Physicochemical Descriptors Research Articles

StaPep: An Open-Source Toolkit for Structure Prediction, Feature Extraction, and Rational Design of Hydrocarbon-Stapled Peptides.

All-hydrocarbon stapled peptides, with their covalent side-chain constraints, provide enhanced proteolytic stability and membrane permeability, making them superior to linear peptides. However, tools for extracting structural and physicochemical descriptors to predict the properties of hydrocarbon-stapled peptides are lacking. To address this, we present StaPep, a Python-based toolkit for generating 3D structures and calculating 21 features for hydrocarbon-stapled peptides. StaPep supports peptides containing two non-standard amino acids (norleucine and 2-aminoisobutyric acid) and six non-natural anchoring residues (S3, S5, S8, R3, R5, and R8), with customization options for other non-standard amino acids. We showcase StaPep's utility through three case studies. The first generates 3D structures of these peptides with a mean RMSD of 1.62 ± 0.86, offering essential structural insights for drug design and biological activity prediction. The second develops machine learning models based on calculated molecular features to differentiate between membrane-permeable and non-permeable stapled peptides, achieving an AUC of 0.93. The third constructs regression models to predict the antimicrobial activity of stapled peptides against Escherichia coli, with a Pearson correlation of 0.84. StaPep's pipeline spans data retrieval, structure generation, feature calculation, and machine learning modeling for hydrocarbon-stapled peptides. The source codes and data set are freely available on Github: https://github.com/dahuilangda/stapep_package.

Thiazolidin-4-one derivatives as antitumor agents against (Caco-2) cell line: synthesis, characterization, in silico and in vitro studies

Compounds based on thiazolidinones have drawn particular attention in the field of medicinal chemistry as potential sources of novel drug-like molecules. In this study thiazolidin-4-one derivatives (3–17) were prepared by refluxing a mixture of β-aroylacrylic acid (1) and aryl thiosemicarbazone (2) and characterized using IR and 1H NMR spectroscopy. In silico, physicochemical descriptors and pharmacokinetic properties were assessed using the SwissADME web tool. Moreover, docking studies of synthesized compounds were performed in order to explore their binding to cyclooxygenase-2 (COX-2) (PDB code: 5IKT) and p53 (PDB code: 6MXZ). Some synthesized compounds were evaluated for their in vitro cytotoxic activity against colon cancer (Caco-2) cell line. Among the tested compounds, compound (16) showed a higher effect with (IC50 of 70 µg/ml). The synthesized compounds revealed reduced Caspase-3 and p53 gene expression, suggesting the potential antineoplastic effects of these compounds.

An improved dataset of force fields, electronic and physicochemical descriptors of metabolic substrates

In silico prediction of xenobiotic metabolism is an important strategy to accelerate the drug discovery process, as candidate compounds often fail in clinical phases due to their poor pharmacokinetic profiles. Here we present MetaQM, a dataset of quantum-mechanical (QM) optimized metabolic substrates, including force field parameters, electronic and physicochemical properties. MetaQM comprises 2054 metabolic substrates extracted from the MetaQSAR database. We provide QM-optimized geometries, General Amber Force Field (FF) parameters for all studied molecules, and an extended set of structural and physicochemical descriptors as calculated by DFT and PM7 methods. The generated data can be used in different types of analysis. FF parameters can be applied to perform classical molecular mechanics calculations as exemplified by the validating molecular dynamics simulations reported here. The calculated descriptors can represent input features for developing improved predictive models for metabolism and drug design, as exemplified in this work. Finally, the QM-optimized molecular structures are valuable starting points for both ligand- and structure-based analyses such as pharmacophore mapping and docking simulations.

Molecular properties, including chameleonicity, as essential tools for designing the next generation of oral beyond rule of five drugs.

The classical drug discovery toolbox continually expands beyond traditional rule of five (Ro5)-compliant small molecules to include new chemical modalities for difficult-to-drug targets. The paper focuses on the molecular properties essential to drive oral bioavailability within the bRo5 framework. The first part outlines the concept and methodologies for characterizing bRo5 physicochemical properties, including considerations on chameleonicity; in particular, the paper summarizes the content of the last author's talk presented during the IAPC-10 Meeting held in Belgrade in September 2023 (https://iapchem.org/index.php/iapc-10-home). The second part of the manuscript presents novel experimental and computational data on three proteolysis targeting chimeras (PROTACs) currently in clinical trials. Molecular descriptors of ARV-110, ARV-471, and DT-2216 are reported and the main limitations of the applied experimental approaches are discussed. Moreover, a simple computational method shows how predicting the presence of chameleonic effects. A full complete physicochemical characterization of three degraders in clinical trials is reported to highlight the differences in physicochemical descriptors between PROTACs dosed orally and intravenously.

In silico and in vitro chemometrics, cell toxicity and permeability of naringenin 8-sulphonate and derivatives.

Sulphur containing natural compounds are among the most biologically relevant metabolites in vivo. Naringenin 8-sulphonate from Parinari excelsa Sabine was evaluated in a previous work, demonstrating ability to act as a natural anti-inflammatory. Although the interference of this molecule against different inflammatory mediators was described, there is no information regarding its potential toxicity and pharmacokinetics, which are essential for its capacity to reach its therapeutic targets. In fact, despite the existence of reports on naringenin ADMET properties, the influence of sulphation patterns on them remains unknown. This work aims to assess the in vitro pharmacokinetic and toxicological behavior of naringenin 8-sulphonate, as well as to understand the importance of the presence and position of the sulphur containing group for that. Naringenin 8-sulphonate physicochemical and ADMET properties were investigated using in silico tools and cell-based in vitro models. At the same time, naringenin and naringenin 4'-O-sulphate were investigated to evaluate the impact of the sulphonate group on the results. ADMETlab 2.0 in silico tool was used to predict the compounds' physicochemical descriptors. Pharmacokinetic properties were determined experimentally in vitro. While MRC-5 lung fibroblasts and HaCaT keratinocytes were used to evaluate the cytotoxicity of samples through MTT and LDH assays, Caco-2 human intestinal epithelial cells were used for the determination of genotoxicity, through alkaline comet assay, and as a permeability model to assess the ability of compounds to cross biological barriers. Experimental determinations showed that none of the compounds was cytotoxic. In terms of genotoxicity, naringenin 8-sulphonate and naringenin caused significant DNA fragmentation, whereas naringenin 4'-O-sulphate did not. When it comes to permeability, the two sulphur-containing compounds with a sulphur containing group were clearly less capable to cross the Caco-2 cell barrier than naringenin. In this study, we conclude that the sulphur containing group from naringenin 8-sulphonate is disadvantageous for the molecule in terms of ADMET properties, being particularly impactful in the permeability in intestinal barrier models. Thus, this work provides important insights regarding the role of flavonoids sulphation and sulphonation upon pharmacokinetics and toxicity.

The role of physicochemical and topological parameters in drug design

Quantitative structure activity relationship (QSAR) is a widely used tool in rational drug design that establishes relationships between the physicochemical and topological descriptors of ligands and their biological activities. Obtained QSAR models help identify descriptors that play pivotal roles in the biological activity of ligands. This not only helps the prediction of new compounds with desirable biological activities but also helps with the design of new compounds with better activities and low toxicities. QSAR commonly uses lipophilicity (logP), hydrophobicity (logD), water solubility (logS), the acid–base dissociation constant (pKa), the dipole moment, the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), molecular weight (MW), molar volume (MV), molar refractivity (MR), and the kappa index as physicochemical parameters. Some commonly used topological indices in QSAR are the Wiener index, Platt index, Hosoya index, Zagreb indices, Balaban index, and E-state index. This review presents a brief description of the significance of the most extensively used physicochemical and topological parameters in drug design.

Physicochemical properties and in silico binding interaction with gyrase B of antibacterial compounds from aquous extract of Hibiscus sabdariffa L.

Hibiscus sabdariffa L. (Hs) is recognized to have various pharmacological activities such as antibacterial, antiedema, anti-fungal, antihypertensive, anti-inflammatory, and antiviral. The aquous extract of Hs’ calyx which is rich in polyphenols and flavonoids displayed antibacterial activity against several bacteria so that the compounds are important to be developed further as antibacterial agents. This study aims to find out which compounds contained in the extract have the antibacterial potency based on in silico interaction with gyrase B. Physicochemical descriptors of ten compounds were evaluated before molecular docking study, and the docking simulation was executed with Autodocktools 1.5.6. The selected compounds based on Lipinski’s Rules and their lower DG0 were subjected to prediction of pharmacokinetic and toxicity (ADMET) using pKCSM online tool. The results gave five test compounds showed the higher binding affinity on gyrase B compared to quercetin which served as inhibitor of the enzyme. The glycosides displayed better scores due to the existence of more hydroxyl groups. The potential aglycones were myricetin and delphinidin. According to the ADMET parameters, the two compounds have the best profile where they were neither hepatotoxic nor carcinogenic and also safer due to the higher LD50. In conclusion, the myricetin and delphinidin generated better binding interaction than the ligand inhibitor of gyrase B which indicated that they have higher potential as antibacterial agents.

Synthesis, antiproliferative, antibacterial activity of acridone derivatives: Experimental and theoretical calculations

A new ethyl 5-(2-(9-oxoacridin-10(9H)-yl)acetamido) thiophene-2-carboxylate (EATPC) and ethyl 2-(2-(9-oxoacridin-10(9H)-yl)acetamido)thiazole-5-carboxylate (EATZC) acridone derivatives were successfully synthesized and described using NMR, UV–Vis and FTIR spectroscopy approaches. The theoretical calculations were computed using Gaussian 09 W software employing DFT/TD-DFT/B3LYP methods with a basis set of 6–311+G (d,p). The calculated and observed spectroscopy results are compared and discussed, demonstrating the reliability of the developed structure of compounds. The natural bond orbital, molecular electrostatic potential, physicochemical descriptors and HOMO-LUMO energies were all calculated and thoroughly discussed in the present study. EATPC and EATZC were tested for antiproliferative capabilities against human breast cancer (MCF-7) cell lines by employing the MTT assay. Furthermore, molecular docking analyses were performed to determine the compound's most active binding sites to the target protein. The molecules EATPC and EATZC are docked with the 4E28 protein, yielding free binding energy energies of -7.8 and -7.9 kcal/mol. This study is unique in that it is the first in-depth report on the comprehensive biological and theoretical evaluation of these title compounds.

Decoding the molecular Symphony: Unravelling neurologically crucial GSK-3 inhibition through 2D QSAR modelling with MLR, PLS, and ANN approaches

This study aimed to decipher the encoded information within the molecular structure of a dataset of GSK-3β inhibitors through a comprehensive quantity structural-activity relationship (QSAR) investigation employing traditional physicochemical descriptors. Various statistical techniques were applied, encompassing linear methods such as Multiple Linear Regression (MLR) and Partial Least Squares (PLS), along with non-linear approaches such as Artificial Neural Networks (ANN). Rigorous validation using diverse statistical tools confirmed the models’ precision and predictability. The model exhibited exceptional statistical relevance, as evidenced by standard parameters: S-value (0.37), F-value (37.17), r (0.93), r2 (0.855), and r2CV (0.78). They assessed predictive power and robustness by involving specific statistical parameters. Key descriptors analyzed, including Verloop L (subs 2), Lipole Z component (whole molecule), and VAMP Dipole Z component, offered crucial insights into their respective contributions. This analysis implies that targeted modifications in the substitution pattern potentially enhance GSK-3β inhibitory activity significantly. The established model clarified how bioactivity depends on structure and offered valuable recommendations for creating new compounds with better inhibitory activity profiles against the GSK-3β enzyme.

Modeling Chromatography Binding through Molecular Dynamics Simulations with Resin Fragments.

Accurate atomistic modeling of the interactions of a chromatography resin with a solute can inform the selection of purification conditions for a product, an important problem in the biotech and pharmaceutical industries. We present a molecular dynamics simulation-based approach for the qualitative prediction of interaction sites (specificity) and retention times (affinity) of a protein for a given chromatography resin. We mimicked the resin with an unrestrained ligand composed of the resin headgroup coupled with successively larger fragments of the agarose backbone. The interactions of the ligand with the protein are simulated in an explicit solvent using the Replica Exchange Molecular Dynamics enhanced sampling approach in conjunction with Hydrogen Mass Repartitioning (REMD-HMR). We computed the ligand interaction surface from the simulation trajectories and correlated the features of the interaction surface with experimentally determined retention times. The simulation and analysis protocol were first applied to a series of ubiquitin mutants for which retention times on Capto MMC resin are available. The ubiquitin simulations helped identify the optimal ligand that was used in subsequent simulations on six proteins for which Capto MMC elution times are available. For each of the six proteins, we computed the interaction surface and characterized it in terms of a range of simulation-averaged residue-level physicochemical descriptors. Modeling of the salt concentrations required for elution with respect to the descriptors resulted in a linear fit in terms of aromaphilicity and Kyte-Doolittle hydrophobicity that was robust to outliers, showed high correlation, and correctly ranked the protein elution order. The physics-based model building approach described here does not require a large experimental data set and can be readily applied to different resins and diverse biomolecules.

Structure-aware machine learning strategies for antimicrobial peptide discovery

Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86–88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.

Systematic screening of synthetic organochalcogen compounds with anticancer activity using human lung adenocarcinoma spheroids

Lung adenocarcinoma stands as a leading global cause of cancer-related fatalities, with current therapeutic approaches remaining unsatisfactory. Given the association between elevated oxidative markers and the aggressive nature of cancer cells (including multidrug resistance and metastatic potential) that can predict poor outcome of lung adenocarcinoma patients, any compounds that interfere with their aberrant redox biology should be rationally explored as innovative intervention strategies. This study was designed to screen potential anticancer activities within nine newly synthesized organochalcogen – compounds characterized by the presence of oxygen, sulfur, or selenium elements in their structure and exhibiting antioxidant activity – and systematically evaluated their performance against cisplatin, the cornerstone therapeutic agent for lung adenocarcinoma. Our methodology involved the establishment of optimal conditions for generating single tumor spheroids using A549 human lung adenocarcinoma cell line. The initiation interval for spheroid formation was determined to be four days in vitro (DIV), and these single spheroids demonstrated sustained growth over a period of 20 DIV. Toxic dose-response curves were subsequently performed for each compound after 24 and 48 h of incubation at the 12th DIV. Our findings reveal that at least two of the synthetic organochalcogen compounds exhibited noteworthy anticancer activity, surpassing cisplatin in key parameters such as lower LD (Lethal Dose) 50, larger drug activity area, and maximum amplitude of effect, and are promising drugs for futures studies in the treatment of lung adenocarcinomas. Physicochemical descriptors and prediction ADME (absorption, distribution, metabolism, and excretion) parameters of selected compounds were obtained using SwissADME computational tool; Molinspiration server was used to calculate a biological activity score, and possible molecule targets were evaluated by prediction with the SwissTargetPrediction server. This research not only sheds light on novel avenues for therapeutic exploration but also underscores the potential of synthetic organochalcogen compounds as agents with superior efficacy compared to established treatments.

Arylamines QSAR-Based Design and Molecular Dynamics of New Phenylthiophene and Benzimidazole Derivatives with Affinity for the C111, Y268, and H73 Sites of SARS-CoV-2 PLpro Enzyme.

A non-structural SARS-CoV-2 protein, PLpro, is involved in post-translational modifications in cells, allowing the evasion of antiviral immune response mechanisms. In this study, potential PLpro inhibitory drugs were designed using QSAR, molecular docking, and molecular dynamics. A combined QSAR equation with physicochemical and Free-Wilson descriptors was formulated. The r2, q2, and r2test values were 0.833, 0.770, and 0.721, respectively. From the equation, it was found that the presence of an aromatic ring and a basic nitrogen atom is crucial for obtaining good antiviral activity. Then, a series of structures for the binding sites of C111, Y268, and H73 of PLpro were created. The best compounds were found to exhibit pIC50 values of 9.124 and docking scoring values of -14 kcal/mol. The stability of the compounds in the cavities was confirmed by molecular dynamics studies. A high number of stable contacts and good interactions over time were exhibited by the aryl-thiophenes Pred14 and Pred15, making them potential antiviral candidates.

Quantitative read-across structure-activity relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats

We have developed a quantitative safety prediction model for subchronic repeated doses of diverse organic chemicals on rats using the novel quantitative read-across structure-activity relationship (q-RASAR) approach, which uses similarity-based descriptors for predictive model generation. The experimental -Log (NOAEL) values have been used here as a potential indicator of oral subchronic safety on rats as it determines the maximum dose level for which no observed adverse effects of chemicals are found. A total of 186 data points of diverse organic chemicals have been used for the model generation using structural and physicochemical (0D–2D) descriptors. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D–2D descriptors. Then, the combined pool of RASAR and the identified 0D–2D descriptors of the training set were employed to develop the final models by using the partial least squares (PLS) algorithm. The developed PLS model was rigorously validated by various internal and external validation metrics as suggested by the Organization for Economic Co-operation and Development (OECD). The final q-RASAR model is proven to be statistically sound, robust and externally predictive (R2 = 0.85, Q2LOO = 0.82 and Q2F1 = 0.94), superseding the internal as well as external predictivity of the corresponding quantitative structure-activity relationship (QSAR) model as well as previously reported subchronic repeated dose toxicity model found in the literature. In a nutshell, the q-RASAR is an effective approach that has the potential to be used as a good alternative way to improve external predictivity, interpretability, and transferability for subchronic oral safety prediction as well as ecotoxicity risk identification.

Design of acyl salicylic acid derivates as COX-1 inhibitors using QSAR approach, molecular docking and QSPR analysis

Background: Acetylsalicylic acid (aspirin), widely used as an antiplatelet agent, is more likely to inhibit COX-1. Along with discovering the cardioprotective role of COX-1 in controlling platelet aggregation, it is important to develop a selective COX-1 inhibitor. Objective: This study aims to design acyl salicylic acid derivatives intended as COX-1 inhibitors. Method: Fourteen derivatives (AcS1-14) were subjected to a quantitative structure-activity relationship (QSAR) study, and 31 QSAR models were obtained using multiple linear regression (MLR) analysis. Molecular docking was performed on COX-1 (PDB. 1PTH) using the Molecular Orbital Environment (MOE) program ver2022.02, and QSPR analysis was conducted to ascertain the contribution of physicochemical descriptors to the free energy score (S) of ligand-receptor complexes. Results: The QSAR-Hansch model predicted hydrophobicity (LogP) and molecular energy (Etotal) and contributed to pain inhibitory action. All derivatives displayed higher in silico affinity than aspirin (S= -4.33±0.00 kcal/mol), and compound AcS7 afforded the highest (S= -5.32 kcal/mol). In QSPR, Etotal also revealed a positive contribution to the affinity. AcS1, AcS2, AcS5, AcS7, and AcS8 expressed higher drug-like properties than aspirin. Conclusion: Derivatives with optimum hydrophobicity and high energy would generate potent COX-1 inhibition. The five selected compounds were recommended to be developed as drug candidates for COX-1 inhibitors.

Prediction of Bond Dissociation Energy for Organic Molecules Based on a Machine‐Learning Approach

Comprehensive SummaryBond dissociation energy (BDE), which refers to the enthalpy change for the homolysis of a specific covalent bond, is one of the basic thermodynamic properties of molecules. It is very important for understanding chemical reactivities, chemical properties and chemical transformations. Here, a machine learning‐based comprehensive BDE prediction model was established based on the iBonD experimental BDE dataset and the calculated BDE dataset by St. John et al. Differential Structural and PhysicOChemical (D‐SPOC) descriptors that reflected changes in molecules’ structural and physicochemical features in the process of bond homolysis were designed as input features. The model trained with LightGBM algorithm gave a low mean absolute error (MAE) of 1.03 kcal/mol on the test set. The D‐SPOC model could apply to accurate BDE prediction of phenol O—H bonds, uncommon N‐SCF3 and O‐SCF3 reagents, and β‐C—H bonds in enamine intermediates. A fast online prediction platform was constructed based on the D‐SPOC model, which could be found at http://isyn.luoszgroup.com/bde_prediction.

Investigation of cathinone analogs targeting human dopamine transporter using molecular modeling

In a step towards understanding the structure–property relationship among Synthetic Cathinones (SCs), a combined methodology based on Density Functional Theory (DFT), Administration, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions, docking and molecular dynamics simulations have been applied to correlate physicochemical descriptors of various SCs to their biological activity. The results from DFT and molecular docking studies correlate well with each other explaining the biological activity trends of the studied SCs. Quantum mechanical descriptors viz. polarizability, electron affinity, ionization potential, chemical hardness, electronegativity, molecular electrostatic potential, and ion interaction studies unravel the distinguishingly reactive nature of Group D (pyrrolidine substituted) and Group E (methylenedioxy and pyrrolidine substituted) compounds. According to ADMET analysis, Group D and Group E molecules have a higher probability of permeating through the blood–brain barrier. Molecular docking results indicate that Phe76, Ala77, Asp79, Val152, Tyr156, Phe320, and Phe326 constitute the binding pocket residues of hDAT in which the most active ligands MDPV, MDPBP, and MDPPP are bound. Finally, to validate the derived quantum chemical descriptors and docking results, Molecular Dynamics (MD) simulations are performed with homology-modelled hDAT (human dopamine transporter). The MD simulation results revealed that the majority of SCs remain stable within the hDAT protein’s active sites via non-bonded interactions after 100 ns long simulations. The findings from DFT, ADMET analysis, molecular docking, and molecular dynamics simulation studies complement each other suggesting that pyrrolidine-substituted SCs (Group D and E), specifically, MPBP and PVN are proven potent SCs along with MDPV, validating various experimental observations. Communicated by Ramaswamy H. Sarma

Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health

The extensive use of various pesticides in the agriculture field badly affects both chickens and humans, primarily through residues in food products and environmental exposure. This study offers the first quantitative structure-toxicity relationship (QSTR) and quantitative read-across-structure toxicity relationship (q-RASTR) models encompassing the LOEL and NOEL endpoints for acute toxicity in chicken, a widely consumed protein. The study's significance lies in the direct link between chemical toxicity in chicken, human intake, and environmental damage. Both the QSTR and the similarity-based read-across algorithms are applied concurrently to improve the predictability of the models. The q-RASTR models were generated by combining read-across derived similarity and error-based parameters, alongside structural and physicochemical descriptors. Machine Learning approaches (SVM and RR) were also employed with the optimization of relevant hyperparameters based on the cross-validation approach, and the final test set prediction results were compared. The PLS-based q-RASTR models for NOEL and LOEL endpoints showed good statistical performance, as traced from the external validation metrics Q2F1: 0.762–0.844; Q2F2: 0.759–0.831 and MAEtest: 0.195–0.214. The developed models were further used to screen the Pesticide Properties DataBase (PPDB) for potential toxicants in chickens. Thus, established models can address eco-toxicological data gaps and development of novel and safe eco-friendly pesticides.

Development of quantitative structure activity relationships (QSARs) for predicting the aggregation of TiO2 nanoparticles under favorable conditions

This study developed multi-linear regression (MLR) quantitative structure-activity relationships (QSARs) to predict n-TiO2 aggregation in the presence of high concentrations of representative emerging organic contaminants (EOCs), which presented favorable conditions to interaction with n-TiO2. The largest diameter change (Δ 517 nm at 0 h and Δ 1164 nm at 12 h) of n-TiO2 was observed by estrone, while the smallest diameter change (Δ −114 nm at 0 h and – 4 nm at 12 h) was observed by lincomycin during experimental periods. In addition, the zeta potential changes of n-TiO2 were observed that the biggest changes were observed by 17β-estradiol (−1.3 mV) and alachlor (−10.02 mV) at 0 h, while 17β-estradiol (−1.31 mV) and pendimethalin (−11.4 mV) showed the biggest changes at 12 h comparing to control. These changes of n-TiO2 diameter and zeta potential may implicate the effects of unique physico-chemical properties of each EOC on the surface modification of n-TiO2. Based on the interaction results, this study investigated the QSARs between n-TiO2 aggregation and physico-chemical descriptors of EOCs with 7 representative descriptors (pKa, Cw, log Kow, M.W., P.S.A., M.V., # of HBD) for predicting n-TiO2 aggregation rate kinetics at 0 h and 12 h by applying MATLAB statistical methods (model 1 - fitlm and model 2 - stepwiselm). In a model 1, QSARs showed the good coefficients of determination (R2 = 0.92) at 0 h and (R2 = 0.87) at 12 h with 7 descriptors. In a model 2, QSARs showed the goodness of fit of a model (R2 = 0.9998) with 8 descriptors (pKa, Cw, log Kow, M.W., P.S.A., M.V., #HBD, pKa⋅#H bond donors) at 0 h, while QSARs showed the coefficients of determination (R2 = 0.68) with 2 descriptors (pKa, M.V.) at 12 h. Particularly, we observed that some descriptors of EOCs such as pKa and # of HBD having polarity have more influenced on the n-TiO2 aggregation rate kinetics. Our developed QSARs demonstrated that the 7 descriptors of EOCs were significantly effective descriptors for predicting n-TiO2 aggregation rate kinetics in favorable conditions, which may implicate the complexity interactions between heterogeneous surfaces of n-TiO2 and physico-chemical properties of EOCs.

4,5-difluoro-2-((m-tolylimino)methyl)phenol: Investigations on a synthesized Schiff base with in silico medicinal approaches and DFT studies

A new Schiff base structure, 4,5-difluoro-2-((m-tolylimino)methyl)phenol was synthesized and characterized. The DFT studies were performed at the B3LYP level with the 6–311 ++ G (d, p) basis set. Medicinal chemistry, ADMET, and toxicity properties of the synthesized compound were studied. Biological targets for potential activity were determined. X-ray analysis revealed that the synthesized compound has a monoclinic lattice, space group P21/n and a volume of 1177.3 Å and its unit cell consists of four molecules. DFT studies revealed a relatively stable structure, open positions for the molecular interactions, and a crystalline lattice stabilized by hydrogen bonds. Medicinal chemistry studies predicted good physicochemical properties, medicinal properties and ADME descriptors, but undesirable toxicity endpoints for this compound. The compound's most potent target was nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, which is a therapeutic target for Alzheimer's disease.