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Predicting the likely biological activity (or property) of compounds is a fundamental and challenging task in the drug discovery process. Current computational methodologies aim to improve their predictive accuracies by using deep learning (DL) approaches. However, non‐DL based approaches for small‐ and medium‐sized chemical datasets have demonstrated to be most suitable for. In this approach, an initial universe of molecular descriptors (MDs) is first calculated, then different feature selection algorithms are applied, and finally, one or several predictive models are built. Herein we demonstrate that this traditional approach may miss relevant information by assuming that the initial universe of MDs codifies all relevant aspects for the respective learning task. We argue that this limitation is mainly because of the constrained intervals of the parameters used in the algorithms that compute MDs, parameters that define the Descriptor Configuration Space (DCS). We propose to relax these constraints in an open CDS approach, so that a larger universe of MDs can be initially considered. We model the generation of MDs as a multicriteria optimization problem and tackle it with a variant of the standard genetic algorithm. As a novel component, the fitness function is computed by aggregating four criteria via the Choquet integral. Experimental results show that the proposed approach generates a meaningful DCS by improving state‐of‐the‐art approaches in most of the benchmarking chemical datasets accounted for. [ABSTRACT FROM AUTHOR]

Advances to the distributed, multi‐core and fully cross‐platform QuBiLS‐MIDAS software v2.0 (http://tomocomd.com/qubils-midas) are reported in this article since the v1.0 release. The QuBiLS‐MIDAS software is the only one that computes atom‐pair and alignment‐free geometrical MDs (3D‐MDs) from several distance metrics other than the Euclidean distance, as well as alignment‐free 3D‐MDs that codify structural information regarding the relations among three and four atoms of a molecule. The most recent features added to the QuBiLS‐MIDAS software v2.0 are related (a) to the calculation of atomic weightings from indices based on the vertex‐degree invariant (e.g., Alikhanidi index); (b) to consider central chirality during the molecular encoding; (c) to use measures based on clustering methods and statistical functions to codify structural information among more than two atoms; (d) to the use of a novel method based on fuzzy membership functions to spherically truncate inter‐atomic relations; and (e) to the use of weighted and fuzzy aggregation operators to compute global 3D‐MDs according to the importance and/or interrelation of the atoms of a molecule during the molecular encoding. Moreover, a novel module to compute QuBiLS‐MIDAS 3D‐MDs from their headings was also developed. This module can be used either by the graphical user interface or by means of the software library. By using the library, both the predictive models built with the QuBiLS‐MIDAS 3D‐MDs and the QuBiLS‐MIDAS 3D‐MDs calculation can be embedded in other tools. A set of predefined QuBiLS‐MIDAS 3D‐MDs with high information content and low redundancy on a set comprised of 20,469 compounds is also provided to be employed in further cheminformatics tasks. This set of predefined 3D‐MDs evidenced better performance than all the universe of Dragon (v5.5) and PaDEL 0D‐to‐3D MDs in variability studies, whereas a linear independence study proved that these QuBiLS‐MIDAS 3D‐MDs codify chemical information orthogonal to the Dragon 0D‐to‐3D MDs. This set of predefined 3D‐MDs would be periodically updated as long as new results be achieved. In general, this report highlights our continued efforts to provide a better tool for a most suitable characterization of compounds, and in this way, to contribute to obtaining better outcomes in future applications. [ABSTRACT FROM AUTHOR]

A novel spherical truncation method, based on fuzzy membership functions, is introduced to truncate interatomic (or interaminoacid) relations according to smoothing values computed from fuzzy membership degrees. In this method, the molecules are circumscribed into a sphere, so that the geometric centers of the molecules are the centers of the spheres. The fuzzy membership degree of each atom (or aminoacid) is computed from its distance with respect to the geometric center of the molecule, by using a fuzzy membership function. So, the smoothing value to be applied in the truncation of a relation (or interaction) is computed by averaging the fuzzy membership degrees of the atoms (or aminoacids) involved in the relation. This truncation method is rather different from the existing ones, at considering the geometric center for the whole molecule and not only for atom‐groups, as well as for using fuzzy membership functions to compute the smoothing values. A variability study on a set comprised of 20,469 compounds (15,050 drug‐like compounds, 2994 drugs approved, 880 natural products from African sources, and 1545 plant‐derived natural compounds exhibiting anti‐cancerous activity) demonstrated that the truncation method proposed allows to determine molecular encodings with better ability for discriminating among structurally different molecules than the encodings obtained without applying truncation or applying non‐fuzzy truncation functions. Moreover, a principal component analysis revealed that orthogonal chemical information of the molecules is encoded by using the method proposed. Lastly, a modeling study proved that the truncation method improves the modeling ability of existing geometric molecular descriptors, at allowing to develop more robust models than the ones built only using non‐truncated descriptors. In this sense, a comparison and statistical assessment were performed on eight chemical datasets. As a result, the models based on the truncated molecular encodings yielded statistically better results than 12 procedures considered from the literature. It can thus be stated that the proposed truncation method is a relevant strategy for obtaining better molecular encodings, which will be ultimately useful in enhancing the modeling ability of existing encodings both on small‐to‐medium size molecules and biomacromolecules. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) is addressed via a computational approach. The BBB passage was modeled using classification and regression schemes on a novel extensive and curated data set (the largest to the best of our knowledge) in terms of log BB. Prior to the model development, steps of data analysis that comprise chemical data curation, structural, cutoff and cluster analysis (CA) were conducted. Linear Discriminant Analysis (LDA) and Multiple Linear Regression (MLR) were used to fit classification and correlation functions. The best LDA-based model showed overall accuracies over 85 % and 83 % for the training and test sets, respectively. Also a MLR-based model with acceptable explanation of more than 69 % of the variance in the experimental log BB was developed. A brief and general interpretation of proposed models allowed the estimation on how 'near' our computational approach is to the factors that determine the passage of molecules through the BBB. In a final effort some popular and powerful Machine Learning methods were considered. Comparable or similar performance was observed respect to the simpler linear techniques. Most of the compounds with anomalous behavior were put aside into a set denoted as controversial set and discussion regarding to these compounds is provided. Finally, our results were compared with methodologies previously reported in the literature showing comparable to better results. The results could represent useful tools available and reproducible by all scientific community in the early stages of neuropharmaceutical drug discovery/development projects. [ABSTRACT FROM AUTHOR]

The present report introduces a novel module of the QuBiLS-MIDAS software for the distributed computation of the 3D Multi-Linear algebraic molecular indices. The main motivation for developing this module is to deal with the computational complexity experienced during the calculation of the descriptors over large datasets. To accomplish this task, a multi-server computing platform named T-arenal was developed, which is suited for institutions with many workstations interconnected through a local network and without resources particularly destined for computation tasks. This new system was deployed in 337 workstations and it was perfectly integrated with the QuBiLS-MIDAS software. To illustrate the usability of the T-arenal platform, performance tests over a dataset comprised of 15 000 compounds are carried out, yielding a 52 and 60 fold reduction in the sequential processing time for the 2-Linear and 3-Linear indices, respectively. Therefore, it can be stated that the T-arenal based distribution of computation tasks constitutes a suitable strategy for performing high-throughput calculations of 3D Multi-Linear descriptors over thousands of chemical structures for posterior QSAR and/or ADME-Tox studies. [ABSTRACT FROM AUTHOR]

The present report introduces the QuBiLS-MIDAS software belonging to the ToMoCoMD-CARDD suite for the calculation of three-dimensional molecular descriptors (MDs) based on the two-linear (bilinear), three-linear, and four-linear (multilinear or N-linear) algebraic forms. Thus, it is unique software that computes these tensor-based indices. These descriptors, establish relations for two, three, and four atoms by using several (dis-)similarity metrics or multimetrics, matrix transformations, cutoffs, local calculations and aggregation operators. The theoretical background of these N-linear indices is also presented. The QuBiLS-MIDAS software was developed in the Java programming language and employs the Chemical Development Kit library for the manipulation of the chemical structures and the calculation of the atomic properties. This software is composed by a desktop user-friendly interface and an Abstract Programming Interface library. The former was created to simplify the configuration of the different options of the MDs, whereas the library was designed to allow its easy integration to other software for chemoinformatics applications. This program provides functionalities for data cleaning tasks and for batch processing of the molecular indices. In addition, it offers parallel calculation of the MDs through the use of all available processors in current computers. The studies of complexity of the main algorithms demonstrate that these were efficiently implemented with respect to their trivial implementation. Lastly, the performance tests reveal that this software has a suitable behavior when the amount of processors is increased. Therefore, the QuBiLS-MIDAS software constitutes a useful application for the computation of the molecular indices based on N-linear algebraic maps and it can be used freely to perform chemoinformatics studies. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

This report presents a new mathematical method based on the concept of the derivative of a molecular graph (G) with respect to a given event (S) to codify chemical structure information. The derivate over each pair of atoms in the molecule is defined as ∂G/∂S( vi , vj)=( fi−2 fij+ fj)/ fij, where fi (or fj) and fij are the individual frequency of atom i (or j) and the reciprocal frequency of the atoms i and j, respectively. These frequencies characterize the participation intensity of atom pairs in S. Here, the event space is composed of molecular sub-graphs which participate in the formation of the G skeleton that could be complete (representing all possible connected sub-graphs) or comprised of sub-graphs of certain orders or types or combinations of these. The atom level graph derivative index, Δ i, is expressed as a linear combination of all atom pair derivatives that include the atomic nuclei i. Global [total or local (group or atom-type)] indices are obtained by applying the so called invariants over a vector of Δ i values. The novel MDs are validated using a data set of 28 alkyl-alcohols and other benchmark data sets proposed by the International Academy of Mathematical Chemistry. Also, the boiling point for the alcohols, the adrenergic blocking activity of N, N-dimethyl-2-halo-phenethylamines and physicochemical properties of polychlorinated biphenyls and octanes are modeled. These models exhibit satisfactory predictive power compared with other 0-3D indices implemented successfully by other researchers. In addition, tendencies of the proposed indices are investigated using examples of various types of molecular structures, including chain-lengthening, branching, heteroatoms-content, and multiple bonds. On the other hand, the relation of atom-based derivative indices with 17O NMR of a series of ethers and carbonyls reflects that the new MDs encode electronic, topological and steric information. Linear independence between the graph derivative indices and other 0-3D MDs is demonstrated by using principal component analysis on a dataset of 41 heterogeneous molecules. It is concluded that the graph derivative indices are independent indices containing important structural information to be used in QSPR/QSAR and drug design studies, and permit obtaining easier, more interpretable and robust mathematical models than the majority of those reported in the literature. [ABSTRACT FROM AUTHOR]

ABSTRACT The compound VAM2-6 (1-methyl-7-nitro-4-(5-(piperidin-1-yl)pentyl)-3,4-dihydroquinoxalin-2(1H)-one) has previously been shown to have an in vitro efficacy of 100% at a concentration of 100 µg ml-1 against Trichomonas vaginalis, a protozoon parasite that causes the sexually transmitted disease trichomoniasis. Because VAM2-6 is a quinoxaline derivative and given the lack of studies on the genotoxic activity of this compound, the present study was undertaken to evaluate its ability to induce DNA damage in the peripheral blood of mice using single-cell gel electrophoresis (SCGE or comet assay) and the micronucleus (MN) assay. Cell viability was assessed using a fluorochrome-mediated viability test. The compound was tested on CD1 mice at 60, 40 and 10 mg kg-1 body weight administrated intraperitoneal (i.p.) in a single dose. Peripheral blood samples were collected 24 and 48 h after treatment. N-Ethyl- N-nitrosourea (ENU) was used as a positive control for the comet and micronucleus assays. The results showed that i.p. VAM2-6 induced single-strand DNA breaks and increased the average number of micronuclei in the treated mice in a dose-dependent manner at 60, 40 and 10 mg kg-1. Cell viability decreased at 24 h but recovered at 48 h for all three evaluated doses. Therefore, the chemical structure of VAM2-6 should be modified to reduce its genotoxic potential. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Graph-theoretic matrix representations constitute the most popular and significant source of topological molecular descriptors (MDs). Recently, we have introduced a novel matrix representation, named the duplex relations frequency matrix, F, derived from the generalization of an incidence matrix whose row entries are connected subgraphs of a given molecular graph G. Using this matrix, a series of information indices (IFIs) were proposed. In this report, an extension of F is presented, introducing for the first time the concept of a hypermatrix in graph-theoretic chemistry. The hypermatrix representation explores the n-tuple participation frequencies of vertices in a set of connected subgraphs of G. In this study we, however, focus on triple and quadruple participation frequencies, generating triple and quadruple relations frequency matrices, respectively. The introduction of hypermatrices allows us to redefine the recently proposed MDs, that is, the mutual, conditional, and joint entropy-based IFIs, in a generalized way. These IFIs are implemented in GT-STAF (acronym for Graph Theoretical Thermodynamic STAte Functions), a new module of the TOMOCOMD-CARDD program. Information theoretic-based variability analysis of the proposed IFIs suggests that the use of hypermatrices enhances the entropy and, hence, the variability of the previously proposed IFIs, especially the conditional and mutual entropy based IFIs. The predictive capacity of the proposed IFIs was evaluated by the analysis of the regression models, obtained for physico-chemical properties the partition coefficient (Log P) and the specific rate constant (Log K) of 34 derivatives of 2-furylethylene. The statistical parameters, for the best models obtained for these properties, were compared to those reported in the literature depicting better performance. This result suggests that the use of the hypermatrix-based approach, in the redefinition of the previously proposed IFIs, avails yet other valuable tools beneficial in QSPR studies and diversity analysis. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Atom-based bilinear indices and linear discriminant analysis are used to discover novel trypanosomicidal compounds. The obtained linear discriminant analysis-based quantitative structure-activity relationship models, using non-stochastic and stochastic indices, provide accuracies of 89.02% (85.11%) and 89.60% (88.30%) of the chemicals in the training (test) sets, respectively. Later, both models were applied to the virtual screening of 18 in-house synthesized compounds to find new pro-lead antitrypanosomal agents. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Predictions agree with experimental results to a great extent (16/18) of the chemicals. Sixteen compounds show more than 70% of epimastigote inhibition at a concentration 100 μg/mL. In addition, three compounds ( CRIS 112, CRIS 140 and CRIS 147) present more than 70% of epimastigote inhibition at a concentration of 10 μg/mL (79.95%, 73.97% and 78.13%, respectively) with low values of cytotoxicity (19.7%, 7.44% and 20.63%, correspondingly).Taking into account all these results, we could say that these three compounds could be optimized in forthcoming works. Even though none of them resulted more active than nifurtimox, the current results constitute a step forward in the search for efficient ways to discover new lead antitrypanosomals. [ABSTRACT FROM AUTHOR]

A number of vanilloids have been tested as tyrosinase inhibitors using Ligand-Based Virtual Screening (LBVS) driven by QSAR (Quantitative Structure-Activity Relationship) models as the multi-agent classification system. A total of 81 models were used to screen this family. Then, a preliminary cluster analysis of the selected chemicals was carried out based on their bioactivity to detect possible similar substructural features among these compounds and the active database used in the QSAR model construction. The compounds identified were tested in vitro to corroborate the results obtained in silico. Among them, two chemicals, isovanillin ( K = 1.08 mM) near to kojic acid (reference drug) in one cluster and isovanillyl alcohol ( K = 0.88 mM) at the same distance as hydroquinone (reference drug) in another cluster showed inhibitory activity against tyrosinase. The algorithm proposed here could result in a suitable approach for faster and more effective identification of hit and/or lead compounds with tyrosinase inhibitory activity, helping to shorten the long pipeline in the research of novel depigmenting agents to treat skin disorders. Copyright © 2010 John Wiley & Sons, Ltd. A number of vanilloids have been tested as tyrosinase inhibitors using Ligand−Based Virtual Screening (LBVS) driven by QSAR (Quantitative Structure−Activity Relationship) models as the multi−agent classification system. The algorithm proposed here could result in a suitable approach for more effective identification of compounds with tyrosinase inhibitory activity, helping in the research of novel depigmenting agents to treat skin disorders. [ABSTRACT FROM AUTHOR]

Bond-extended stochastic and nonstochastic bilinear indices are introduced in this article as novel bond-level molecular descriptors (MDs). These novel totals (whole-molecule) MDs are based on bilinear maps (forms) similar to use defined in linear algebra. The proposed nonstochastic indices try to match molecular structure provided by the molecular topology by using the kth Edge(Bond)-Adjacency Matrix (, designed here as a nonstochastic matrix). The stochastic parameters are computed by using the kth stochastic edge-adjacency matrix, , as matrix operators of bilinear transformations. This new edge (bond)-adjacency relationship can be obtained directly from and can be considered like a new matrix-transformation strategic to obtain new relations for a molecular graph. In both set of MDs, chemical information is codified by using different pair combinations of atomic weightings (in this case four atomic-labels: atomic mass, polarizability, van der Waals volume, and electronegativity). In addition, a local-fragment (bond-type) formalism was also developed. The kth bond-type bilinear indices are calculated by summing the kth bond bilinear indices of all bonds of the same bond type in the molecules. The new set of MDs can be easily and quickly calculated in our in house software TOMOCOMD-CARDD (topological molecular computational design computer-aided-rational-drug design). The reported application and utilization of these MDs for predictive capability correlations of structure with physicochemical and pharmacological properties are reviewed. Three benchmark datasets have been used to evaluate the QSPR/QSAR behavior of the new bond-level TOMOCOMD-CARDD MDs. We developed the QSPR models to describe several physicochemical properties of octane isomers (First Case) and, to analyze of the boiling point of 28 alkyl-alcohols (Second Case) and to examine of the specific rate constant (log k), the partition coefficient (log P), as well as the antibacterial activity of 34 derivatives of 2-furylethylenes (Third Case). For these three rounds, the quantitative models found are significant from a statistical point of view and permit a clear interpretation of the studied properties in terms of the structural features of molecules. A leave-one-out cross-validation procedure revealed that the regression models had a good predictability. The comparison with other approaches reveals good performance of the method proposed. Therefore, it is clearly demonstrated that this suitability is higher than that shown by other 2D/3D well-known sets of MDs. The approach described here appears to be a very promising structural invariant, useful for QSPR/QSAR studies and shown to provide an excellent alternative or guides for discovery and optimization of new lead compounds, reducing the time and cost of the traditional procedure. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2011 [ABSTRACT FROM AUTHOR]

In this report, we show the results of quantitative structure-activity relationship (QSAR) studies of tyrosinase inhibitory activity, by using the bond-based quadratic indices as molecular descriptors (MDs) and linear discriminant analysis (LDA), to generate discriminant functions to predict the anti-tyrosinase activity. The best two models [Eqs () and ()] out of the total 12 QSAR models developed here show accuracies of 93.51% and 91.21%, as well as high Matthews correlation coefficients ( C) of 0.86 and 0.82, respectively, in the training set. The validation external series depicts values of 90.00% and 89.44% for these best two equations () and (), respectively. Afterwards, a second external prediction data are used to perform a virtual screening of compounds reported in the literature as active (tyrosinase inhibitors). In a final step, a series of lignans is analysed using the in silico-developed models, and in vitro corroboration of the activity is carried out. An issue of great importance to remark here is that all compounds present greater inhibition values than Kojic Acid (standard tyrosinase inhibitor: IC = 16.67 μ). The current obtained results could be used as a framework to increase the speed, in the biosilico discovery of leads for the treatment of skin disorders. [ABSTRACT FROM AUTHOR]

Descriptors calculated from a specific representation scheme encode only one part of the chemical information. For this reason, there is a need to construct novel graphical representations of proteins and novel protein descriptors that can provide new information about the structure of proteins. Here, a new set of protein descriptors based on computation of bilinear maps is presented. This novel approach to biomacromolecular design is relevant for QSPR studies on proteins. Protein bilinear indices are calculated from the kth power of nonstochastic and stochastic graph–theoretic electronic-contact matrices, and , respectively. That is to say, the kth nonstochastic and stochastic protein bilinear indices are calculated using and as matrix operators of bilinear transformations. Moreover, biochemical information is codified by using different pair combinations of amino acid properties as weightings. Classification models based on a protein bilinear descriptor that discriminate between Arc mutants of stability similar or inferior to the wild-type form were developed. These equations permitted the correct classification of more than 90% of the mutants in training and test sets, respectively. To predict tm and values for Arc mutants, multiple linear regression and piecewise linear regression models were developed. The multiple linear regression models obtained accounted for 83% of the variance of the experimental tm. Statistics calculated from internal and external validation procedures demonstrated robustness, stability and suitable power ability for all models. The results achieved demonstrate the ability of protein bilinear indices to encode biochemical information related to those structural changes significantly influencing the Arc repressor stability when punctual mutations are induced. [ABSTRACT FROM AUTHOR]

The recently introduced non-stochastic and stochastic bond-based linear indices are been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. These improved modified descriptors are applied to several well-known data sets to validate each one of them. Particularly, Cramer's steroid data set has become a benchmark for the assessment of novel quantitative structure activity relationship methods. This data set has been used by several researchers using 3D-QSAR approaches such as Comparative Molecular Field Analysis, Molecular Quantum Similarity Measures, Comparative Molecular Moment Analysis, E-state, Mapping Property Distributions of Molecular Surfaces, and so on. For that reason, it is selected by us for the sake of comparability. In addition, to evaluate the effectiveness of this novel approach in drug design we model the angiotensin-converting enzyme inhibitory activity of perindoprilate's σ-stereoisomers combinatorial library, as well as codify information related to a pharmacological property highly dependent on the molecular symmetry of a set of seven pairs of chiral N-alkylated 3-(3-hydroxyphenyl)-piperidines that bind σ-receptors. The validation of this method is achieved by comparison with earlier publications applied to the same data sets. The non-stochastic and stochastic bond-based 3D-chiral linear indices appear to provide a very interesting alternative to other more common 3D-QSAR descriptors. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008 [ABSTRACT FROM AUTHOR]

The great cost associated with the development of new anabolic–androgenic steroid (AASs) makes necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, quantum, and physicochemical molecular descriptors, plus linear discriminant analysis (LDA) were used to analyze the anabolic/androgenic activity of structurally diverse steroids and to discover novel AASs, as well as also to give a structural interpretation of their anabolic–androgenic ratio (AAR). The obtained models are able to correctly classify 91.67% (86.27%) of the AASs in the training (test) sets, respectively. The results of predictions on the 10% full-out cross-validation test also evidence the robustness of the obtained model. Moreover, these classification functions are applied to an “in house” library of chemicals, to find novel AASs. Two new AASs are synthesized and tested for in vivo activity. Although both AASs are less active than some commercially AASs, this result leaves a door open to a virtual variational study of the structure of the two compounds, to improve their biological activity. The LDA-assisted QSAR models presented here, could significantly reduce the number of synthesized and tested AASs, as well as could increase the chance of finding new chemical entities with higher AAR. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2008 [ABSTRACT FROM AUTHOR]

The aim of the present study was to evaluate the fasciolicide activity of three experimental drugs, selected by an in silico system called TOMOCOMD–CARDD, in sheep. Drugs were identified by the computer system, and, after statistical selection, 24 Pelibuey sheep were infected on days 0 and 30, each with 200 metacercariae of Fasciola hepatica. When the infection reached 8 and 4 weeks of age, respectively, four groups of six animals each were formed. Group 1 received thiacetazone 150 mg/animal/p.o. Group 2 was treated with 3,5,5, trimethyloxazolidine 2,4-dione at 450 mg/animal/p.o. G3 received guanabenz acetate at a dose of 1.5 mg/animal/p.o. G4 served as an untreated control. Monitoring of the animals was followed by individual coprological examinations and slaughter of the animals 15 days after treatment to collect and count flukes from the liver. Efficacy was measured as the reduction in the percentage of flukes of treated animals relative to untreated controls. Results indicated an efficacy of 80.0, 43.8, and 100% for 8-week-old flukes and 62.1, 57.9, and 100% for 4-week-old flukes in the three experimental groups, respectively. Even though guanabenz acetate showed a high efficacy, it was highly toxic since two animals died approximately 24 h after being treated. We conclude that further investigations should be conducted to perform computer-aided prediction of drugs aimed to detect fasciolicide activity. [ABSTRACT FROM AUTHOR]

A different perspective to compute global weighted definitions of molecular descriptors from the contributions of each atom (LOVIs) or covalent bond (LOEIs) within a molecule is presented, using the generalized ordered weighted averaging – weighted averaging (GOWAWA) aggregation operator. This operator is rather different from the other norm‐, mean‐ and statistic‐based operators used up to date for the descriptors calculation from LOVIs/LOEIs. GOWAWA unifies the generalized ordered weighted averaging (GOWA) and the weighted generalized mean (WGM) functions and, in addition, it uses a smoothing parameter to assign different importance values to both functions depending on the problem under study. With the GOWAWA operator, diversity of novel global aggregations of molecular descriptors can be determined, where the influence that each atom (or covalent bond) has on the molecular characterization is taken into account. Therefore, this approach is completely different from the ones reported in the literature, where the values of LOVIs/LOEIs are considered equally important. To demonstrate the feasibility of using this operator, the QuBiLS‐MIDAS descriptors (http://tomocomd.com/qubils‐midas) were used and, as a result, a module was built into the corresponding software to compute them, being thus the only software reported in the literature that can be employed to determine weighted descriptors. Moreover, several modeling studies were performed on eight chemical datasets, which demonstrated that, with the GOWAWA aggregation operator, weighted QuBiLS‐MIDAS descriptors that contribute to develop models with greater predictive power can be computed, if compared to the models based on the non‐weighted descriptors calculated from the other operators used up to date. A non‐parametric statistical assessment confirmed that the GOWAWA‐based predictions are significantly superior to the others obtained. Therefore, all in all, it can be concluded that, from the results achieved, the GOWAWA operator constitutes a prominent alternative to codify relevant chemical information of the molecules, ultimately useful in improving the modeling ability of several old and recent descriptors whose definition is based on the LOVIs/LOEIs calculation. [ABSTRACT FROM AUTHOR]