl in methanol. In order to reproduce the observed Cl 2p spectrum by means of theoretical line-shape modeling, one needs to take into account both the intermediate proton-sharing state and the fully dissociated state.

Time-resolved photoelectron angular distributions from strong-field ionization of rotating naphthalene molecules.

A nanosecond laser pulse confines the spatial orientation of naphthalene in 1D or 3D while a femtosecond kick pulse initiates rotation of the molecular plane around the fixed long axis. Time-dependent photoelectron angular distributions (PADs), resulting from ionization by an intense femtosecond probe pulse, exhibit pronounced changes as the molecular plane rotates. Enhanced 3D alignment, occurring shortly after the kick pulse, provides strongly improved contrast in molecular-frame PADs. Calculations in the strong-field approximation show that the striking structures observed in the PADs originate from nodal planes in occupied valence orbitals.