Assessment of the application of selected metal-organic frameworks as advanced sorbents in passive extraction used in the monitoring of contaminants of emerging concern in surface waters

Water pollution has become a critical global concern requiring effective monitoring techniques and robust protection strategies. Contaminants of emerging concern (CECs) are increasingly detected in various water sources, with their harmful effects on humans and ecosystems continually evolving. Based on literature reports highlighting the promising sorption properties of metal-organic frameworks (MOFs), the aim of this study was to evaluate the suitability of NH2-MIL-125 (Ti) and UiO-66 (Ce) as sorbents in passive sampling devices (MOFs-PSDs) for the collection and extraction of a wide group of CECs. Solvothermal methods were used to synthesize MOFs, and the characterization of the obtained materials was performed using field-emission scanning electron microscopy (FE-SEM), powder X-ray diffractometry (pXRD) and Fourier-transform infrared (FTIR) spectroscopy. The research demonstrated the sorption capabilities of the tested MOFs, the ease and rapidity of their chemical regeneration and the possibility of reuse as sorbents. Using chemometric analysis, the structural properties of CECs determining the sorption efficiency on the surface of NH2-MIL-125 (Ti) were identified. The MOFs-PSDs were lab-calibrated to examine the kinetics of analytes sorption and determine the sampling rates (Rs). MOFs-PSDs and CNTs-PSDs (PSDs containing carbon nanotubes as a sorbent) were then placed in the Elbląg River and the Vistula Lagoon to sampling and extraction of the target compounds from the water. CNTs-PSDs were selected, based on our previous research, for the comparison of the effectiveness of the MOFs-PSDs in environmental monitoring. MOFs-PSDs were successfully used in monitoring of CECs in water. The time-weighted average concentrations (CTWA) of 2-hydroxycarbamazepine, carbamazepine-10,11-epoxide, p-nitrophenol, 3,5-dichlorophenol and caffeine were determined in the Elbląg River and CTWA of metoprolol, diclofenac, 2-hydroxycarbamazepine, carbamazepine-10,11-epoxide, p-nitrophenol, 3,5-dichlorophenol and caffeine were determine in the Vistula Lagoon using MOFs-PSDs and a high-performance liquid chromatography coupled with triple quadrupole mass spectrometer.

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

Molecular properties of linear amino acids in water

Four linear amino acids of increased separation of the carboxyl and amino groups, namely glycine (aminoacetic acid), β-alanine (3-aminopropanoic acid), GABA (4-aminobutanoic acid) and DAVA (5-aminopentanoic acid), have been studied by quantum chemical ab initio and DFT methods including the solvent effect in order to get electronic structure and molecular descriptors, such as ionisation energy, electron affinity, molecular electronegativity, chemical hardness, electrophilicity index, dipole moment, quadrupole moment and dipole polarizability. Thermodynamic functions (zero-point energy, inner energy, enthalpy, entropy, and the Gibbs energy) were evaluated after the complete vibrational analysis at the true energy minimum provided by the full geometry optimization. Reaction Gibbs energy allows evaluating the absolute redox potentials on reduction and/or oxidation. The non-local non-additive molecular descriptors were compared along the series showing which of them behave as extensive, varying in match with the molar mass and/or separation of the carboxyl and amino groups. Amino acidic forms and zwitterionic forms of the substances were studied in parallel in order to compare their relative stability and redox properties. In total, 24 species were investigated by B3LYP/def2-TZVPD method (M1) including neutral molecules, molecular cations and molecular anions. For comparison, MP2/def2-TZVPD method (M2) with full geometry optimization and vibrational analysis in water has been applied for 12 species; analogously, for 24 substances, DLPNO-CCSD(T)/aug-cc-pVTZ method (M3) has been applied in the geometry obtained by MP2 and/or B3LYP. It was found that the absolute oxidation potential correlates with the adiabatic ionisation energy; the absolute reduction potential correlates with the adiabatic electron affinity and the electrophilicity index. In order to validate the used methodology with experimental vertical ionisation energies and vibrational spectrum obtained in gas phase, calculations were done also in vacuo.

QSPR predicting the vapor pressure of pesticides into high/low volatility classes

In this work, the vapor pressure of pesticides is employed as an indicator of their volatility potential. Quantitative Structure-Property Relationship models are established to predict the classification of compounds according to their volatility, into the high and low binary classes separated by the 1-mPa limit. A large dataset of 1005 structurally diverse pesticides with known experimental vapor pressure data at 20 °C is compiled from the publicly available Pesticide Properties DataBase (PPDB) and used for model development. The freely available PaDEL-Descriptor and ISIDA/Fragmentor molecular descriptor programs provide a large number of 19,947 non-conformational molecular descriptors that are analyzed through multivariable linear regressions and the Replacement Method technique. Through the selection of appropriate molecular descriptors of the substructure fragment type and the use of different standard classification metrics of model's quality, the classification of the structure-property relationship achieves acceptable results for discerning between the high and low volatility classes. Finally, an application of the obtained QSPR model is performed to predict the classes for 504 pesticides not having experimentally measured vapor pressures.

Prediction of free radical reactions toward organic pollutants with easily accessible molecular descriptors

Machine learning (ML) is becoming an efficient tool for predicting the fate of aquatic contaminants owing to the preponderance of big data. However, whether ML can "learn" the differences in reactivity among different free radicals has not yet been tested. In this work, the effectiveness of combining ML algorithms with molecular fingerprints to predict the reactivity of three free radicals was evaluated. First, a dataset containing 211 organic pollutants and their respective rate constants with the carbonate radical (CO3•-) was used to develop predictive models using both linear regression and ML methods. The use of topological atomic alignment information, in the form of the molecular access system (MACCS) and Morgan Fingerprint, and the electronic structure features (energy levels of the lowest unoccupied and highest occupied molecular orbitals, ELUMO and EHOMO, and the energy gap between ELUMO and EHOMO) gave satisfactory predictive performances (ML model with Random Forest algorithm with MACCS: RMSEtest = 0.787; linear regression model with energy levels: RMSEtest = 0.641). Additionally, the model interpretation correctly described that the key reactivity features for CO3•- were relatively close to those for SO4•- rather than those for •OH. These results suggest that combination of ML algorithms with easily accessible molecular fingerprints would be a powerful tool to accurately predict the radical reactions towards organic compounds.

Mining Chemogenomic Spaces for Prediction of Drug-Target Interactions

The pipeline of drug discovery consists of a number of processes; drug-target interaction determination is one of the salient steps among them. Computational prediction of drug-target interactions can facilitate in reducing the search space of experimental wet lab-based verifications steps, thus considerably reducing time and other resources dedicated to the drug discovery pipeline. While machine learning-based methods are more widespread for drug-target interaction prediction, network-centric methods are also evolving. In this chapter, we focus on the process of the drug-target interaction prediction from the perspective of using machine learning algorithms and the various stages involved for developing an accurate predictor.

Multi-cavity molecular descriptor interconnections: Enhanced protocol for prediction of serum albumin drug binding

The role of human serum albumin (HSA) in the transport of molecules predicates its involvement in the determination of drug distribution and metabolism. Optimization of ADME properties are analogous to HSA binding thus this is imperative to the drug discovery process. Currently, various in silico predictive tools exist to complement the drug discovery process, however, the prediction of possible ligand-binding sites on HSA has posed several challenges. Herein, we present a strong and deeper-than-surface case for the prediction of HSA-ligand binding sites using multi-cavity molecular descriptors by exploiting all experimentally available and crystallized HSA-bound drugs. Unlike previously proposed models found in literature, we established an in-depth correlation between the physicochemical properties of available crystallized HSA-bound drugs and different HSA binding site characteristics to precisely predict the binding sites of investigational molecules. Molecular descriptors such as the number of hydrogen bond donors (nHD), number of heteroatoms (nHet), topological polar surface area (TPSA), molecular weight (MW), and distribution coefficient (LogD) were correlated against HSA binding site characteristics, including hydrophobicity, hydrophilicity, enclosure, exposure, contact, site volume, and donor/acceptor ratio. Molecular descriptors nHD, TPSA, LogD, nHet, and MW were found to possess the most inherent capacities providing baseline information for the prediction of serum albumin binding site. We believe that these associations may form the bedrock for establishing a solid correlation between the physicochemical properties and Albumin binding site architecture. Information presented in this report would serve as critical in provisions of rational drug designing as well as drug delivery, bioavailability, and pharmacokinetics.

Fragments quantum descriptors in classification of bio-accumulative compounds

The aim of the following research is to assess the applicability of calculated quantum properties of molecular fragments as molecular descriptors in machine learning classification task. The research is based on bio-concentration and QM9-extended databases. A number of compounds with results from quantum-chemical calculations conducted with Psi4 quantum chemistry package was also added to the quantum properties database. Classification results are compared with a baseline of random guesses and predictions obtained with the traditional RDKit generated molecular descriptors. Chosen classification metrics show that results obtained with fragments quantum descriptors fall between results from baseline and those provided by molecular descriptors widely applied in cheminformatics. According to the results, the implementation of principal component analysis, causes a drop in categorization metrics.

A multi-target QSRR approach to model retention times of small molecules in RPLC

Quantitative structure-retention relationship models (QSRR) have been utilized as an alternative to costly and time-consuming separation analyses and associated experiments for predicting retention time. However, achieving 100 % accuracy in retention prediction is unrealistic despite the existence of various tools and approaches. The limitations of vast data availability and time complexity hinder the use of most algorithms for retention prediction. Therefore, in this study, we examined and compared two approaches for modelling retention time using a dataset of small molecules with retention times obtained at multiple conditions, referred to as multi-targets (five pH levels: 2.7, 3.5, 5, 6.5, and 8 at gradient times of 20 min of mobile phase). The first approach involved developing separate models for predicting retention time at each condition (single-target approach), while the second approach aimed to learn a single model for predicting retention across all conditions simultaneously (multi-target approach). Our findings highlight the advantages of the multi-target approach over the single-target modelling approach. The multi-target models are more efficient in terms of size and learning speed compared to the single-target models. These retention prediction models offer two-fold benefits. Firstly, they enhance knowledge and understanding of retention times, identifying molecular descriptors that contribute to changes in retention behaviour under different pH conditions. Secondly, these approaches can be extended to address other multi-target property prediction problems, such as multi-quantitative structure Property(X) relationship studies (mt-QS(X)R).

Continuous symmetry and chirality measures: approximate algorithms for large molecular structures

Quantifying imperfect symmetry of molecules can help explore the sources, roles and extent of structural distortion. Based on the established methodology of continuous symmetry and chirality measures, we develop a set of three-dimensional molecular descriptors to estimate distortion of large structures. These three-dimensional geometrical descriptors quantify the gap between the desirable symmetry (or chirality) and the actual one. They are global parameters of the molecular geometry, intuitively defined, and have the ability to detect even minute structural changes of a given molecule across chemistry, including organic, inorganic, and biochemical systems. Application of these methods to large structures is challenging due to countless permutations that are involved in the symmetry operations and have to be accounted for. Our approach focuses on iteratively finding the approximate direction of the symmetry element in the three-dimensional space, and the relevant permutation. Major algorithmic improvements over previous versions are described, showing increased accuracy, reliability and structure preservation. The new algorithms are tested for three sets of molecular structures including pillar[5]arene complexes with Li+, C100 fullerenes, and large unit cells of metal organic frameworks. These developments complement our recent algorithms for calculating continuous symmetry and chirality measures for small molecules as well as protein homomers, and simplify the usage of the full set of measures for various research goals, in molecular modeling, QSAR and cheminformatics.

Ab initio study of molecular properties of l-tyrosine

CONTEXT

l-Tyrosine is a naturally occurring agent that acts as a precursor in biosynthesis of monoaminergic neurotransmitters in brain such as dopamine, adrenaline, noradrenaline, and hormones like thyroxine and triiodothyronine. While l-tyrosine in vacuo adopts the canonical aminoacid form with -NH2 and -COOH functional groups, from neutral solutions, is crystallizes in the zwitterionic form possessing -NH3+ and -COO- groups. As l-tyrosine is non-innocent agent with respect to redox processes, redox ability in water expressed by the absolute oxidation and reduction potentials is investigated. The cluster analysis applied to a set of nine related neurotransmitters and trace amines confirms that l-tyrosine is mostly similar to aminoacid forms of phenylalanine, octopamine, and noradrenaline.

METHODS

The energetic data at the Hartree-Fock MO-LCAO-SCF method has been conducted using def2-TZVP basis set, and improved by the many-body perturbation theory using the MP2 correction to the correlation energy. For the aminoacid form and the zwitterionic form of l-tyrosine, a set of molecular descriptors has been evaluated (ionization energy, electron affinity, molecular electronegativity, chemical hardness, electrophilicity index, dipole moment, quadrupole moment, and dipole polarizability). The solvent effect (CPCM) is very expressive to the zwitterionic form and alters the sign of the electron affinity from positive to negative values. In parallel, density-functional theory with B3LYP variant in the same basis set has been employed for full geometry optimization of the neutral and ionized forms of l-tyrosine allowing assessing the adiabatic (a) ionization/affinity processes. The complete vibrational analysis enables evaluating thermodynamic functions such as the inner energy, enthalpy, entropy, Gibbs energy, and consequently the absolute oxidation and reduction potentials. Of applied methods, the most reliable are B3LYP(a) results that account to the correlation energy and the electron and nuclear relaxation during the ionization/affinity processes.

Computational analysis for eccentric neighborhood Zagreb indices and their significance

In this paper, a novel eccentric neighborhood degree-based topological indices, termed eccentric neighborhood Zagreb indices, have been conceptualized and its discriminating power investigated with regard to the predictability of the boiling point of the chemical substances. The discriminating power of the eccentric neighborhood Zagreb indices was compared with that of Wiener and eccentric connectivity indices. Some explicit results for those new indices for some graphs and graph operations such as join, disjunction, composition, and symmetric difference.

6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling

The application of QSAR analysis dates back a half-century ago and is currently continuously employed in any rational drug design. The multi-dimensional QSAR modeling can be a promising tool for researchers to develop reliable predictive QSAR models for designing novel compounds. In the present work, we studied inhibitors of human aldose reductase (AR) to generate multi-dimensional QSAR models using 3D- and 6D-QSAR methods. For this purpose, Pentacle and Quasar's programs were used to produce the QSAR models using corresponding dissociation constant (K_d) values. By inspecting the performance metrics of the generated models, we achieved similar results with comparable internal validation statistics. However, considering the externally validated values, 6D-QSAR models provide significantly better prediction of endpoint values. The obtained results suggest that the higher the dimension of the QSAR model, the higher the performance of the generated model. However, more studies are required to verify these outcomes.

Prediction of Nucleophilicity and Electrophilicity Based on a Machine Learning Approach

Nucleophilicity and electrophilicity dictate the reactivity of polar organic reactions. In the past decades, Mayr et al. established a quantitative scale for nucleophilicity (N) and electrophilicity (E), which proved to be useful tools for the rationalization of chemical reactivity. In this study, a holistic prediction model was developed through a machine-learning approach. rSPOC, an ensemble molecular representation with structural, physicochemical and solvent features, was developed for this purpose. With 1115 nucleophiles, 285 electrophiles, and 22 solvents, the dataset was currently the largest one for reactivity prediction. The rSPOC model trained with the Extra Trees algorithm showed high accuracy in predicting Mayr's N and E parameters with R2 of 0.92 and 0.93, MAE of 1.45 and 1.45, respectively. Furthermore, the practical applications of the model, for instance, nucleophilicity prediction of NAD(P)H and a series of enamines showed potential in predicting molecules with unknown reactivity within seconds. An online prediction platform (http://isyn.luoszgroup.com/) was constructed based on the current model, which is available free to the scientific community.

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor β dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

Development of QSPR-ANN models for the estimation of critical properties of pure hydrocarbons

The current work aimed to predict three critical properties: critical temperature (Tc), critical volume (Vc), and critical pressure (Pc) of pure hydrocarbons. A multi-layer perceptron artificial neural network (MLP-ANN) has been adopted as a nonlinear modeling technique and computational approach based on a few relevant molecular descriptors. A set of diverse data points was used to build three QSPR-ANN models, including 223 points for Tc, Vc, and 221 for Pc. The entire database was randomly split into two subsets: 80% for the training set and 20% for the testing set. A large number of 1666 molecular descriptors were calculated and then reduced by a statistical methodology based on several phases to retain them into a reasonable number of relevant descriptors, wherein about 99% of initial descriptors were excluded. Thus, the Quasi-Newton backpropagation (BFGS) algorithm was applied to train the ANN structure. The results of three QSPR-ANN models showed good precision, confirmed by the high values of determination coefficient (R²) ranging from 0.9990 to 0.9945, and the low values of calculated errors, such as the Mean Absolute Percentage Error (MAPE) that ranged from 2.2497 to 0.7424% for the best three models of Tc, Vc, and Pc. The weight sensitivity analysis method was applied to know the contribution of each input descriptor individually or by class on each appropriate QSPR-ANN model. Moreover, the applicability domain (AD) method was also used with a strict limit of standardized residual values (d_i = ±2). However, the results were promising, with nearly 88% of the data points validated within the AD range. Finally, the results of the proposed QSPR-ANN models were compared with other well-known QSPR or ANN models for each property. Consequently, our three models provided satisfactory results, outperforming most of the models mentioned in this comparison. This computational approach can be applied in petroleum engineering and other related fields to accurately determine the critical properties of pure hydrocarbons: Tc, Vc, and Pc.

Development and optimization of stability-indicating method of ethinylestradiol, levonorgestrel, and their main impurities using quality by design approach

The association of Ethinylestradiol 0.03 mg and Levonorgestrel 0.15 mg is a hormonal contraceptive that combines estrogen and progestogen. According to a bibliographic survey, these combined drugs present at least 18 known degradation products, which are required to control the potential impurities harmful to human health. The high number of impurities and the low concentrations of the active pharmaceutical ingredients (APIs) and their respective degradation products increase the complexity of the stability-indicating method development for this medicine. Thus, this work aimed to develop and optimize the stability-indicating method using the quality by design (QbD) approach and in-silico tools for application in samples of oral contraceptives sold in Brazil. The analysis samples were initially subjected to a forced degradation study through 7 days of exposure under acid and alkali hydrolysis, oxidative condition, and oxidation by metal ions. In addition to the chemical exposure, the sample was subjected to physical stress through 10 days of exposure under dry heat, moisture, and photolytic degradation. These exposure samples were analyzed in the development and optimization of chromatographic conditions. As a result, the developed method was able to separate 20 known substances, including the two APIs and their respective 18 degradation products, as well as unknown degradation products obtained by the forced degradation study. Finally, this stability-indicating method was successfully applied for comparative analysis of contraceptive drugs marketed in Brazil, newly purchased and subjected to accelerated stability condition at 40 °C and 75% RH over the 6-month period.

Assessing machine learning approaches for predicting failures of investigational drug candidates during clinical trials

One of the major challenges in drug development is having acceptable levels of efficacy and safety throughout all the phases of clinical trials followed by the successful launch in the market. While there are many factors such as molecular properties, toxicity parameters, mechanism of action at the target site, etc. that regulates the therapeutic action of a compound, a holistic approach directed towards data-driven studies will invariably strengthen the predictive toxicological sciences. Our quest for the current study is to find out various reasons as to why an investigational candidate would fail in the clinical trials after multiple iterations of refinement and optimization. We have compiled a dataset that comprises of approved and withdrawn drugs as well as toxic compounds and essentially have used time-split based approach to generate the training and validation set. Five highly robust and scalable machine learning binary classifiers were used to develop the predictive models that were trained with features like molecular descriptors and fingerprints and then validated rigorously to achieve acceptable performance in terms of a set of performance metrics. The mean AUC scores for all the five classifiers with the hold-out test set were obtained in the range of 0.66-0.71. The models were further used to predict the probability score for the clinical candidate dataset. The top compounds predicted to be toxic were analyzed to estimate different dimensions of toxicity. Apparently, through this study, we propose that with the appropriate use of feature extraction and machine learning methods, one can estimate the likelihood of success or failure of investigational drugs candidates thereby opening an avenue for future trends in computational toxicological studies. The models developed in the study can be accessed at https://github.com/gnsastry/predicting_clinical_trials.git.

Quantitative Structure Activity/Toxicity Relationship through Neural Networks for Drug Discovery or Regulatory Use

Quantitative structure - activity relationship (QSAR) modelling is widely used in medicinal chemistry and regulatory decision making. The large amounts of data collected in recent years in materials and life sciences projects provide a solid foundation for data-driven modelling approaches that have fostered the development of machine learning and artificial intelligence tools. An overview and discussion of the principles of QSAR modelling focus on the assembly and curation of data, computation of molecular descriptor, optimization, validation, and definition of the scope of the developed QSAR models. In this review, some examples of (QSAR) models based on artificial neural networks are given to demonstrate the effectiveness of nonlinear methods for extracting information from large data sets to classify new chemicals and predict their biological properties.

An Optimized Cloud Computing Method for Extracting Molecular Descriptors

Extracting molecular descriptors from chemical compounds is an essential preprocessing phase for developing accurate classification models. Supervised machine learning algorithms offer the capability to detect "hidden" patterns that may exist in a large dataset of compounds, which are represented by their molecular descriptors. Assuming that molecules with similar structure tend to share similar physicochemical properties, large chemical libraries can be screened by applying similarity sourcing techniques in order to detect potential bioactive compounds against a molecular target. However, the process of generating these compound features is time-consuming. Our proposed methodology not only employs cloud computing to accelerate the process of extracting molecular descriptors but also introduces an optimized approach to utilize the computational resources in the most efficient way.

Insights into the lipophilicity of four commonly prescribed antidiabetic drugs and their simultaneous analysis using a simple TLC-spectrodensitometric method: Application to fixed-dose combination tablets and human plasma

The retention and lipophilicity characteristics of four oral antidiabetic drugs namely; Metformin (MET), Linagliptin (LIN), Empagliflozin (EMP), and Dapagliflozin (DAP) were evaluated by a facile TLC-spectrodensitometric method. The developed method was validated and employed for simultaneous determination of the investigated drugs in their synthetic quaternary mixture, single- and multi-component tablets, and human plasma. The separation of the cited drugs was achieved using silica gel G 60F₂₅₄-TLC plates and a mobile system consisting of n-butanol: water: glacial acetic acid (7: 3: 1, v/v/v). After scanning at 234 nm, good linearities (10.0-2000.0 ng/band for each drug) and correlation coefficients (r = 0.99882-0.99972) with lower limits of detection and quantitation (2.17-3.58 and 6.57-10.85 ng/band, respectively) were statistically calculated. The obtained recoveries (98.35-101.38%) proved the wide applicability of the established method for concurrent estimation of the studied antidiabetics in fixed-dose combination tablets and human plasma. Besides, the present work was extended to estimate the lipophilicity parameters of the targeted drugs. Molecular lipophilicity (R_M), relative lipophilicity (R_M0), and lipophilic descriptor (C₀) were calculated for MET, LIN, EMP, and DAP. Good correlations (r = 0.8729-0.9933) between the chromatographic retention data and molecular descriptors of the studied drugs were attained. The obtained results confirmed the poor lipophilicity of MET and LIN compared to EMP and DAP. Lastly, understanding the lipophilicity of the cited drugs may be promising for the future design of safer and more effective formulations for diabetes mellitus, cancer, and Alzheimer's disease. Over and above, this work may be further applied to QSAR studies.

A survey on computational taste predictors

Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00217-022-04044-5.

Synthesis and investigation of anti-COVID19 ability of ferrocene Schiff base derivatives by quantum chemical and molecular docking

The recent outbreak of coronavirus disease (COVID-19) has rampaged the world with more than 236 million confirmed cases and over 4.8 million deaths across the world reported by the world health organization (WHO) till Oct 5, 2021. Due to the advent of different variants of coronavirus, there is an urgent need to identify effective drugs and vaccines to combat rapidly spreading virus varieties across the globe. Ferrocene derivatives have attained immense interest as anticancer, antifungal, antibacterial, and antiparasitic drug candidates. However, the ability of ferrocene as anti-COVID-19 is not yet explored. Therefore, in the present work, we have synthesized four new ferrocene Schiff bases (L1-L4) to understand the active sites and biological activity of ferrocene derivatives by employing various molecular descriptors, frontier molecular orbitals (FMO), electron affinity, ionization potential, and molecular electrostatic potential (MEP). A theoretical insight on synthesized ferrocene Schiff bases was accomplished by molecular docking, frontier molecular orbitals energies, active sites, and molecular descriptors which were further compared with drugs being currently used against COVID-19, i.e., dexamethasone, hydroxychloroquine, favipiravir (FPV), and remdesivir (RDV). Moreover, through the molecular docking approach, we recorded the inhibitions of ferrocene derivatives on core protease (6LU7) protein of SARS-CoV-2 and the effect of substituents on the anti-COVID activity of these synthesized compounds. The computational outcome indicated that L1 has a powerful 6LU7 inhibition of SARS-CoV-2 compared to the currently used drugs. These results could be helpful to design new ferrocene compounds and explore their potential application in the prevention and treatment of SARS-CoV-2.

HobPre: accurate prediction of human oral bioavailability for small molecules

Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process. In this study, a total of 1588 drug molecules with HOB data were collected from the literature for the development of a classifying model that uses the consensus predictions of five random forest models. The consensus model shows excellent prediction accuracies on two independent test sets with two cutoffs of 20% and 50% for classification of molecules. The analysis of the importance of the input variables allowed the identification of the main molecular descriptors that affect the HOB class value. The model is available as a web server at www.icdrug.com/ICDrug/ADMET for quick assessment of oral bioavailability for small molecules. The results from this study provide an accurate and easy-to-use tool for screening of drug candidates based on HOB, which may be used to reduce the risk of failure in late stage of drug development.

Persistence and removal of trace organic compounds in centralized and decentralized wastewater treatment systems

The persistence of trace organic chemicals in treated effluent derived from both centralized wastewater treatment plants (WWTPs) and decentralized wastewater treatment systems (DEWATS) is of concern due to their potential impacts on human and ecosystem health. Here, we utilize non-targeted analysis (NTA) with comprehensive two-dimensional gas chromatography coupled with time of flight mass spectrometry (GC × GC/TOF-MS) to conduct an evaluation of the common persistent and removed compounds found in two centralized WWTPs in the USA and South Africa and one DEWATS in South Africa. Overall, removal efficiencies of chemicals were similar between the treatment plants when they were compared according to the number of chemical features detected in the influents and effluents of each treatment plant. However, the DEWATS treatment train, which has longer solids retention and hydraulic residence times than both of the centralized WWTPs and utilizes primarily anaerobic treatment processes, was able to remove 13 additional compounds and showed a greater decrease in normalized peak areas compared to the centralized WWTPs. Of the 111 common compounds tentatively identified in all three influents, 11 compounds were persistent in all replicates, including 5 compounds not previously reported in effluents of WWTPs or water reuse systems. There were no significant differences among the physico-chemical properties of persistent and removed compounds, but significant differences were observed among some of the molecular descriptors. These results have important implications for the treatment of trace organic chemicals in centralized and decentralized WWTPs and the monitoring of new compounds in WWTP effluent.

Predicting reversed-phase liquid chromatographic retention times of pesticides by deep neural networks

To be able to predict reversed phase liquid chromatographic (RPLC) retention times of contaminants is an asset in order to solve food contamination issues. The development of quantitative structure-retention relationship models (QSRR) requires selection of the best molecular descriptors and machine-learning algorithms. In the present work, two main approaches have been tested and compared, one based on an extensive literature review to select the best set of molecular descriptors (16), and a second with diverse strategies in order to select among 1545 molecular descriptors (MD), 16 MD. In both cases, a deep neural network (DNN) were optimized through a gridsearch.

Transfer of gas chromatographic retention data among poly(siloxane) columns by quantitative structure-retention relationships based on molecular descriptors of both solutes and stationary phases

In the present study, computational molecular descriptors of 90 saturated esters and seven poly(siloxane) stationary phases with different polarity (SE-30, OV-7, DC-710, OV-25, XE-60, OV-225 and Silar-5CP) were combined into quantitative structure-retention relationship (QSRR) models aimed at predicting the Kováts retention indices (RIs) of the solutes. The molecular descriptors (174) of the stationary phases included in the models were computed using Dragon software from poly(siloxane) oligomers made of 20 siloxane units reflecting the nominal composition of the stationary phase, whereas 439 molecular descriptors were adopted to represent the esters. Different QSRR models were generated by means of Partial Least Squares (PLS) regression to assess the accuracy of this approach in predicting the RIs of unexplored solutes both in known and external stationary phases. After calibration of each PLS model, the descriptors were selected/discarded according to their relevance, evaluated by Covariance Selection (CovSel), and the PLS models were re-built, which resulted in a noticeable improvement of their predictive ability. Firstly, all the available data were equally divided into a training and a test set; the model built on the calibration set was used to predict the RIs of the validation observations. Successively, seven diverse PLS models were created following a "leave-one-column-out" fashion procedure, each one finalized to the estimation of the RIs of the 90 esters associated with a single stationary phase, whereas the calibration model was calculated on the remaining data. All the estimated models provided successful results on the external stationary phase, and predictive performance further increased after variable selection based on CovSel analysis. The final models provided a Root Mean Square Error in Cross Validation (RMSECV) in the range 12-20, a Root Mean Square Error in Prediction (RMSEP) in the range 11-26, and Mean Absolute Percentage Errors in Prediction (MAMEPs) in the range 0.7-1.5, revealing accurate cross-column prediction. Eventually, to test the robustness of the proposed approach, the 90 solutes were equally partitioned into a calibration and a test set and two further QSSR strategies were applied. The first PLS model was calibrated on all the seven stationary phases and the RIs of the 45 external solutes in the same seven columns were simultaneously predicted. The last QSRR approach followed a "leave-one-column-out" scheme and RI of 45 test solutes on an external stationary phase was predicted by a PLS model calibrated with the data of the 45 remaining solutes and the six left stationary phases. After selection of the significant molecular descriptors, PLS regression provided RMSECV values in the range 6-19, RMSEPs in the range 10-14, and MAPEPs in the range 0.9-2.4, revealing the suitability of the approach to deduce the RI of unknown solutes in uncharted stationary phases.

Block-wise exploration of molecular descriptors with Multi-block Orthogonal Component Analysis (MOCA)

Data tables for machine learning and structure‐activity relationship modelling (QSAR) are often naturally organized in blocks of data, where multiple molecular representations or sets of descriptors form the blocks. Multi‐block Orthogonal Component Analysis (MOCA), a new analytical tool, can be used to explore such data structures in a single model, identifying principal components that are unique to a single block or joint over multiple blocks. We applied MOCA to two sets of 550 and 300 molecules and up to 9213 molecular descriptors organized in 11 blocks. The MOCA models reveal relationships between the blocks and overarching trends across the whole dataset. Based on the MOCA joint components, we propose a quantitative metric for the redundancy of blocks, useful for a priori block‐wise feature selection or evaluation of new molecular representations. The second data set includes 7 ecotoxicological study endpoints for crop protection chemicals, for which we (re‐)discovered some general trends and linked them to molecular properties. Using a single MOCA model we estimated the predictive potential of each block and the model‐ability of the target block.

Insight into selected emerging micropollutant interactions with wastewater colloidal organic carbon: implications for water treatment and analysis

This study reports how adding a membrane filter (0.45-μm cellulose nitrate filter) between a glass fibre filter and the solid phase extraction (SPE) cartridge affected the GC/MS analysis of 48 emerging organic micropollutants in wastewater. Most of them are widely used as active pharmaceuticals, cosmetic and packaging material ingredients including classes of parabens, benzophenones and bisphenols among other chemicals tested. A high artificial organic carbon (OC) content in wastewater (DOC = 280 ± 14 mg/L) was investigated to gain insight into micropollutants/colloidal OC filter cake interactions. The results show that even with the use of matrix-matched calibration, the introduction of a second (membrane) filtration step can affect the analysis. Both positive, negative and no effects on the theoretical concentrations calculated from the calibration curves with and without additional filtration were observed. Positive effects on the concentration for the same analyte peak area relative to its surrogate standard were the consequence of a reduced signal for the same concentration, while the negative effects are the consequence of increasing signal for the same concentration. Effect types were dependent on the concentration and the nature of the analytes. Results show that bisphenols and parabens significantly interact with colloidal OC. Statistical analysis of molecular descriptor distribution with effect type showed that micropollutants that have a stronger interaction with colloidal OC have significantly higher ability to act as hydrogen bond donors (HBD) and have larger molar volume (MV). All compounds that experienced either positive or negative effects have a significantly higher median logD. However, further exploration within a single class of compounds (parabens, benzophenones and bisphenols) revealed that selected descriptors are unrelated to an effect type. Pearson's correlations showed that a correlation exists for certain concentration levels and groups of compounds between a negative effect and MV and logD and a positive effect with MV, MW and rotatable bond (RB) count.

Connecting chemistry and biology through molecular descriptors

Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties. Here, we give an overview of biologically relevant descriptors, covering chemical compounds, proteins and other biological entities, such as diseases and cell lines, while aligning them to the major contributions in the field from disciplines, such as natural language processing or computer vision. We now envision a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations, such as distance measures, additions, and subtractions.

Assessment of pesticides volatilization potential based on their molecular properties using the TyPol tool

Following treatment, amounts of pesticides can reach the atmosphere because of spray drift, volatilization from soil or plants, and/or wind erosion. Monitoring and risk assessment of air contamination by pesticides is a recent issue and more insights on pesticide transfer to atmosphere are needed. Thus, the objective of this work was to better understand and assess pesticides emission potential to air through volatilization. The TyPol tool was used to explore the relationships between the global, soil and plant volatilization potentials of 178 pesticides, and their molecular properties. The outputs of TyPol were then compared to atmospheric pesticide concentrations monitored in various French regions. TyPol was able to discriminate pesticides that were observed in air from those that were not. Clustering considering parameters driving the emission potential from soil (sorption characteristics) or plant (lipophilic properties), in addition to vapor pressure, allowed better discrimination of the pesticides than clustering considering all parameters for the global emission potential. Pesticides with high volatilization potential have high total energy, and low molecular weight, molecular connectivity indices and polarizability. TyPol helped better understand the volatilization potential of pesticides. It can be used as a first step to assess the risk of air contamination by pesticides.

Topological properties of Sierpinski network and its application

BACKGROUND

Sierpinski graphs S(n,k) are largely studied because of their fractal nature with applications in topology, chemistry, mathematics of Tower of Hanoi, and computer sciences. Applications of molecular structure descriptors are a standard procedure that are used to correlate the biological activity of molecules with their chemical structures and thus can be helpful in the field of pharmacology.

OBJECTIVE

The aim of this article is to establish analytically closed computing formulae for eccentricity-based descriptors of Sierpinski networks and their regularizations. These computing formulae are useful to determine a large number of properties like thermodynamic properties, physicochemical properties, chemical and biological activity of chemical graphs.

METHODS

At first, vertex sets have been partitioned on the basis of their degrees, eccentricities, and frequencies of occurrence. Then these partitions are used to compute the eccentricity-based indices with the aid of some combinatorics.

RESULTS

The total eccentric index and eccentric-connectivity index have been computed. We also compute some eccentricity-based Zagreb indices of the Sierpinski networks. Moreover, a comparison has also been presented in the form of graphs.

CONCLUSION

These computations will help the readers to estimate the thermodynamic properties, physicochemical properties of chemical structures, which are of fractal nature and can not be dealt with easily. A 3D graphical representation is also presented to understand the dynamics of the aforementioned topological descriptors.

Prediction of pesticide retention time in reversed-phase liquid chromatography using quantitative-structure retention relationship models: A comparative study of seven molecular descriptors datasets

Predicting chromatographic retention times of pesticides has become more and more important for suspect and non-target screening. Indeed, high-resolution mass spectrometry hyphenated (HRMS) to liquid chromatography (LC) are of growing interest for research and monitoring of pesticides, their metabolites and transformation products. The development of quantitative structure-retention relationship models require selecting the most adequate and best set of molecular descriptors and the best machine-learning algorithm. Here, we used seven molecular descriptor sets extracted from four well-known studies and applied them to roughly 800 pesticides and their chromatographic reversed-phase retention times. We used and optimized five different machine-learning algorithms with these descriptor sets to carry out predictions. Our results show that a support-vector machine regression algorithm with only eight molecular descriptors gave the best compromise between the number of molecular descriptors, processing time and model complexity to optimize prediction performance for this specific gradient LC method.

Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing

The current study utilized an artificial neural network (ANN) to generate computational models to achieve process optimization for a previously developed continuous liposome manufacturing system. The liposome formation was based on a continuous manufacturing system with a co-axial turbulent jet in a co-flow technology. The ethanol phase with lipids and aqueous phase resulted in liposomes of homogeneous sizes. The input features of the ANN included critical material attributes (CMAs) (e.g., hydrocarbon tail length, cholesterol percent, and buffer type) and critical process parameters (CPPs) (e.g., solvent temperature and flow rate), while the ANN outputs included critical quality attributes (CQAs) of liposomes (i.e., particle size and polydispersity index (PDI)). Two common ANN architectures, multiple-input-multiple-output (MIMO) models and multiple-input-single-output (MISO) models, were evaluated in this study, where the MISO outperformed MIMO with improved accuracy. Molecular descriptors, obtained from PaDEL-Descriptor software, were used to capture the physicochemical properties of the lipids and used in training of the ANN. The combination of CMAs, CPPs, and molecular descriptors as inputs to the MISO ANN model reduced the training and testing mean relative error. Additionally, a graphic user interface (GUI) was successfully developed to assist the end-user in performing interactive simulated risk analysis and visualizing model predictions.

Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform

The physicochemical characterisation data from a library of 69 engineered nanomaterials (ENMs) has been exploited in silico following enrichment with a set of molecular descriptors that can be easily acquired or calculated using atomic periodicity and other fundamental atomic parameters. Based on the extended set of twenty descriptors, a robust and validated nanoinformatics model has been proposed to predict the ENM ζ-potential. The five critical parameters selected as the most significant for the model development included the ENM size and coating as well as three molecular descriptors, metal ionic radius (r_ion), the sum of metal electronegativity divided by the number of oxygen atoms present in a particular metal oxide (Σχ/n_O) and the absolute electronegativity (χ_abs), each of which is thoroughly discussed to interpret their influence on ζ-potential values. The model was developed using the Isalos Analytics Platform and is available to the community as a web service through the Horizon 2020 (H2020) NanoCommons Transnational Access services and the H2020 NanoSoveIT Integrated Approach to Testing and Assessment (IATA).

Synthesis of new α-Aryl-α-tetralones and α-Fluoro-α-aryl-α-tetralones, preliminary antiproliferative evaluation on drug resistant cell lines and in silico prediction of ADMETox properties

α-aryl-α-tetralones and α-fluoro-α-aryl-α-tetralones derivatives were synthesized by palladium catalyzed α-arylation reaction of α-tetralones and α-fluoro-α-tetralones, with bromoarenes in moderate to good yields. These compounds were evaluated for their in vitro anti-proliferative effects against human breast cancer and leukemia cell lines with diverse profiles of drug resistance. The most promising compounds, 3b, 3c, 8a and 8c, were effective on both neoplastic models. 3b and 8a induced higher toxicity on multidrug resistant cells and were able to avoid efflux by ABCB1 and ABCC1 transporters. Theoretical calculations of the physicochemical descriptors to predict ADMETox properties were favorable concerning Lipinski's rule of five, results that reflected on the low effects on non-tumor cells. Therefore, these compounds showed great potential for development of pharmaceutical agents against therapy refractory cancers.

Molecular modeling and design of some β-amino alcohol grafted 1,4,5-trisubstituted 1,2,3-triazoles derivatives against chloroquine sensitive, 3D7 strain of Plasmodium falciparum

Resistance nature of Plasmodium falciparum (P. falciparum) to the most effective antimalarial drug, Artemisinin, intimidate the global goal of total eradication of malarial. In an attempt to overcome this challenge, the research was aimed at designing derivatives of β-amino alcohol grafted 1,4,5-trisubstituted 1,2,3-triazoles with improve activity against the P. falciparum through structural modifications of the most active compound (design template), and their activity determined using the developed theoretical predictive model. To achieve this, the geometries were optimized via density functional theory (DFT) using B3LYP/6-31G^∗ basis set to generate molecular descriptors for model development. Analysis of the developed model and the descriptors mean effect lead to the design of derivatives with improved activity. Five (5) theoretical models were developed, where the model {pIC₅₀ = 5.95067(SpMin5_Bhi) - 0.0323461(RDF45m) + 0.0203865 (RDF95e) + 0.0499285 (L1m) - 3.50822} with the highest coefficient of determination (R²) of 0.9367, cross-validated R² (Q²cv) of 0.8242, and the external validated R² (R²_pred) of 0.9462, selected as the best model. The mean effect analysis revealed descriptor SpMin5_Bhi as the most contributive. The descriptor encodes the first ionization potentials of the compounds and are influenced by electron-withdrawing/donating substituents. Hence, structural modifications of the compound with the highest activity (a design template) using electron-withdrawing substituents such as –NO₂, –SO₃H, -Br, –I, –CH₂CH₃, and –CH₃ was done at a different positions, to obtain five (5) hypothetical novel compounds. The statistical results, shows the robustness, excellent prediction power, and validity of the selected model. Descriptor analysis revealed the first ionization potential (SpMin5_Bhi) to play a significant role in the activity of β-amino alcohol grafted 1,4,5-trisubstituted 1,2,3-triazoles derivatives. The five design derivatives of β-amino alcohol grafted 1,4,5-trisubstituted 1,2,3-triazoles with higher activities revealed compound 21C to have an antimalarial activity of pIC₅₀ = 6.7573 higher than it co-designed compounds and even the standard drug. This claim could be verified through molecular docking to determine their interaction with the target protein.

Artichoke pectic oligosaccharide characterisation and virtual screening of prebiotic properties using in silico colonic fermentation

The aim of this work was to develop a comprehensive workflow to elucidate molecular features of artichoke pectic oligosaccharides (POS) contributing to high potential prebiotic activity. First, obtainment of artichoke POS by Pectinex® Ultra-Olio was optimised using an artificial neural network. Under optimal conditions (pH 6.86; 1.5 h; enzyme dose 520.5 U/g pectin) POS yield was 624 mg/g pectin. Oligosaccharide structures (M_w < 1.3 kDa) were characterised by MALDI-TOF-MS. Then, conformational analysis of glycosidic bonds was performed by replica exchange molecular dynamics simulations and interaction mechanisms between POS and several microbial glycosidases were proposed by molecular modelling. Chemical information was integrated in virtual simulations of colonic fermentation. Highest hydrolysis rate was obtained for GalA-Rha-GalA trisaccharide, while the presence of partial negative charges and high radius of gyration enhance short chain fatty acid formation in distal colon. Established structure-activity relationships could help the rational design of prebiotics and clinical trials.

Predictive identification of co-formers in co-amorphous systems

This work aims to understand the properties of co-formers that form co-amorphous pharmaceutical materials and to predict co-amorphous system formation. A partial least square - discriminant analysis (PLS-DA) was performed using known co-amorphous systems described by 36 variables based on the properties of the co-former and the binding energy of the system. The PLS-DA investigated the propensity to form co-amorphous material of the active pharmaceutical ingredients: mebendazole, carvedilol, indomethacin, simvastatin, carbamazepine and furosemide in combination with 20 amino acid co-formers. The variables that were found to favour the propensity to form co-amorphous systems appear to be a relatively large value for average molecular weight and the sum of the difference between hydrogen bond donors and hydrogen bond acceptors for both components, and a relatively small or negative value for excess enthalpy of mixing, excess enthalpy of hydrogen bonding and the difference in the Hansen parameter for hydrogen bonding of the coformer and the active pharmaceutical ingredient (API). To test the predictive power of this model, 29 potential co-formers were used to form either co-amorphous or crystalline two-component materials with mebendazole. Of these 29 two-component systems, the co-amorphous nature of a total of 26 materials was correctly predicted by the model, giving a predictive hit rate of 90 %.

Improvement of quantitative structure-retention relationship models for chromatographic retention prediction of peptides applying individual local partial least squares models

In Reversed-Phase Liquid Chromatography, Quantitative Structure-Retention Relationship (QSRR) models for retention prediction of peptides can be built, starting from large sets of theoretical molecular descriptors. Good predictive QSRR models can be obtained after selecting the most informative descriptors. Reliable retention prediction may be an aid in the correct identification of proteins/peptides in proteomics and in chromatographic method development. Traditionally, global QSRR models are built, using a calibration set containing a representative range of analytes. In this study, a strategy is presented to build individual local Partial Least Squares (PLS) models for peptides, based on selected local calibration samples, most similar to the specific query peptide to be predicted. Similar local calibration peptides are selected from a possible calibration set. The calibration samples with the lowest Euclidian distances to the query peptide are considered as most similar. Two Euclidian distances are investigated as similarity parameter, (i) in the autoscaled descriptor space and, (ii) in the PLS factor space of the global calibration samples, both after variable selection by the Final Complexity Adapted Models (FCAM) method. The predictive abilities of individual local QSRR PLS models for peptides, developed with both Euclidian distances, are found significantly better than those of two global models, i.e. before and after FCAM variable selection. The predictive abilities of the local models, developed with distances calculated in the PLS factor space, were best.

Theoretical design of novel antimalarial agents against P. falciparum strain, Dd2 through the QSAR modeling of synthesized 2'-substituted triclosan derivatives

In an attempt to design compounds with higher antimalarial activities, quantitative structure-activity relationship (QSAR) technique was utilized in the development of a molecular model for some synthesized 2′-substituted triclosan derivatives through a hybrid of the GA-MLR method. The model was found to have excellent statistical parameters (R² = 0.8919, R²_Adj = 0.8728, LOF = 0.2563). The descriptors mean effect (MF) revealed BCUTw-1l, which increases with an increase in molecular weight, to be the most contributive to the antimalarial activity. Consequently, compound 3, with the highest activities (pEC₅₀ = 6.9586) was deployed as the design template. The molecular weight of the template was increasing through substitutions of its atoms at several positions with heavier atoms/groups to increases the descriptor (BCUTw-1l) value. Twelves (12) theoretical derivatives of the template were designed where six of the designed derivatives have better activity than the design template. The most active designed compound, 3L was found to have the highest antimalarial activity (pEC₅₀ = 7.930) than that of the design template.

Theoretical calculations of molecular descriptors for anticancer activities of 1, 2, 3-triazole-pyrimidine derivatives against gastric cancer cell line (MGC-803): DFT, QSAR and docking approaches

This work used quantum chemical method via DFT to calculate molecular descriptors for the development of QSAR model to predict bioactivity (IC50- 50% inhibition concentration) of the selected 1, 2, 3-triazole-pyrimidine derivatives against receptor (human gastric cancer cell line, MGC-803). The selected molecular parameters were obtained by B3LYP/6-31G∗∗. QSAR model linked the molecular parameters of the studied compounds to their cytotoxicity and reproduced their observed bioactivities against MGC-803. The calculated IC50 tailored the observed IC50 and greater than standard compound, 5-fluorouracil, suggesting that the developed QSAR model reproduced the observed bioactivity. Statistical analyses (including R2, CV. R2 andR a 2 gave 0.950, 0.970 and 0.844 respectively) revealed a very good fitness. Molecular docking studies revealed the hydrogen bonding with the amino acid residues in the binding site, as well as ligand conformations which are essential feature for ligand-receptor interactions. Therefore, the methods used in this study are veritable tools that can be employed in pharmacological and medicinal chemistry researches in designing better drugs with improve potency.

Performance comparison of nonlinear and linear regression algorithms coupled with different attribute selection methods for quantitative structure - retention relationships modelling in micellar liquid chromatography

In micellar liquid chromatography (MLC), the addition of a surfactant to the mobile phase in excess is accompanied by an alteration of its solubilising capacity and a change in the stationary phase's properties. As an implication, the prediction of the analytes' retention in MLC mode becomes a challenging task. Mixed Quantitative Structure - Retention Relationships (QSRR) modelling represents a powerful tool for estimating the analytes' retention. This study compares 48 successfully developed mixed QSRR models with respect to their ability to predict retention of aripiprazole and its five impurities from molecular structures and factors that describe the Brij - acetonitrile system. The development of the models was based on an automatic combining of six attribute (feature) selection methods with eight predictive algorithms and the optimization of hyper-parameters. The feature selection methods included Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), ReliefF, Multiple Linear Regression (MLR), Mutual Info and F-Regression. The series of investigated predictive algorithms comprised Linear Regressions (LR), Ridge Regression, Lasso Regression, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosted Trees (GBT) and K-Nearest neighbourhood (k-NN). A sufficient amount of data for building the model (78 cases in total) was provided by conducting 13 experiments for each of the 6 analytes and collecting the target responses afterwards. Different experimental settings were established by varying the values of the concentration of Brij L23, pH of the aqueous phase and acetonitrile content in the mobile phase according to the Box-Behnken design. In addition to the chromatographic parameters, the pool of independent variables was expanded by 27 molecular descriptors from all major groups (physicochemical, quantum chemical, topological and spatial structural descriptors). The best model was chosen by taking into consideration the Root Mean Square Error (RMSE) and cross-validation (CV) correlation coefficient (Q²) values. Interestingly, the comparative analysis indicated that a change in the set of input variables had a minor impact on the performance of the final models. On the other hand, different regression algorithms showed great diversity in the ability to learn patterns conserved in the data. In this regard, testing many regression algorithms is necessary in order to find the most suitable technique for model building. In the specific case, GBT-based models have demonstrated the best ability to predict the retention factor in the MLC mode. Steric factors and dipole-dipole interactions have proven to be relevant to the observed retention behaviour. This study, although being of a smaller scale, is a most promising starting point for comprehensive MLC retention prediction.

Adsorption of phenylurea herbicides by tropical soils

The distribution of pesticides in soils with consequences for their mobility, bioavailability and water contamination is mainly ruled by sorption processes. Such processes are seldom investigated in tropical soils. Thus, specific interactions between tropical soils and most pesticides are widely unknown. Furthermore, the question arises whether the same factors govern adsorption in tropical and temperate soils. Thus, the sorption behaviour of five phenylurea herbicides (PUHs) was studied in eighteen differently composed soils originating from southwestern Nigeria. Sorption data were obtained by equilibrating the soil samples with 0.01 M CaCl2 solutions spiked with increasing concentrations of the target PUHs. The equilibrium data fitted well to the Freundlich isotherm equation (R2 ≥ 0.96), delivering the corresponding parameters (Kf and n). Linear distribution coefficients (Kd) were also calculated. The Pearson correlation was used to identify the specific soil and herbicide properties that have statistically significant correlations with sorption parameters. High correlations were established for various soil properties (pH, cation exchange capacity, organic carbon content, content of amorphous Fe and Mn oxides, clay/silt mass proportions) as well as molecular descriptors (octanol-water partition coefficient (log Kow) and molecular mass (Mw)) of the moderately hydrophobic herbicides. Monuron, chlorotoluron and isoproturon showed higher affinities for soil than previously reported. The gathered knowledge might assist in the assessment and in the precautionary avoidance of potential risks generated by these compounds in tropical soils.

MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry

AIMS

Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.).

BACKGROUND

Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.).

OBJECTIVE

Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures.

METHODS

We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied.

RESULTS

We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series.

CONCLUSION

The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.

Machine learning to predict the specific optical rotations of chiral fluorinated molecules

A chemoinformatics method was applied to the assignment of absolute configurations and to the quantitative prediction of specific optical rotations using a data set of 88 chiral fluorinated molecules (44 pairs of enantiomers). Counterpropagation neural networks were explored for the classification of enantiomers as dextrorotatory or levorotatory. Regression models were trained using multilayer perceptrons (MLP), random forests (RF) or multilinear regressions (MLR), on the basis of physicochemical atomic stereo (PAS) descriptors. New descriptors were also derived considering the common structural features of the data set (cPAS descriptors), which enabled RF models to predict the whole data set with R = 0.964, mean absolute error (MAE) of 9.8° and root mean square error (RMSE) of 12.5° in leave-one-pair-out cross-validation experiments. The predictions for the 30 compounds measured in chloroform were obtained with R = 0.971, MAE = 9.1° and RMSE = 12.5°, which compares favorably with quantum chemistry calculations reported in the literature.

The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification

Developing of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and 2D molecular descriptor calculations. Based on the intensities of two characteristic MS peaks, namely, [M] and [M-35], two classification criterions were proposed. According to criterion I, class 1 comprises [M] signals with the intensity higher than 800 NIST units, while class 2 consists of signals with the intensity lower or equal than 800. According to criterion II, class 1 consists of [M-35] signals with the intensity higher than 100, while signals with the intensity lower or equal than 100 belong to class 2. As a result of ANNs learning stage, five models for both classification criterions were generated. The external model validation showed that all ANNs are characterized by high predicting power; however, criterion I-based ANNs are much more accurate and therefore are more suitable for analytical purposes. In order to obtain another confirmation, selected ANNs were tested against additional dataset comprising popular sunscreen agents disinfection by-products reported in previous works.

QSAR studies of the antioxidant activity of anthocyanins

Through experimental information available from antioxidant assays of seventeen anthocyanins, and six common anthocyanidins, quantitative structure-activity relationships (QSAR) have been established in the present work. The antioxidant bioactivity has been predicted in three different lipid environments: emulsified and bulk oil (methyl linoleate) (in vitro tests) at concentrations of 50 and 250 μM, and 50 μM of the inhibitor, respectively, and in human LDL (low-density lipoprotein; "bad cholesterol") (ex vivo test) at concentrations of 2.5, 10, and 25 μM of the inhibitor. Radical scavenging activity was predicted in the assay with the 1,1-diphenyl-2-picrylhydrazyl radical (DPPH·). The QSAR models developed for each test and concentration used allowed to obtain prospective information on the constitutional and topological molecular characteristics for anthocyanin/anthocyanidin compounds. Therefore, the antioxidant activity was predicted for twenty-one compounds with unknown experimental values, leading for some of them to a favorable predicted bioactivity.

QSAR Models for Predicting Aquatic Toxicity of Esters Using Genetic Algorithm-Multiple Linear Regression Methods

AIM AND OBJECTIVE

Esters are of great importance in industry, medicine, and space studies. Therefore, studying the toxicity of esters is very important. In this research, a Quantitative Structure-Activity Relationship (QSAR) model was proposed for the prediction of aquatic toxicity (log 1/IGC50) of aliphatic esters towards Tetrahymena pyriformis using molecular descriptors.

MATERIALS AND METHODS

A data set of 48 aliphatic esters was separated into a training set of 34 compounds and a test set of 14 compounds. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm (GA) and Multiple Linear Regression (MLR) methods were used to select the suitable descriptors and to generate the correlation models that relate the chemical structural features to the biological activities.

RESULTS

The predictive powers of the MLR models are discussed by using Leave-One-Out (LOO) cross-validation and external test set. The best QSAR model is obtained with R2 value of 0.899, Q2 LOO =0.928, F=137.73, RMSE=0.263.

CONCLUSION

The predictive ability of the GA-MLR model with two selected molecular descriptors is satisfactory and it can be used for designing similar group and predicting of toxicity (log 1/IGC50) of ester derivatives.

How the configurational changes influence on molecular characteristics. The alkyl 3-azido-2,3-dideoxy-D-hexopyranosides - Theoretical approach

In this paper we present two currents of our research. The first goal was the geometry optimization of the structures of derivatives of the alkyl 3-azido-2,3-dideoxy-D-hexopyranosides. Then, we examine the influence of the applied quantum methods on the values of molecular features describing these structures. We use two methods for geometry optimization: the semi-empirical PM7 method and DFT B3LYP/6-31++G* method. The results of comparison parameters of descriptors indicate that there are no statistical differences obtained from both methods. Thus, we recommend the PM7 method for geometry optimization of the derivatives of the alkyl 3-azido-2,3-dideoxy-D-hexopyranosides due to its time and computer resources requirements. Another part of the research was the examination, which groups of descriptors are the most suitable for identifying the similarities in the configuration, the substituents pattern and the molecular mass of 232 examined structures. To explore relation between configuration changes of the 3-azidosaccharides and influence of these changes on the molecular characteristic, we use hierarchical cluster analysis (HCA), where WHIM, 3D-MORSE, RDF and GETAWAY descriptors were selected. Depending on the group of descriptors, molecules are divided in various ways. In general, saccharide' structures are divided into groups based on the configuration of substituents (combination of epimers) or length of the O-glycoside chain. Never before, these saccharides derivatives, were investigated by chemometric analysis. The most problematic issue in experimental and theoretical research is the configuration of substituents in pyranoside ring. Due to vast number of configurations, it is possible to obtain massive amount of diverse structures. This problem concerns us and opens opportunity in investigation the effect of configuration on the parameter of molecules.