Integrating QSAR modeling, ADMET screening, molecular docking, and molecular dynamics simulations to identify potential MCF-7 inhibitors.

In this study, quantitative structure-activity relationship (QSAR) models were developed by the Monte Carlo technique to predict the anti-breast cancer activity of 144 novel 1,2-naphthoquinone and 1,4-naphthoquinone derivatives against MCF-7 breast cancer cells. To establish QSAR models, a balance of correlation techniques involving the index of ideality of correlation (IIC) and the correlation intensity index (CII), as well as an optimal hybrid descriptor derived from the integration of the Simplified Molecular Input Line Entry System (SMILES) and molecular hydrogen-suppressed graphs (HSG), was used. The resulting models provided valuable information about identifying molecular fragments that enhance or reduce biological activity.

A Subcircuit in the Suprachiasmatic Nucleus Generates Wakefulness.

The daily cycles of sleep and arousal are among the most prominent biological rhythms under circadian control. While the role of the suprachiasmatic nucleus (SCN) as the master circadian pacemaker is well-established, the molecular and circuit mechanisms by which it regulates the rhythms of sleep and arousal remain poorly understood. It has previously shown that the Drosophila clock-output molecule Wide Awake (WAKE) and its mammalian homolog mWAKE are expressed in fly clock neurons and dorsomedial hypothalamus neurons that promote arousal.

Construction of reliable QSPR models for predicting the impact sensitivity of nitroenergetic compounds using correlation weights of the fragments of molecular structures.

Impact sensitivity is a critical property of energetic molecules, indicating their tendency to react when subjected to mechanical stimuli such as impact. Nitro compounds are widely used as explosives across industrial, military, and civilian applications, making their safe handling a significant concern for engineers and scientists working with these materials. Predicting whether a molecule has the potential to pose safety risks is therefore of great importance.

FlyVISTA, an integrated machine learning platform for deep phenotyping of sleep in .

There is great interest in using genetically tractable organisms such as to gain insights into the regulation and function of sleep. However, sleep phenotyping in has largely relied on simple measures of locomotor inactivity. Here, we present FlyVISTA, a machine learning platform to perform deep phenotyping of sleep in flies.

SMILES-based QSAR and molecular docking studies of chalcone analogues as potential anti-colon cancer.

QSAR modeling was applied to predict the anti-colon activity (against HT-29) of 193 chalcone derivatives using the Monte Carlo method, based on the index of ideality correlation (IIC) target function. The models were constructed using CORAL software, which employed optimal descriptors combining SMILES notation and hydrogen-suppressed molecular graphs (HSG). Among the developed models, Split #2 was identified as the best-performing model, with R_validation = 0.

aquatic toxicity prediction of chemicals toward and fathead minnow using Monte Carlo approaches.

The fast-increasing use of chemicals led to large numbers of chemical compounds entering the aquatic environment, raising concerns about their potential effects on ecosystems. Therefore, assessment of the ecotoxicological features of organic compounds on aquatic organisms is very important. and are two aquatic species that are commonly tested as standard test organisms for aquatic risk assessment and are typically chosen as the biological model for the ecotoxicology investigations of chemical pollutants.

SMILES-based QSAR virtual screening to identify potential therapeutics for COVID-19 by targeting 3CL and RdRp viral proteins.

The COVID-19 pandemic has prompted the medical systems of many countries to develop effective treatments to combat the high rate of infection and death caused by the disease. Within the array of proteins found in SARS-CoV-2, the 3 chymotrypsin-like protease (3CL) holds significance as it plays a crucial role in cleaving polyprotein peptides into distinct functional nonstructural proteins. Meanwhile, RNA-dependent RNA polymerase (RdRp) takes center stage as the key enzyme tasked with replicating the viral genomic RNA within host cells.

QSAR models for the ozonation of diverse volatile organic compounds at different temperatures.

In order to assess the fate and persistence of volatile organic compounds (VOCs) in the atmosphere, it is necessary to determine their oxidation rate constants for their reaction with ozone (). However, given that experimental values of are only available for a few hundred compounds and their determination is expensive and time-consuming, developing predictive models for is of great importance. Thus, this study aimed to develop reliable quantitative structure-activity relationship (QSAR) models for 302 values of 149 VOCs across a broad temperature range (178-409 K).

A simple and reliable QSPR model for prediction of chromatography retention indices of volatile organic compounds in peppers.

Worldwide, various types of pepper are used in food as an additive due to their unique pungency, aroma, taste, and color. This spice is valued for its pungency contributed by the alkaloid piperine and aroma attributed to volatile essential oils. The essential oils are composed of volatile organic compounds (VOCs) in different concentrations and ratios.

In-silico activity prediction and docking studies of some flavonol derivatives as anti-prostate cancer agents based on Monte Carlo optimization.

The QSAR models are employed to predict the anti-proliferative activity of 81 derivatives of flavonol against prostate cancer using the Monte Carlo algorithm based on the index of ideality of correlation (IIC) criterion. CORAL software is employed to design the QSAR models. The molecular structures of flavonols are demonstrated using the simplified molecular input line entry system (SMILES) notation.

QSAR and molecular docking studies of isatin and indole derivatives as SARS 3CL inhibitors.

The 3C-like protease (3CL), known as the main protease of SARS-COV, plays a vital role in the viral replication cycle and is a critical target for the development of SARS inhibitor. Comparative sequence analysis has shown that the 3CL of two coronaviruses, SARS-CoV-2 and SARS-CoV, show high structural similarity, and several common features are shared among the substrates of 3CL in different coronaviruses. The goal of this study is the development of validated QSAR models by CORAL software and Monte Carlo optimization to predict the inhibitory activity of 81 isatin and indole-based compounds against SARS CoV 3CL.

In silico study of natural antioxidants.

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants.

Correction: Ecotoxicological prediction of organic chemicals toward by Monte Carlo approach.

[This corrects the article DOI: 10.1039/D2RA03936B.].

Quantitative structure-activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes.

Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK.

Ecotoxicological prediction of organic chemicals toward by Monte Carlo approach.

In the ecotoxicological risk assessment, acute toxicity is one of the most significant criteria. Green alga has been used for ecotoxicological studies to assess the toxicity of different toxic chemicals in freshwater. Quantitative Structure Activity Relationships (QSAR) are mathematical models to relate chemical structure and activity/physicochemical properties of chemicals quantitatively.

The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors.

Ionic liquids (ILs) have captured intensive attention owing to their unique properties such as high thermal stability, negligible vapour pressure, high dissolution capacity and high ionic conductivity as well as their wide applications in various scientific fields including organic synthesis, catalysis, and industrial extraction processes. Many applications of ionic liquids (ILs) rely on the melting point ( ). Therefore, in the present manuscript, the melting points of imidazolium ILs are studied employing a quantitative structure-property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs.

Quantitative structure-toxicity relationship models for predication of toxicity of ionic liquids toward leukemia rat cell line IPC-81 based on index of ideality of correlation.

The application of ionic liquids (ILs) as green solvents has attracted the attention of the scientific community. However, ILs may play the role of toxins. Even though ionic liquids may assist to minimize air pollution, but their discharge into aquatic ecosystems might result in significant water pollution due to their potential toxicity and inaccessibility to biodegradation.

DFT based QSAR study on quinolone-triazole derivatives as antibacterial agents.

QSAR modeling was performed on 39 quinolone-triazole derivatives against gram-positive and gram-negative bacteria. The molecular structures were optimized using the DFT/B3LYP method and 6-31 G basis set. Molecular descriptors were extracted using quantum mechanical calculations.

Green synthesis of zero-valent iron nanoparticles and loading effect on activated carbon for furfural adsorption.

The adsorption techniques are extensively used in dyes, metronidazole, aniline, wastewater treatment methods to remove certain pollutants. Furfural is organic in nature, considered a pollutant having a toxic effect on humans and their environment and especially aquatic species. Due to distinct characteristics of the adsorption technique, this technique can be utilized to adsorb furfural efficiently.

SMILES-based QSAR and molecular docking study of xanthone derivatives as α-glucosidase inhibitors.

Increasing diabetic population is one of the major health concerns all over the world. Inhibition of α-glucosidase is a clinically proved and attractive strategy to manage diabetes. In this study, robust and reliable QSAR models to predict α-glucosidase inhibitory potential of xanthone derivatives are developed by the Monte Carlo technique.

Thermotolerance and Cellulolytic Activity of Fungi Isolated from Soils/Waste Materials in the Industrial Region of Nigeria.

The current study aimed on isolating thermotolerant, cellulolytic fungi from different tropical soil/waste materials samples such as wood waste, sawmill, decomposing straw and compost pit sites in Abraka, Southern Nigeria and assessing their applications in diverse cellulolytic processes. Fungal isolates were identified based on cultural, morphological, ITS-5.8S barcoding, reproductive structures and thereafter screened for thermotolerance and cellulolytic activities [carboxy methyl cellulase (CMC-ase) and filter paperase (FP-ase)] by cultivating at 45, 50, 60, 70, 80° and 45 °C, respectively.

Correlation intensity index: mathematical modeling of cytotoxicity of metal oxide nanoparticles.

Metal oxide nanoparticles (MO-NPs) have unique structural characteristics, exceptionally high surface area, strong mechanical stability, catalytic activities, and are biocompatible. Consequently, MO-NPs have recently attracted considerable interest in the field of imaging-guided therapeutic and biosensing applications. This study aims to develop Quantitative Structure-Activity Relationships (QSAR) for the prediction of cell viability of MO-NPs.

Acid Dye Removal from Aqueous Solution by Using Neodymium(III) Oxide Nanoadsorbents.

In the current work, neodymium oxide (NdO) nanoparticles were synthesized and characterized by means of X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). The major aim/investigation of this research was to fit/model and optimize the removal of Acid Blue 92 (AB92) dye from synthetic effluents (aqueous solutions) using the adsorption process based on neodymium oxide (NdO) nanoparticles. To optimize the adsorption conditions, central composite design (CCD) based on response surface methodology (RSM) was applied.

Hydrothermal synthesis of LaFeO nanoparticles adsorbent: Characterization and application of error functions for adsorption of fluoride.

The adsorption of fluoride from aqueous solution by lanthanum ferrite nanoparticles (LaFeO NPs) synthesized by the hydrothermal method has been investigated. This experimental study was conducted on a laboratory scale. The effects of various operating parameters such as pH (3-11), LaFeO NPs dosage (0.

Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method.

In this research, QSAR modeling was carried out through SMILES of compounds and on the basis of the Monte Carlo method to predict the antioxidant activity of 79 derivatives of pulvinic acid, 23 of coumarine, as well as nine structurally non-related compounds against three radiation sources of Fenton, gamma, and UV. QSAR model was designed through CORAL software, as well as a newer optimizing method well known as the index of ideality correlation. The full set of antioxidant compounds were randomly distributed into four sets, including training, invisible training, validation, and calibration; this division was repeated three times randomly.