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The modified second Zagreb index, symmetric difference index, inverse symmetric index, and augmented Zagreb index are among the molecular descriptors which have good correlations with some physicochemical properties (such as formation heat, total surface area, etc.) of chemical compounds. By a random cyclooctane chain, we mean a molecular graph of a saturated hydrocarbon containing at least two rings such that all rings are cyclooctane, every ring is joint with at most two other rings through a single bond, and exactly two rings are joint with one other ring. In this article, our main purpose is to determine the expected values of the aforementioned molecular descriptors of random cyclooctane chains explicitly. We also make comparisons in the form of explicit formulae and numerical tables consisting of the expected values of the considered descriptors of random cyclooctane chains. Moreover, we outline the graphical profiles of these comparisons among the mentioned descriptors.

Drug-resistant cases of human immunodeficiency virus (HIV) nucleoside reverse transcriptase inhibitors (NRTI) are constantly accumulating due to the frequent mutations of the reverse transcriptase (RT). Predicting the potential drug resistance of HIV-1 NRTIs could provide instructions for the proper clinical use of available drugs. In this study, a novel proteochemometric (PCM) model was constructed to predict the drug resistance between six NRTIs against different variants of RT. Forty-seven dominant mutation sites were screened using the whole protein of HIV-1 RT. Thereafter, the physicochemical properties of the dominant mutation sites can be derived to generate the protein descriptors of RT. Furthermore, by combining the molecular descriptors of NRTIs, PCM modeling can be constructed to predict the inhibition ability between RT variants and NRTIs. The results indicated that our PCM model could achieve a mean AUC value of 0.946 and a mean accuracy of 0.873 on the external validation set. Finally, based on PCM modeling, the importance of features was calculated to reveal the dominant amino acid distribution and mutation patterns on RT, to reflect the characteristics of drug-resistant sequences.

In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable yet never achieved before. This study aims to computationally predict siRNA nanoparticles in vivo efficacy, which saves time and resources. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log(dose) with R2val = 0.86 – 0.89 and 0.75 – 80 for validation sets one and two respectively. Artificial neural networks resulted in the best R2val for both validation sets. For predictions that have high bias, improvement of R2val from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques.

This paper presents the results of structure–activity relationship (SAR) studies of 140 3,3’-(α,ω-dioxaalkan)bis(1-alkylimidazolium) chlorides. In the SAR analysis, the dominance-based rough set approach (DRSA) was used. For analyzed compounds, minimum inhibitory concentration (MIC) against strains of Staphylococcus aureus and Pseudomonas aeruginosa was determined. In order to perform the SAR analysis, a tabular information system was formed, in which tested compounds were described by means of condition attributes, characterizing the structure (substructure parameters and molecular descriptors) and their surface properties, and a decision attribute, classifying compounds with respect to values of MIC. DRSA allows to induce decision rules from data describing the compounds in terms of condition and decision attributes, and to rank condition attributes with respect to relevance using a Bayesian confirmation measure. Decision rules present the most important relationships between structure and surface properties of the compounds on one hand, and their antibacterial activity on the other hand. They also indicate directions of synthesizing more efficient antibacterial compounds. Moreover, the analysis showed differences in the application of various parameters for Gram-positive and Gram-negative strains, respectively.

Pseudomonas aeruginosa is a Gram-negative bacillus included among the six "ESKAPE" microbial species with an outstanding ability to "escape" currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature, and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (Support vector classifier, K Nearest Neighbors, Random Forest Classifier, Decision Tree Classifier, AdaBoost Classifier, Logistic Regression, and Naive Bayes Classifier). The main descriptors used in building the models belonged to the families of adjacency matrix, constitutional descriptors, first highest eigenvalue of Burden matrix, centered Moreau-Broto autocorrelation, and averaged and centered Moreau-Broto autocorrelation descriptors. A total of 32 models were built, of which 28 were selected and stacked to create a meta-model. In terms of balanced accuracy, the best performance was provided by KNN, SVM and AdaBoost algorithms, but the ensemble method had slightly superior results in nested cross-validation.

Cutinases are bacterial and fungal enzymes that catalyze the hydrolysis of natural cutin, a three-dimensional inter-esterified polyester with epoxy-hydroxy fatty acids with chain lengths between 16 and 18 carbon atoms. Due to their ability to accept long chain substrates, cutinases are also effective in catalyzing in vitro both the degradation and synthesis of several synthetic polyesters and polyamides. Here, we present a bioinformatics study that intends to correlate the structural features of cutinases with their catalytic properties to provide rational basis for their effective exploitation, particularly in polymer synthesis and biodegradation. The bioinformatics study used the BioGPS method (Global Positioning System in Biological Space) that computed molecular descriptors based on Molecular Interaction Fields (MIFs) described in the GRID force field. The information was used to generate catalophores, spatial representations of the ability of each enzymatic active site to establish hydrophobic and electrostatic interactions. These tools were exploited for comparing cutinases to other serine-hydrolases enzymes, namely lipases, esterases, amidases and proteases, and for highlighting differences and similarities that might guide rational engineering strategies. Structural features of cutinases with their catalytic properties were correlated. The “catalophore” of cutinases indicate shared features with lipases and esterases.

The use of corrosion inhibitors is an important method to retard the process of metallic attack by corrosion. The construction of mathematical models from theoretical-computational and experimental data obtained for different molecules is one of the most attractive alternatives in the analysis of corrosion prevention, whose objective is to define those molecular characteristics that are common in high-performance corrosion inhibitors. This review includes data of corrosion inhibitors evaluated in different media, the most commonly studied molecular descriptors, and some examples of mathematical models generated by different researchers.

The present study describes a simple procedure to separate into patterns of similarity a large group of solvents, 259 in total, presented by 15 specific descriptors (experimentally found and theoretically predicted physicochemical parameters). Solvent data is usually characterized by its high variability, different molecular symmetry, and spatial orientation. Methods of chemometrics can usefully be used to extract and explore accurately the information contained in such data. In this order, advanced fuzzy divisive hierarchical-clustering methods were efficiently applied in the present study of a large group of solvents using specific descriptors. The fuzzy divisive hierarchical associative-clustering algorithm provides not only a fuzzy partition of the solvents investigated, but also a fuzzy partition of descriptors considered. In this way, it is possible to identify the most specific descriptors (in terms of higher, smallest, or intermediate values) to each fuzzy partition (group) of solvents. Additionally, the partitioning performed could be interpreted with respect to the molecular symmetry. The chemometric approach used for this goal is fuzzy c-means method being a semi-supervised clustering procedure. The advantage of such a clustering process is the opportunity to achieve separation of the solvents into similarity patterns with a certain degree of membership of each solvent to a certain pattern, as well as to consider possible membership of the same object (solvent) in another cluster. Partitioning based on a hybrid approach of the theoretical molecular descriptors and experimentally obtained ones permits a more straightforward separation into groups of similarity and acceptable interpretation. It was shown that an important link between objects&rsquo, groups of similarity and similarity groups of variables is achieved. Ten classes of solvents are interpreted depending on their specific descriptors, as one of the classes includes a single object and could be interpreted as an outlier. Setting the results of this research into broader perspective, it has been shown that the fuzzy clustering approach provides a useful tool for partitioning by the variables related to the main physicochemical properties of the solvents. It gets possible to offer a simple guide for solvents recognition based on theoretically calculated or experimentally found descriptors related to the physicochemical properties of the solvents.

Cystic fibrosis (CF) is the autosomal recessive disorder most recurrent in Caucasian populations. Different mutations involving the cystic fibrosis transmembrane regulator protein (CFTR) gene, which encodes the CFTR channel, are involved in CF. A number of life-prolonging therapies have been conceived and deeply investigated to combat this disease. Among them, the administration of the so-called CFTR modulators, such as correctors and potentiators, have led to quite beneficial effects. Recently, based on QSAR (quantitative structure activity relationship) studies, we reported the rational design and synthesis of compound 2, an aminoarylthiazole-VX-809 hybrid derivative exhibiting promising F508del-CFTR corrector ability. Herein, we explored the docking mode of the prototype VX-809 as well as of the aforementioned correctors in order to derive useful guidelines for the rational design of further analogues. In addition, we refined our previous QSAR analysis taking into account our first series of in-house hybrids. This allowed us to optimize the QSAR model based on the chemical structure and the potency profile of hybrids as F508del-CFTR correctors, identifying novel molecular descriptors explaining the SAR of the dataset. This study is expected to speed up the discovery process of novel potent CFTR modulators.

{Abstract] Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. The software to perform the calculation is not always available for general users. In this work, we developed the first library in R for the calculation of MCDs and we also report the first public web server for the calculation of MCDs online that include the calculation of a new class of MCDs called Markov Singular values. We also report the first Cheminformatics study of the biological activity of 5644 compounds against colorectal cancer.

Intrinsically Disordered Peptides and Proteins (IDPs) in solution can span a broad range of conformations that often are hard to characterize by both experimental and computational methods. However, obtaining a significant representation of the conformational space is important to understand mechanisms underlying protein functions such as partner recognition. In this work, we investigated the behavior of the Sic1 Kinase-Inhibitor Domain (KID) in solution by Molecular Dynamics (MD) simulations. Our results point out that application of common descriptors of molecular shape such as Solvent Accessible Surface (SAS) area can lead to misleading outcomes. Instead, more appropriate molecular descriptors can be used to define 3D structures. In particular, we exploited Weighted Holistic Invariant Molecular (WHIM) descriptors to get a coarse-grained but accurate definition of the variegated Sic1 KID conformational ensemble. We found that Sic1 is able to form a variable amount of folded structures even in absence of partners. Among them, there were some conformations very close to the structure that Sic1 is supposed to assume in the binding with its physiological complexes. Therefore, our results support the hypothesis that this protein relies on the conformational selection mechanism to recognize the correct molecular partners.

Density prediction is of great significance for molecular design of energetic materials, since detonation velocity linearly with density and detonation pressure increases with the density squared. However, the accuracy and generalization of former reported prediction models need further improvement, because most of them are derived from small data sets and few molecular descriptors. As shown in this paper, for molecules presenting brick-like shape or containing more hydrogen-bond donors the predicted densities have large negative deviations from experimental values. Thus, a molecular morphology descriptor &eta, and a hydrogen-bond descriptor Hb are introduced as correction items to build 3 new QSPR models. Besides, 3694 nitro compounds are adopted as data set by this work. The accuracies are obviously improved, and the generalizations are verified by an independent test set. At the level of B3PW91/6-31G(d,p), the effective ratios (ERs) of the 3 Equations, for &Delta, &rho, &lt, 5%, are 92.7%, 91.8%, and 93.3%, for &Delta, 2%, the values are 53.5%, 51.3%, and 54.7%. At the level of B3LYP/6-31G**, for &Delta, 5%, the values are 92.3%, 91.4% and 92.9%, 2%, the values are 53.7%, 51.4% and 53.2%.

The glass transition temperature (Tg) is an important decision parameter when synthesizing polymeric compounds or when selecting their applicability domain. In this work, the glass transition temperature of more than 100 homopolymers with saturated backbones was predicted using a neuro-evolutive technique combining Artificial Neural Networks with a modified Bacterial Foraging Optimization Algorithm. In most cases, the selected polymers have a vinyl-type backbone substituted with various groups. A few samples with an oxygen atom in a linear non-vinyl hydrocarbon main chain were also considered. Eight structural, thermophysical, and entanglement properties estimated by the quantitative structure–property relationship (QSPR) method, along with other molecular descriptors reflecting polymer composition, were considered as input data for Artificial Neural Networks. The Tg’s neural model has a 7.30% average absolute error for the training data and 12.89% for the testing one. From the sensitivity analysis, it was found that cohesive energy, from all independent parameters, has the highest influence on the modeled output.

Introduction of a new drug to the market is a challenging and resource-consuming process. Predictive models developed with the use of artificial intelligence could be the solution to the growing need for an efficient tool which brings practical and knowledge benefits, but requires a large amount of high-quality data. The aim of our project was to develop quantitative structure–activity relationship (QSAR) model predicting serotonergic activity toward the 5-HT1A receptor on the basis of a created database. The dataset was obtained using ZINC and ChEMBL databases. It contained 9440 unique compounds, yielding the largest available database of 5-HT1A ligands with specified pKi value to date. Furthermore, the predictive model was developed using automated machine learning (AutoML) methods. According to the 10-fold cross-validation (10-CV) testing procedure, the root-mean-squared error (RMSE) was 0.5437, and the coefficient of determination (R2) was 0.74. Moreover, the Shapley Additive Explanations method (SHAP) was applied to assess a more in-depth understanding of the influence of variables on the model’s predictions. According to to the problem definition, the developed model can efficiently predict the affinity value for new molecules toward the 5-HT1A receptor on the basis of their structure encoded in the form of molecular descriptors. Usage of this model in screening processes can significantly improve the process of discovery of new drugs in the field of mental diseases and anticancer therapy.

Water activity is a key factor in the development of pharmaceutical, cosmetic, and food products. In aqueous solutions of nonelectrolytes, the Norrish model provides a simple and effective way to evaluate this quantity. However, it contains a parameter, known as the Norrish constant, that must be estimated from experimental data. In this study, a new strategy is proposed for the prediction of water activity in the absence of experimental information, based on the use of theoretical molecular descriptors for characterizing the effects of a solute. This approach was applied to the evaluation of water activity in the presence of sugars (glucose, fructose, xylose, sucrose) and polyols (sorbitol, xylitol, glycerol, erythritol). The use of two descriptors related to the constitutional and connectivity properties of the solutes was first investigated. Subsequently, a new theoretical descriptor, named the global information index (G), was developed. By using this index, the water activity curves in the binary systems were reconstructed. The positive results obtained support the proposed strategy, as well as the possibility of including, in a single information index, the main molecular features of a solute that determine its effects on water activity.

Dapsone (DDS) is an antibacterial drug with well-known antioxidant properties. However, the antioxidant behavior of its derivatives has not been well explored. In the present work, the antioxidant activity of 10 dapsone derivatives 4-substituted was determined by an evaluation in two in vitro models (DPPH radical scavenging assay and ferric reducing antioxidant power). These imine derivatives 1–10 were obtained through condensation between DDS and the corresponding aromatic aldehydes 4-substuited. Three derivatives presented better results than DDS in the determination of DPPH (2, 9, and 10). Likewise, we have three compounds with better reducing activity than dapsone (4, 9, and 10). In order to be more insight, the redox process, a conceptual DFT analysis was carried out. Molecular descriptors such as electronic distribution, the total charge accepting/donating capacity (I/A), and the partial charge accepting/donating capacity (ω+/ω−) were calculated to analyze the relative donor-acceptor capacity through employing a donor acceptor map (DAM). The DFT calculation allowed us to establish a relationship between GAPHOMO-LUMO and DAM with the observed antioxidant effects. According to the results, we concluded that compounds 2 and 3 have the lowest Ra values, representing a good antioxidant behavior observed experimentally in DPPH radical capturing. On the other hand, derivatives 4, 9, and 10 display the best reducing capacity activity with the highest ω− and Rd values. Consequently, we propose these compounds as the best antireductants in our DDS imine derivative series.

The current treatment of depression involves antidepressant synthetic drugs that have a variety of side effects. In searching for alternatives, natural compounds could represent a solution, as many studies reported that such compounds modulate the nervous system and exhibit antidepressant effects. We used bioinformatics methods to predict the antidepressant effect of ten natural compounds with neuroleptic activity, reported in the literature. For all compounds we computed their drug-likeness, absorption, distribution, metabolism, excretion (ADME), and toxicity profiles. Their antidepressant and neuroleptic activities were predicted by 3D-ALMOND-QSAR models built by considering three important targets, namely serotonin transporter (SERT), 5-hydroxytryptamine receptor 1A (5-HT1A), and dopamine D2 receptor. For our QSAR models we have used the following molecular descriptors: hydrophobicity, electrostatic, and hydrogen bond donor/acceptor. Our results showed that all compounds present drug-likeness features as well as promising ADME features and no toxicity. Most compounds appear to modulate SERT, and fewer appear as ligands for 5-HT1A and D2 receptors. From our prediction, linalyl acetate appears as the only ligand for all three targets, neryl acetate appears as a ligand for SERT and D2 receptors, while 1,8-cineole appears as a ligand for 5-HT1A and D2 receptors.

H-phosphonates, H-phosphinates and secondary phosphine oxides may be preligands, and are important building blocks in the synthesis of pharmaceuticals, pesticides, and P-ligands. The prototropic tautomerism influenced by substituent effects plays an important role in the reactivity of these species. The main goal of our research was to study the tautomerism of the &gt, P(O)H reagents by means of computational investigations applying several DFT methods at different levels. We focused on the effect of implicit solvents, and on explaining the observed trends with physical chemical molecular descriptors. In addition, multiple reaction pathways incorporating three P-molecules were elucidated for the mechanism of the interconversion.

Dendrimers are branched organic macromolecules with successive layers of branch units surrounding a central core. The molecular topology and the irregularity of their structure plays a central role in determining structural properties like enthalpy and entropy. Irregularity indices which are based on the imbalance of edges are determined for the molecular graphs associated with some general classes of dendrimers. We also provide graphical analysis of these indices for the above said classes of dendrimers.

[Abstract]The prediction of metabolic activities in silico form is crucial to be able to address all research possibilities without exceeding the experimental costs. In particular, for cancer research, the prediction of certain activities can be of great help in the discovery of different treatments. In this work it has been proposed to predict, through Machine Learning, the anti-angiogenic activity of peptides is currently being used in cancer treatment and is giving hopeful results. From a list of peptide sequences, three types of molecular descriptors were obtained (AAC, DC and TC) that offered the possibility of training different ML algorithms. After a Feature Selection process, different models were obtained with a predictive value that surpassed the current state of the art. These results shown that ML is useful for the classification and prediction of the activity of new peptides, making experimental screening cheaper and faster. Instituto Carlos III; PI17/01826 Xunta de Galicia; Ref. ED431G/01 Xunta de Galicia; , ED431D 2017/16 Red Gallega de Investigación sobre Cáncer Colorrecta; Ref. ED431D 2017/23 Ministerio de Economía y Competivividad; UNLC08-1E-002 Ministerio de Economía y Competivividad; UNLC13-13-3503 Ministerio de Economía y Competivividad; FJCI- 2015-26071

Face-centered cubic lattice F C C ( n ) has received extensive consideration as of late, inferable from its recognized properties and non-poisonous nature, minimal effort, plenitude, and basic creation process. The graph of a face-centered cubic cross-section contains cube points and face centres. A topological index of a molecular graph G is a numeric amount identified with G, which depicts its topological properties. In this paper, using graph theory tools, we computed the molecular descriptors (topological indices)&mdash, to be specific, Zagreb-type indices, a forgotten index, a Balaban index, the fourth version of an atom&ndash, bond connectivity index, and the fifth version of a geometric arithmetic index for face-centered cubic lattice F C C ( n ) .

Retention in gas&ndash, liquid chromatography is mainly governed by the extent of intermolecular interactions between the solute and the stationary phase. While molecular descriptors of computational origin are commonly used to encode the effect of the solute structure in quantitative structure&ndash, retention relationship (QSRR) approaches, characterisation of stationary phases is historically based on empirical scales, the McReynolds system of phase constants being one of the most popular. In this work, poly(siloxane) stationary phases, which occupy a dominant position in modern gas&ndash, liquid chromatography, were characterised by theoretical molecular descriptors. With this aim, the first five McReynolds constants of 29 columns were modelled by multilinear regression (MLR) coupled with genetic algorithm (GA) variable selection applied to the molecular descriptors provided by software Dragon. The generalisation ability of the established GA-MLR models, evaluated by both external prediction and repeated calibration/evaluation splitting, was better than that reported in analogous studies regarding nonpolymeric (molecular) stationary phases. Principal component analysis on the significant molecular descriptors allowed to classify the poly(siloxanes) according to their chemical composition and partitioning properties. Development of QSRR-based models combining molecular descriptors of both solutes and stationary phases, which will be applied to transfer retention data among different columns, is in progress.

In recent years, there have been frequent reports on the adverse effects of synthetic cannabinoid (SC) abuse. SCs cause psychoactive effects, similar to those caused by marijuana, by binding and activating cannabinoid receptor 1 (CB1R) in the central nervous system. The aim of this study was to establish a reliable quantitative structure&ndash, activity relationship (QSAR) model to correlate the structures and physicochemical properties of various SCs with their CB1R-binding affinities. We prepared tetrahydrocannabinol (THC) and 14 SCs and their derivatives (naphthoylindoles, naphthoylnaphthalenes, benzoylindoles, and cyclohexylphenols) and determined their binding affinity to CB1R, which is known as a dependence-related target. We calculated the molecular descriptors for dataset compounds using an R/CDK (R package integrated with CDK, version 3.5.0) toolkit to build QSAR regression models. These models were established, and statistical evaluations were performed using the mlr and plsr packages in R software. The most reliable QSAR model was obtained from the partial least squares regression method via Y-randomization test and external validation. This model can be applied in vivo to predict the addictive properties of illicit new SCs. Using a limited number of dataset compounds and our own experimental activity data, we built a QSAR model for SCs with good predictability. This QSAR modeling approach provides a novel strategy for establishing an efficient tool to predict the abuse potential of various SCs and to control their illicit use.

Substances that do not degrade over time have proven to be harmful to the environment and are dangerous to living organisms. Being able to predict the biodegradability of substances without costly experiments is useful. Recently, the quantitative structure&ndash, activity relationship (QSAR) models have proposed effective solutions to this problem. However, the molecular descriptor datasets usually suffer from the problems of unbalanced class distribution, which adversely affects the efficiency and generalization of the derived models. Accordingly, this study aims at validating the performances of balanced random trees (RTs) and boosted C5.0 decision trees (DTs) to construct QSAR models to classify the ready biodegradation of substances and their abilities to deal with unbalanced data. The balanced RTs model algorithm builds individual trees using balanced bootstrap samples, while the boosted C5.0 DT is modeled using cost-sensitive learning. We employed the two-dimensional molecular descriptor dataset, which is publicly available through the University of California, Irvine (UCI) machine learning repository. The molecular descriptors were ranked according to their contributions to the balanced RTs classification process. The performance of the proposed models was compared with previously reported results. Based on the statistical measures, the experimental results showed that the proposed models outperform the classification results of the support vector machine (SVM), K-nearest neighbors (KNN), and discrimination analysis (DA). Classification measures were analyzed in terms of accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, F1 score, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUROC).

Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate-able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, and . Similar metrics, calculated on an external set of data (e.g., ), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context -&rdquo, ignorant&rdquo, In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening-aware metric, e.g., an enrichment-based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small-scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by &ldquo, classical&rdquo, metrics, e.g., and and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable and/or values were unable to pick a single active compound from within the pool whereas in other cases, models with poor and/or values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment-based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment-based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening.

Two new anticancer-active 1,2,3-triazole-dipyridothiazine hybrids were evaluated for their lipophilicity using thin-layer chromatography (TLC) and computational methods. The experimental lipophilicity was evaluated with mobile phases (mixtures of TRIS buffer and acetone), exploiting a linear correlation between the retention parameter (RM) and the volume of acetone. The relative lipophilicity parameter (RM0) was obtained by extrapolation to 0% acetone concentration. This parameter was intercorrelated with a specific hydrophobic surface area (b) revealing two congeneric subgroups: hybrids of 1,2,3-triazole-2,7-diazaphenothiazines and 1,2,3-triazole-3,6-diazaphenothiazines. The parameter RM0 was converted into the absolute lipophilicity parameter logPTLC using a calibration curve prepared on the basis of compounds of known logP values. Triazole–dipyridothiazine hybrids turned out to be medium lipophilic with logPTLC values of 1.232–2.979. The chromatographically established parameter logPTLC was compared to the calculated lipophilic parameter logPcalcd obtained with various algorithms. The lipophilicity was correlated with molecular descriptors and ADME properties. The new triazole–dipyridothiazine hybrids followed Lipinski’s rule of five. The lipophilicity of these hybrids was dependent on the substituents attached to the triazole ring and the location of the azine nitrogen atoms.

The process of encoding the structure of chemicals by molecular descriptors is a crucial step in quantitative structure-activity/property relationships (QSAR/QSPR) modeling. Since ionic liquids (ILs) are disconnected structures, various ways of representing their structure are used in the QSAR studies: the models can be based on descriptors either derived for particular ions or for the whole ionic pair. We have examined the influence of the type of IL representation (separate ions vs. ionic pairs) on the model&rsquo, s quality, the process of the automated descriptors selection and reliability of the applicability domain (AD) assessment. The result of the benchmark study showed that a less precise description of ionic liquid, based on the 2D descriptors calculated for ionic pairs, is sufficient to develop a reliable QSAR/QSPR model with the highest accuracy in terms of calibration as well as validation. Moreover, the process of a descriptors&rsquo, selection is more effective when the possible number of variables can be decreased at the beginning of model development. Additionally, 2D descriptors usually demand less effort in mechanistic interpretation and are more convenient for virtual screening studies.

Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming in vitro and in vivo studies do not reflect the complexity of the phenomenon. Complementary to wet lab methods are in silico approaches, which present a cost-efficient method for toxicity prediction. The aim of our study was to explore the capabilities of counter-propagation artificial neural networks (CPANNs) for the classification of an imbalanced dataset related to idiosyncratic drug-induced liver injury and to develop a model for prediction of the hepatotoxic potential of drugs. Genetic algorithm optimization of CPANN models was used to build models for the classification of drugs into hepatotoxic and non-hepatotoxic class using molecular descriptors. For the classification of an imbalanced dataset, we modified the classical CPANN training algorithm by integrating random subsampling into the training procedure of CPANN to improve the classification ability of CPANN. According to the number of models accepted by internal validation and according to the prediction statistics on the external set, we concluded that using an imbalanced set with balanced subsampling in each learning epoch is a better approach compared to using a fixed balanced set in the case of the counter-propagation artificial neural network learning methodology.

The physicochemical properties of classical lignans, neolignans, flavonolignans and carbohydrate-lignan conjugates (CLCs) were analysed to assess their ADMET profiles and establish if these compounds are lead-like/drug-like and thus have potential to be or act as leads in the development of future therapeutics. It was found that while no studied compounds were lead-like, a very large proportion (&gt, 75%) fulfilled all the requirements to be deemed as present in drug-like space and almost all compounds studied were in the known drug space. Principal component analysis was an effective technique that enabled the investigation of the relationship between the studied molecular descriptors and was able to separate the lignans from their sugar derivatives and flavonolignans, primarily according to the parameters that are considered when defining chemical space (i.e., number of hydrogen bond donors, acceptors, rotatable bonds, polar surface area and molecular weight). These results indicate that while CLCs and flavonolignans are less drug-like, lignans show a particularly high level of drug-likeness, an observation that coupled with their potent biological activities, demands future pursuit into their potential for use as therapeutics.

A quantitative structure-property relationship (QSPR) model is proposed to explore the relationship between the pKa of various compounds and their structures. Through QSPR studies, the relationship between the structure and properties can be obtained. In this study, a novel chaos-enhanced accelerated particle swarm algorithm (CAPSO) is adopted to screen molecular descriptors and optimize the weights of back propagation artificial neural network (BP ANN). Then, the QSPR model based on CAPSO and BP ANN is proposed and named the CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm was a reliable method for screening molecular descriptors. The five molecular descriptors obtained by the CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed that the CAPSO BP ANN model exhibited good performance in predicting the pKa values of various compounds. The absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the high prediction accuracy. The proposed hybrid intelligent model can be applied in engineering design and the prediction of physical and chemical properties.

The skin permeability (Kp) defines the rate of a chemical penetrating across the stratum corneum. This value is widely used to quantitatively describe the transport of molecules in the outermost layer of epidermal skin and indicate the significance of skin absorption. This study defined a Kp quantitative structure-activity relationship (QSAR) based on 106 chemical substances of Kp measured using human skin and interpreted the molecular interactions underlying transport behavior of small molecules in the stratum corneum. The Kp QSAR developed in this study identified four molecular descriptors that described the molecular cyclicity in the molecule reflecting local geometrical environments, topological distances between pairs of oxygen and chlorine atoms, lipophilicity, and similarity to antineoplastics in molecular properties. This Kp QSAR considered the octanol-water partition coefficient to be a direct influence on transdermal movement of molecules. Moreover, the Kp QSAR identified a sub-domain of molecular properties initially defined to describe the antineoplastic resemblance of a compound as a significant factor in affecting transdermal permeation of solutes. This finding suggests that the influence of molecular size on the chemical’s skin-permeating capability should be interpreted with other relevant physicochemical properties rather than being represented by molecular weight alone.

Critical properties and acentric factor are widely used in phase equilibrium calculations but are difficult to evaluate with high accuracy for many organic compounds. Quantitative Structure-Property Relationship (QSPR) models are a powerful tool to establish accurate correlation between molecular properties and chemical structure. QSPR multi-linear (MLR) and radial basis-function-neural-network (RBFNN) models have been developed to predict the critical temperature, critical pressure and acentric factor of a database of 306 organic compounds. RBFNN models provided better data correlation and higher predictive capability (an AAD% of 0.92&ndash, 2.0% for training and 1.7&ndash, 4.8% for validation sets) than MLR models (an AAD% of 3.2&ndash, 8.7% for training and 6.2&ndash, 12.2% for validation sets). The RMSE of the RBFNN models was 20&ndash, 30% of the MLR ones. The correlation and predictive performances of the models for critical temperature were higher than those for critical pressure and acentric factor, which was the most difficult property to predict. However, the RBFNN model for the acentric factor resulted in the lowest RMSE with respect to previous literature. The close relationship between the three properties resulted from the selected molecular descriptors, which are mostly related to molecular electronic charge distribution or polar interactions between molecules. QSPR correlations were compared with the most frequently used group-contribution methods over the same database of compounds: although the MLR models provided comparable results, the RBFNN ones resulted in significantly higher performance.

The present study is focused on the chemometric QSAR analysis of antifungal and antibacterial activity of novel 2-morpholinoquinoline analogs and their molecular structure described by in silico molecular descriptors. [...]

A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity.

The Density Functional Theory (DFT) method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with different degrees of cytotoxicity against the human hepatocellular carcinoma HepG2 line. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to select the most important descriptors related to anticancer activity. The significant molecular descriptors related to the compounds with anticancer activity were the ALOGPS_log, Mor29m, IC5 and GAP energy. The Pearson correlation between activity and most important descriptors were used for the regression partial least squares (PLS) and principal component regression (PCR) models built. The regression PLS and PCR were very close, with variation between PLS and PCR of R(2) = ± 0.0106, R(2)(ajust) = ± 0.0125, s = ± 0.0234, F(4,11) = ± 12.7802, Q(2) = ± 0.0088, SEV = ± 0.0132, PRESS = ± 0.4808 and SPRESS = ± 0.0057. These models were used to predict the anticancer activity of eight new artemisinin compounds (test set) with unknown activity, and for these new compounds were predicted pharmacokinetic properties: human intestinal absorption (HIA), cellular permeability (PCaCO2), cell permeability Maden Darby Canine Kidney (PMDCK), skin permeability (P(Skin)), plasma protein binding (PPB) and penetration of the blood-brain barrier (C(Brain/Blood)), and toxicological: mutagenicity and carcinogenicity. The test set showed for two new artemisinin compounds satisfactory results for anticancer activity and pharmacokinetic and toxicological properties. Consequently, further studies need be done to evaluate the different proposals as well as their actions, toxicity, and potential use for treatment of cancers.

An important goal for drug development within the pharmaceutical industry is the application of simple methods to determine human pharmacokinetic parameters. Effective computing tools are able to increase scientists' ability to make precise selections of chemical compounds in accordance with desired pharmacokinetic and safety profiles. This work presents a method for making predictions of the clearance, plasma protein binding, and volume of distribution for alkaloid drugs. The tools used in this method were genetic algorithms (GAs) combined with artificial neural networks (ANNs) and these were applied to select the most relevant molecular descriptors and to develop quantitative structure-pharmacokinetic relationship (QSPkR) models. Results showed that three-dimensional structural descriptors had more influence on QSPkR models. The models developed in this study were able to predict systemic clearance, volume of distribution, and plasma protein binding with normalized root mean square error (NRMSE) values of 0.151, 0.263, and 0.423, respectively. These results demonstrate an acceptable level of efficiency of the developed models for the prediction of pharmacokinetic parameters.

Parasitic infections like leishmaniasis and trypanosomiasis remain as a worldwide concern to public health. Improvement of the currently available drug discovery pipelines for those diseases is therefore mandatory. We have recently reported on the antileishmanial and antitrypanosomal activity of a set of cinnamate esters where we identified several compounds with interesting activity against L. donovani and T. brucei rhodesiense. For a better understanding of such compounds&rsquo, anti-infective activity, analyses of the underlying structure-activity relationships, especially from a quantitative point of view, would be a prerequisite for rational further development of such compounds. Thus, quantitative structure-activity relationships (QSAR) modeling for the mentioned set of compounds and their antileishmanial and antitrypanosomal activity was performed using a genetic algorithm as main variable selection tool and multiple linear regression as statistical analysis. Changes in the composition of the training/test sets were evaluated (two randomly selected and one by Kennard-Stone algorithm). The effect of the size of the models (number of descriptors) was also investigated. The quality of all resulting models was assessed by a variety of validation parameters. The models were ranked by newly introduced scoring functions accounting for the fulfillment of each of the validation criteria evaluated. The test sets were effectively within the applicability domain of the best models, which demonstrated high robustness. Detailed analysis of the molecular descriptors involved in those models revealed strong dependence of activity on the number and type of polar atoms, which affect the hydrophobic/hydrophilic properties causing a prominent influence on the investigated biological activities.

Drugs that accumulate in lysosomes reach very high tissue concentrations, which is evident in the high volume of distribution and often lower clearance of these compounds. Such a pharmacokinetic profile is beneficial for indications where high tissue penetration and a less frequent dosing regime is required. Here, we show how the level of lysosomotropic accumulation in cells can be predicted solely from molecular structure. To develop quantitative structure&ndash, activity relationship (QSAR) models, we used cellular accumulation data for 69 lysosomotropic macrocycles, the pharmaceutical class for which this type of prediction model is extremely valuable due to the importance of cellular accumulation for their anti-infective and anti-inflammatory applications as well as due to the fact that they are extremely difficult to model by computational methods because of their large size (Mw &gt, 500). For the first time, we show that five levels of intracellular lysosomotropic accumulation (as measured by liquid chromatography coupled to tandem mass spectrometry&mdash, LC-MS/MS), from low/no to extremely high, can be predicted with 60% balanced accuracy solely from the compound&rsquo, s structure. Although largely built on macrocycles, the eight non-macrocyclic compounds that were added to the set were found to be well incorporated by the models, indicating their possible broader application. By uncovering the link between the molecular structure and cellular accumulation as the key process in tissue distribution of lysosomotropic compounds, these models are applicable for directing the drug discovery process and prioritizing the compounds for synthesis with fine-tuned accumulation properties, according to the desired pharmacokinetic profile.

Deep-sea natural products have been created by unique marine organisms that thrive in a challenging environment of extreme conditions for its inhabitants. In this study, 179 deep-sea natural products isolated from 2009 to 2013 were investigated by analysing their physicochemical properties that are important indicators of the ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) profile of a compound. The study and analysis of these molecular descriptors and characteristics enabled the defining of these compounds in various chemical spaces, particularly as an indication of their drug-likeness and position in chemical space and is the first to be conducted to analyse deep-sea derived natural products. It was found that ~40% of all deep-sea natural products were drug-like and 2/3 were within Known Drug Space (KDS), highlighting the high drug-likeness of a significant proportion of deep-sea natural products, most of which have already been shown to have notable biological activities, that should be further investigated as potential therapeutics. Furthermore, this study was able to reveal the general structural differences between compounds from Animalia, Bacteria and Fungi organisms where it was observed that natural products from members of the Animalia kingdom are structurally more varied than compounds from bacteria and fungi. It was also noted that, in general, fungi-derived compounds occupy a more favourable position in drug-like chemical space and are a rich and promising source of biologically-active natural products for the purposes of drug development and therapeutic application.