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The rise in multidrug-resistant bacteria highlights the critical need for novel antibiotics. This study explores clovibactin-like compounds as potential therapeutic agents targeting lipid II, a crucial component in bacterial cell wall synthesis, using in silico techniques. A total of 2624 clovibactin analogs were sourced from the PubChem database and screened using ProTox 3.0 software based on their ADME-Tox properties, prioritizing candidates with favorable pharmacokinetic profiles and minimal toxicity. Molecular docking protocols were then employed to assess the binding interactions of the selected compounds with lipid II. Our analysis identified Compound 22 as a particularly promising candidate, exhibiting strong binding affinity, stable complex formation, and high selectivity for the target. Binding energy analysis, conducted via molecular dynamics simulations, revealed a highly negative value of -25.50 kcal/mol for Compound 22, surpassing that of clovibactin and underscoring its potential efficacy. In addition, Compound 22 was prioritized due to its exceptional binding affinity to lipid II and its favorable ADME-Tox properties, suggesting a lower likelihood of adverse effects. These characteristics position Compound 22 as a promising candidate for further pharmacological development. While our computational results are encouraging, experimental validation is essential to confirm the efficacy and safety of these compounds. This study not only advances our understanding of clovibactin analogs but also contributes to the ongoing efforts to combat antimicrobial resistance through innovative antibiotic development. more...

Context: This study investigates the potential of leveraging molecular properties, as determined by MD-LOVIs software and machine learning techniques, to predict the ability of compounds to cross the blood-brain barrier (BBB). Accurate prediction of BBB permeation is critical for the development of central nervous system (CNS) drugs. The study applies various machine learning models, including both classification and regression techniques, to predict BBB passage and molecular activity. Notably, classification models such as GBM-AWV (accuracy = 0.801), GLM-CN (accuracy = 0.808), SVMPoly-means (accuracy = 0.980), SVMPoly-AC (accuracy = 0.980), SVMPoly-MI_TI_SI (accuracy = 0.900), SVMPoly-GI (accuracy = 0.900), RF-means (accuracy = 0.870), and GLM-means (accuracy = 0.818) demonstrate high accuracy in predicting BBB passage. In contrast, regression models like ES-RLM-AG (R 2  = 0.902), IB-IBK (R 2  = 0.82), IB-Kstar (R 2  = 0.834), IB-MLP (R 2  = 0.843), and DRF-AWV (R 2  = 0.810) exhibit strong performance in predicting molecular activity. The results show that classification models like GBM-AWV, GLM-CN, and SVMPoly variants, as well as regression models like ES-RLM-AG and IB-MLP, achieve high performance, demonstrating the effectiveness of machine learning in predicting BBB permeability., Methods: The computational methods employed in this study include the MD-LOVIs software for generating molecular descriptors and several machine learning algorithms, including gradient boosting machines (GBM), generalized linear models (GLM), support vector machines (SVM) with polynomial kernels, random forests (RF), ensemble regression models, and instance-based learning algorithms. These models were trained and validated using various datasets to predict BBB passage and molecular activity, with the performance metrics reported for each model. Standard computational techniques were employed throughout, ensuring the reliability of the predictions., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.) more...

Peptides are promising drug development frameworks that have been hindered by intrinsic undesired properties including hemolytic activity. We aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and data mining. Metadata networks (METNs) were useful to characterize and find general patterns associated with hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), represented the hemolytic peptide space. The best candidate HSPNs were used to extract various subsets of hemolytic peptides (scaffolds) considering network centrality and peptide similarity. These scaffolds have been proved to be useful in developing robust similarity-based model classifiers. Finally, using an alignment-free approach, we reported 47 putative hemolytic motifs, which can be used as toxic signatures when developing novel peptide-based drugs. We provided evidence that the number of hemolytic motifs in a sequence might be related to the likelihood of being hemolytic., (© 2024. The Author(s).) more...

Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several researchers have applied cheminformatics and artificial intelligence methods to study the inhibition of proteasomes, including the prediction of UPP inhibitors. Following this idea, we applied a new tool for obtaining molecular descriptors (MDs) for modeling proteasome Inhibition in terms of EC 50 (µmol/L), in which a set of new MDs called atomic weighted vectors (AWV) and several prediction algorithms were used in cheminformatics studies. In the manuscript, a set of descriptors based on AWV are presented as datasets for training different machine learning techniques, such as linear regression, multiple linear regression (MLR), random forest (RF), K-nearest neighbors (IBK), multi-layer perceptron, best-first search, and genetic algorithm. The results suggest that these atomic descriptors allow adequate modeling of proteasome inhibitors despite artificial intelligence techniques, as a variant to build efficient models for the prediction of inhibitory activity., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.) more...

The role of the gut microbiota and its interplay with host metabolic health, particularly in the context of type 2 diabetes mellitus (T2DM) management, is garnering increasing attention. Dipeptidyl peptidase 4 (DPP4) inhibitors, commonly known as gliptins, constitute a class of drugs extensively used in T2DM treatment. However, their potential interactions with gut microbiota remain poorly understood. In this study, we employed computational methodologies to investigate the binding affinities of various gliptins to DPP4-like homologs produced by intestinal bacteria. The 3D structures of DPP4 homologs from gut microbiota species, including Segatella copri , Phocaeicola vulgatus , Bacteroides uniformis , Parabacteroides merdae , and Alistipes sp., were predicted using computational modeling techniques. Subsequently, molecular dynamics simulations were conducted for 200 ns to ensure the stability of the predicted structures. Stable structures were then utilized to predict the binding interactions with known gliptins through molecular docking algorithms. Our results revealed binding similarities of gliptins toward bacterial DPP4 homologs compared to human DPP4. Specifically, certain gliptins exhibited similar binding scores to bacterial DPP4 homologs as they did with human DPP4, suggesting a potential interaction of these drugs with gut microbiota. These findings could help in understanding the interplay between gliptins and gut microbiota DPP4 homologs, considering the intricate relationship between the host metabolism and microbial communities in the gut. more...

The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported. more...

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.) more...

Inflammasomes are multiprotein complexes that represent critical elements of the inflammatory response. The dysregulation of the best-characterized complex, the NLRP3 inflammasome, has been linked to the pathogenesis of diseases such as multiple sclerosis, type 2 diabetes mellitus, Alzheimer's disease, and cancer. While there exist molecular inhibitors specific for the various components of inflammasome complexes, no currently reported inhibitors specifically target NLRP3 PYD homo-oligomerization. In the present study, we describe the identification of QM380 and QM381 as NLRP3 PYD homo-oligomerization inhibitors after screening small molecules from the MyriaScreen library using a split-luciferase complementation assay. Our results demonstrate that these NLRP3 PYD inhibitors interfere with ASC speck formation, inhibit pro-inflammatory cytokine IL1-β release, and decrease pyroptotic cell death. We employed spectroscopic techniques and computational docking analyses with QM380 and QM381 and the PYD domain to confirm the experimental results and predict possible mechanisms underlying the inhibition of NLRP3 PYD homo-interactions. more...

Multiple substances are considered endocrine disrupting chemicals (EDCs). However, there is a significant gap in the early prioritization of EDC's effects. In this work, in silico and in vitro methods were used to model estrogenicity. Two Quantitative Structure-Activity Relationship (QSAR) models based on Logistic Regression and REPTree algorithms were built using a large and diverse database of estrogen receptor (ESR) agonism. A 10-fold external validation demonstrated their robustness and predictive capacity. Mechanistic interpretations of the molecular descriptors (C-026, nArOH,PW5, B06[Br-Br]) used for modelling suggested that the heteroatomic fragments, aromatic hydroxyls, and bromines, and the relative bond accessibility areas of molecules, are structural determinants in estrogenicity. As validation of the QSARs, ESR transactivity of thirteen persistent organic pollutants (POPs) and suspected EDCs was tested in vitro using the MMV-Luc cell line. A good correspondence between predictions and experimental bioassays demonstrated the value of the QSARs for prioritization of ESR agonist compounds., (Copyright © 2021 Elsevier B.V. All rights reserved.) more...

Scientific and consumer interest in healthy foods (also known as functional foods), nutraceuticals and cosmeceuticals has increased in the recent years, leading to an increased presence of these products in the market. However, the regulations across different countries that define the type of claims that may be made, and the degree of evidence required to support these claims, are rather inconsistent. Moreover, there is also controversy on the effectiveness and biological mode of action of many of these products, which should undergo an exhaustive approval process to guarantee the consumer rights. Computational approaches constitute invaluable tools to facilitate the discovery of bioactive molecules and provide biological plausibility on the mode of action of these products. Indeed, methodologies like QSAR, docking or molecular dynamics have been used in drug discovery protocols for decades and can now aid in the discovery of bioactive food components. Thanks to these approaches, it is possible to search for new functions in food constituents, which may be part of our daily diet, and help to prevent disorders like diabetes, hypercholesterolemia or obesity. In the present manuscript, computational studies applied to this field are reviewed to illustrate the potential of these approaches to guide the first screening steps and the mechanistic studies of nutraceutical, cosmeceutical and functional foods., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.) more...

Background: There is currently no effective dengue virus (DENV) therapeutic. We aim to develop a genetic algorithm-based framework for the design of peptides with possible DENV inhibitory activity. Methods & results: A Python-based tool (denominated AutoPepGEN) based on a DENV support vector machine classifier as the objective function was implemented. AutoPepGEN was applied to the design of three- to seven-amino acid sequences and ten peptides were selected. Peptide-protease (DENV) docking and Molecular Mechanics-Generalized Born Surface Area calculations were performed for the selected sequences and favorable binding energies were observed. Conclusion: It is hoped that AutoPepGEN will serve as an in silico alternative to the experimental design of positional scanning combinatorial libraries, known to be prone to a combinatorial explosion. AutoPepGEN is available at: https://github.com/sjbarigye/AutoPepGEN. more...

We present a novel Java-based program denominated PeptiDesCalculator for computing peptide descriptors. These descriptors include: redefinitions of known protein parameters to suite the peptide domain, generalization schemes for the global descriptions of peptide characteristics, as well as empirical descriptors based on experimental evidence on peptide stability and interaction propensity. The PeptiDesCalculator software provides a user-friendly Graphical User Interface (GUI) and is parallelized to maximize the use of computational resources available in current work stations. The PeptiDesCalculator indices are employed in modeling 8 peptide bioactivity endpoints demonstrating satisfactory behavior. Moreover, we compare the performance of a support vector machine (SVM) classifier built using 15 PeptiDesCalculator indices with that of a recently reported deep neural network (DNN) antimicrobial activity classifier, demonstrating comparable test set performance notwithstanding the remarkably lower degree of freedom for the former. This software will facilitate the development of in silico models for the prediction of peptide properties., (© 2020 Wiley Periodicals LLC.) more...

Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, was selected to develop the computational model. Dragon software was used to calculate the molecular descriptors and WEKA software to obtain the classification tree. The best model shows accuracy greater than 93.4% for the training set; the tree was also validated using a 10-fold cross-validation procedure and through a test set, achieving accuracy values over 90.5% and 92.2%, correspondingly. The values of sensitivity and specificity were around 90% in all series; also the false alarm rate values were under 10.5% for all sets. In addition, a simulated ligand-based virtual screening for several compounds recently reported as promising anti-chagasic agents was carried out, yielding good agreement between predictions and experimental results. Finally, the present work constitutes an example of how this rational computer-based method can help reduce the cost and increase the rate in which novel compounds are developed against Chagas disease. more...

The aryl hydrocarbon receptor (AhR) is a chemical sensor upregulating the transcription of responsive genes associated with endocrine homeostasis, oxidative balance and diverse metabolic, immunological and inflammatory processes, which have raised the pharmacological interest on its modulation. Herein, a novel set of 32 unsymmetrical triarylmethane (TAM) class of structures has been synthesized, characterized and their AhR transcriptional activity evaluated using a cell-based assay. Eight of the assayed TAM compounds (14, 15, 18, 19, 21, 22, 25, 28) exhibited AhR agonism but none of them showed antagonist effects. TAMs bearing benzotrifluoride, naphthol or heteroaromatic (indole, quinoline or thiophene) rings seem to be prone to AhR activation unlike phenyl substituted or benzotriazole derivatives. A molecular docking analysis with the AhR ligand binding domain (LBD) showed similarities in the binding mode and in the interactions of the most potent TAM identified 4-(pyridin-2-yl (thiophen-2-yl)methyl)phenol (22) compared to the endogenous AhR agonist 5,11-dihydroindolo[3,2-b]carbazole-12-carbaldehyde (FICZ). Finally, in silico predictions of physicochemical and biopharmaceutical properties for the most potent agonistic compounds were performed and these exhibited acceptable druglikeness and good ADME profiles. To our knowledge, this is the first study assessing the AhR modulatory effects of unsymmetrical TAM class of compounds., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Masson SAS. All rights reserved.) more...

In this report, we introduce a set of aggregation operators (AOs) to calculate global and local (group and atom type) molecular descriptors (MDs) as a generalization of the classical approach of molecular encoding using the sum of the atomic (or fragment) contributions. These AOs are implemented in a new and free software denominated MD-LOVIs ( http://tomocomd.com/md-lovis ), which allows for the calculation of MDs from atomic weights vector and LOVIs (local vertex invariants). This software was developed in Java programming language and employed the Chemical Development Kit (CDK) library for handling chemical structures and the calculation of atomic weights. An analysis of the complexities of the algorithms presented herein demonstrates that these aspects were efficiently implemented. The calculation speed experiments show that the MD-LOVIs software has satisfactory behavior when compared to software such as Padel, CDKDescriptor, DRAGON and Bluecal software. Shannon's entropy (SE)-based variability studies demonstrate that MD-LOVIs yields indices with greater information content when compared to those of popular academic and commercial software. A principal component analysis reveals that our approach captures chemical information orthogonal to that codified by the DRAGON, Padel and Mold2 software, as a result of the several generalizations in MD-LOVIs not used in other programs. Lastly, three QSARs were built using multiple linear regression with genetic algorithms, and the statistical parameters of these models demonstrate that the MD-LOVIs indices obtained with AOs yield better performance than those obtained when the summation operator is used exclusively. Moreover, it is also revealed that the MD-LOVIs indices yield models with comparable to superior performance when compared to other QSAR methodologies reported in the literature, despite their simplicity. The studies performed herein collectively demonstrated that MD-LOVIs software generates indices as simple as possible, but not simpler and that use of AOs enhances the diversity of the chemical information codified, which consequently improves the performance of traditional MDs. more...

In the present report we evaluate the possible utility of the Generative Adversarial Networks (GANs) in mapping the chemical structural space for molecular property profiles, with the goal of subsequently yielding synthetic (artificial) samples for ligand-based molecular modeling. Two case studies are considered: BACE-1 (β-Secretase 1) and DENV (Dengue Virus) inhibitory activities, with the former focused on data populating and the latter on data balancing tasks. We train GANs using subsamples extracted from datasets for each bioactivity endpoint, and apply the trained networks in generating synthetic examples from the respective bioactivity chemical spaces. Original and synthetic samples are pooled together and employed to build BACE-1 and DENV inhibitory activity classifiers and their performance evaluated over tenfold external validation sets. In both case studies, the obtained classifiers demonstrate satisfactory predictivity with the former yielding accuracy (ACC) and Mathew's correlation coefficient (MCC) values of 0.80 and 0.59, while the latter produces balanced accuracy(BACC) and MCC values of 0.81 and 0.70, respectively. Moreover, the statistics of these classifiers are compared with those of other models in the literature demonstrating comparable to better performance. These results suggest that GANs may be useful in mapping the chemical space for molecular property profiles of interest, and thus allow for the extraction of synthetic examples for computational modeling., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.) more...

The aryl hydrocarbon receptor (AhR) plays a key role in the regulation of gene expression in metabolic machinery and detoxification systems. In the recent years, this receptor has attracted interest as a therapeutic target for immunological, oncogenic and inflammatory conditions. In the present report, in silico and in vitro approaches were combined to study the activation of the AhR. To this end, a large database of chemical compounds with known AhR agonistic activity was employed to build 5 classifiers based on the Adaboost (AdB), Gradient Boosting (GB), Random Forest (RF), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms, respectively. The built classifiers were examined, following a 10-fold external validation procedure, demonstrating adequate robustness and predictivity. These models were integrated into a majority vote based ensemble, subsequently used to screen an in-house library of compounds from which 40 compounds were selected for prospective in vitro experimental validation. The general correspondence between the ensemble predictions and the in vitro results suggests that the constructed ensemble may be useful in predicting the AhR agonistic activity, both in a toxicological and pharmacological context. A preliminary structure-activity analysis of the evaluated compounds revealed that all structures bearing a benzothiazole moiety induced AhR expression while diverse activity profiles were exhibited by phenolic derivatives., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.) more...

Over the past few decades, virtual high-throughput screening (vHTS) and molecular dynamics simulations have become effective and widely used tools in the initial stages of drug discovery efforts. These methods allow a great number of druglike molecules to be screened quickly and inexpensively. Unfortunately, however, the accuracies of both these methods rely on the quality of the underlying molecular mechanics force fields (FFs), which are often poor. This major weakness originates from the reliance of FFs on a finite list of specific parameters, called atom types, which have low transferability between molecules. In particular, the torsional energy barriers of druglike molecules are notoriously difficult to predict. Continuing our endeavor to understand factors affecting the torsional energy barriers of small molecules and quantify them, we showed that descriptors calculated using the extended-Hückel method could be used to rapidly assign accurate torsion parameters for conjugated molecules. This method, called H-TEQ 4.5, was developed using a set of 684 conjugated molecules. It was subsequently validated on a test set of 200 diverse molecules and produced an average root-mean-square error (rmse) of 1.01 kcal·mol -1 , with respect to the reference quantum mechanic torsional profiles. For comparison, GAFF2, MMFF94, and MAB produced average rmse's of 3.49, 1.50, and 1.77 kcal·mol -1 , respectively. H-TEQ 4.5 is also computationally inexpensive, running just under 0.25 ms for a biphenyl molecule on a home computer, allowing it to be used for vHTS of large libraries of compounds. Overall, H-TEQ 4.5 solved the problems associated with the transferability of torsion parameters for conjugated molecules. This method was incorporated into the Molecular Operating Environment and will be available for a wide variety of applications. more...

Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic targets, a better understanding of BC molecular processes is required. Here we focused on elucidating the molecular hallmarks of BC heterogeneity and the oncogenic mutations involved in precision medicine that remains poorly defined. To fill this gap, we established an OncoOmics strategy that consists of analyzing genomic alterations, signaling pathways, protein-protein interactome network, protein expression, dependency maps in cell lines and patient-derived xenografts in 230 previously prioritized genes to reveal essential genes in breast cancer. As results, the OncoOmics BC essential genes were rationally filtered to 140. mRNA up-regulation was the most prevalent genomic alteration. The most altered signaling pathways were associated with basal-like and Her2-enriched molecular subtypes. RAC1, AKT1, CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, BCL2, CTNNB1, EGFR, CDK2, GRB2, MED1 and GATA3 were essential genes in at least three OncoOmics approaches. Drugs with the highest amount of clinical trials in phases 3 and 4 were paclitaxel, docetaxel, trastuzumab, tamoxifen and doxorubicin. Lastly, we collected ~3,500 somatic and germline oncogenic variants associated with 50 essential genes, which in turn had therapeutic connectivity with 73 drugs. In conclusion, the OncoOmics strategy reveals essential genes capable of accelerating the development of targeted therapies for precision oncology. more...

The aryl hydrocarbon receptor (AhR) plays an important role in several biological processes such as reproduction, immunity and homoeostasis. However, little is known on the chemical-structural and physicochemical features that influence the activity of AhR antagonistic modulators. In the present report, in vitro AhR antagonistic activity evaluations, based on a chemical-activated luciferase gene expression (AhR-CALUX) bioassay, and an extensive literature review were performed with the aim of constructing a structurally diverse database of contaminants and potentially toxic chemicals. Subsequently, QSAR models based on Linear Discriminant Analysis and Logistic Regression, as well as two toxicophoric hypotheses were proposed to model the AhR antagonistic activity of the built dataset. The QSAR models were rigorously validated yielding satisfactory performance for all classification parameters. Likewise, the toxicophoric hypotheses were validated using a diverse set of 350 decoys, demonstrating adequate robustness and predictive power. Chemical interpretations of both the QSAR and toxicophoric models suggested that hydrophobic constraints, the presence of aromatic rings and electron-acceptor moieties are critical for the AhR antagonism. Therefore, it is hoped that the deductions obtained in the present study will contribute to elucidate further on the structural and physicochemical factors influencing the AhR antagonistic activity of chemical compounds. more...

Biaryl molecules are ubiquitous pharmacophores found in natural products and pharmaceuticals. In spite of this, existing molecular mechanics force fields are unable to accurately reproduce their torsional energy profiles, except for a few well-parametrized cases. This effectively limits the ability of structure-based drug design methods to correctly identify hits involving biaryls with confidence (e.g., during virtual screening, employing docking and/or molecular dynamics simulations). Continuing in our endeavor to quantify organic chemistry principles, we showed that the torsional energy profile of biaryl compounds could be computed on-the-fly based on the electron richness/deficiency of the aromatic rings. This method, called H-TEQ 4.0, was developed using a set of 131 biaryls. It was subsequently validated on a separate set of 100 diverse biaryls, including multisubstituted, bicyclic and tricyclic druglike molecules, and produced an average root-mean-square error (RMSE) of 0.95 kcal·mol -1 . For comparison, GAFF2 produced an RMSE of 3.88 kcal·mol -1 , owing to problems associated with the transferability of torsion parameters. The success of H-TEQ 4.0 provided further evidence that force fields could transition to become atom-type independent, providing that the correct chemical principles are used. Overall, this method solved the problem of transferability of biaryl torsion parameters, while simultaneously improving the overall accuracy of the force field. more...

Applications of computational methods to predict binding affinities for protein/drug complexes are routinely used in structure-based drug discovery. Applications of these methods often rely on empirical force fields (FFs) and their associated parameter sets and atom types. However, it is widely accepted that FFs cannot accurately cover the entire chemical space of drug-like molecules, due to the restrictive cost of parametrization and the poor transferability of existing parameters. To address these limitations, initiatives have been carried out to develop more transferable methods, in order to allow for more rigorous descriptions of any drug-like molecule. We have previously reported H-TEQ, a method which does not rely on atom types and incorporates well established chemical principles to assign parameters to organic molecules. The previous implementation of H-TEQ (a torsional barrier prediction method) only covered saturated and lone pair containing molecules; here, we report our efforts to incorporate conjugated systems into our model. The next step was the evaluation of the introduction of unsaturations. The developed model (H-TEQ3.0) has been validated on a wide variety of molecules containing heteroaromatic groups, alkyls, and fused ring systems. Our method performs on par with one of the most commonly used FFs (GAFF2), without relying on atom types or any prior parametrization. more...

Imbalanced datasets, comprising of more inactive compounds relative to the active ones, are a common challenge in ligand-based model building workflows for drug discovery. This is particularly true for neglected tropical diseases since efforts to identify therapeutics for these diseases are often limited. In this report, we analyze the performance of several undersampling strategies in modeling the Dengue Virus 2 (DENV2) inhibitory activity, as well as the anti-flaviviral activities for the West Nile (WNV) and Zika (ZIKV) viruses. To this end, we build datasets comprising of 1218 (159 actives and 1059 inactives), 1044 (132 actives and 912 inactives) and 302 (75 actives and 227 inactives) molecules with known DENV2, WNV and ZIKV inhibitory activity profiles, respectively. We develop ensemble classifiers for these endpoints and compare the performance of the different undersampling algorithms on external sets. It is observed that data pruning algorithms yield superior performance relative to data selection algorithms. The best overall performance is provided by the one-sided selection algorithm with test set balanced accuracy (BACC) values of 0.84, 0.74 and 0.77 for the DENV2, WNV and ZIKV inhibitory activities, respectively. For the model building, we use the recently proposed GT-STAF information indices, and compare the predictivity of 3 molecular fragmentation approaches: connected subgraphs, substructure and alogp atom types, which are observed to show comparable performance. On the other hand, a combination of indices based on these fragmentation strategies enhances the predictivity of the built ensembles. The built models could be useful for screening new molecules with possible DENV, WNV and ZIKV inhibitory activities. ADMET modelers are encouraged to adopt undersampling algorithms in their workflows when dealing with imbalanced datasets. more...

A lot of research initiatives in the last decades have been focused on the search of new strategies to treat depression. However, despite the availability of various antidepressants, current treatment is still far from ideal. Unwanted side effects, modest response rates and the slow onset of action are the main shortcomings. As a strategy to improve symptomatic relief and response rates, the dual modulation of the serotonin transporter and the histamine H 3 receptor by a single chemical entity has been proposed in the literature. Accordingly, this work aims to elucidate key structural features responsible for the dual inhibitory activity of the hexahydro-pyrrolo-isoquinoline derivatives. For this purpose, two approaches were employed, four-dimensional quantitative structure-activity relationship (4D-QSAR) and molecular docking. The 4D-QSAR models for both receptors allowed the identification of the pharmacophore groups critical for the modelled biological activity, whereas the binding mode of this class of compounds to the human serotonin transporter was assessed by molecular docking. The findings can be applicable to design new antidepressants., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.) more...

Consensus strategy was proved to be highly efficient in the recognition of gene-disease association. Therefore, the main objective of this study was to apply theoretical approaches to explore genes and communities directly involved in breast cancer (BC) pathogenesis. We evaluated the consensus between 8 prioritization strategies for the early recognition of pathogenic genes. A communality analysis in the protein-protein interaction (PPi) network of previously selected genes was enriched with gene ontology, metabolic pathways, as well as oncogenomics validation with the OncoPPi and DRIVE projects. The consensus genes were rationally filtered to 1842 genes. The communality analysis showed an enrichment of 14 communities specially connected with ERBB, PI3K-AKT, mTOR, FOXO, p53, HIF-1, VEGF, MAPK and prolactin signaling pathways. Genes with highest ranking were TP53, ESR1, BRCA2, BRCA1 and ERBB2. Genes with highest connectivity degree were TP53, AKT1, SRC, CREBBP and EP300. The connectivity degree allowed to establish a significant correlation between the OncoPPi network and our BC integrated network conformed by 51 genes and 62 PPi. In addition, CCND1, RAD51, CDC42, YAP1 and RPA1 were functional genes with significant sensitivity score in BC cell lines. In conclusion, the consensus strategy identifies both well-known pathogenic genes and prioritized genes that need to be further explored. more...

Aim: Metronidazole is the most widely used drug in trichomoniasis therapy. However, the emergence of metronidazole-resistant Trichomonas vaginalis isolates calls for the search for new drugs to counter the pathogenicity of these parasites., Results: Classification models for predicting the antitrichomonas activity of molecules were built. These models were employed to screen antiprotozoal drugs, from which 20 were classified as active. The in vitro experiments showed moderate to high activity for 19 of the molecules at 10 μg/ml, while 3 compounds yielded higher activity than the reference at 1 μg/ml. The 11 most active chemicals were evaluated in vivo using Naval Medical Research Institute (NMRI) mice., Conclusion: Benznidazole showed similar results as metronidazole, and can thus be considered as a potential candidate in antitrichomonas therapy. more...

Despite recent advances in Computer Aided Drug Discovery and High Throughput Screening, the attrition rates of drug candidates continue to be high, underscoring the inherent complexity of the drug discovery paradigm. Indeed, a compromise between several objectives is often required to obtain successful clinical drugs. The present manuscript details a multi-objective workflow that integrates the 4D-QSAR and molecular docking methods in the simultaneous modeling of the Rho Kinase inhibitory activity and acute toxicity of Benzamide derivatives. To this end, the pIC 50 /pLD 50 ratio is considered as the response variable, permitting the concurrent modeling of both properties and representing a shift from classical step-by-step evaluations. The 4D-QSAR strategy is used to generate the Grid Cell Occupancy Descriptors (GCODs), and Stochastic Gradient Boosting (SGB) and Partial Least Squares (PLS) methods as the model fitting techniques. While the statistical parameters for the PLS model do not meet established criteria for acceptability, the SGB model yields satisfactory performance, with correlation coefficients r 2 =0.95 and r 2 pred=0.65 for the training and test set, respectively. Posteriorly, the structural interpretation of the most relevant GCODs according to the SGB model is performed, allowing for the proposal of 139 novel benzamide derivatives, which are then screened using the same model. Of these 9 compounds were predicted to possess pIC 50 /pLD 50 ratio values higher than those for the employed dataset. Finally, in order to corroborate the results obtained with the SGB model, a docking simulation was formed to evaluate the binding affinity of the proposed molecules to the ROCK2 active site and 3 chemical structures (i. e. p6, p14 and p131) showed higher binding affinity than the most active compound in the training set, while the rest generally demonstrated comparable behavior. It may therefore be concluded that the consensus models that intertwine the 4D-QSAR and molecular docking methods contribute to more reliable virtual screening and compound optimization experiments. Additionally, the use of multi-objective modeling schemes permits the simultaneous evaluation of different chemical and biological profiles, which should contribute to the control a priori of causative factors for the high attrition rates in later drug discovery phases., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.) more...

We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility. more...

We previously implemented a well-known qualitative chemical principle into an accurate quantitative model computing relative potential energies of conformers. According to this principle, hyperconjugation strength correlates with electronegativity of donors and acceptors. While this earlier version of our model applies to σ bonds, lone pairs, disregarded in this earlier version, also have a major impact on the conformational preferences of molecules. Among the well-established principles used by organic chemists to rationalize some organic chemical behaviors are the anomeric effect, the alpha effect, basicity, and nucleophilicity. These effects are directly related to the presence of lone pairs. We report herein our effort to incorporate lone pairs into our model to extend its applicability domain to any saturated small molecules. The developed model H-TEQ 2 has been validated on a wide variety of molecules from polyaromatic molecules to carbohydrates and molecules with high heteroatoms/carbon ratios. Interestingly, this method, in contrast to common force field-based methods, does not rely on atom types and is virtually applicable to any organic molecules. more...

Several descriptors from atom weighted vectors are used in the prediction of aquatic toxicity of set of organic compounds of 392 benzene derivatives to the protozoo ciliate Tetrahymena pyriformis (log(IGC50) -1 ). These descriptors are calculated using the MD-LOVIs software and various Aggregation Operators are examined with the aim comparing their performances in predicting aquatic toxicity. Variability analysis is used to quantify the information content of these molecular descriptors by means of an information theory-based algorithm. Multiple Linear Regression with Genetic Algorithms is used to obtain models of the structure-toxicity relationships; the best model shows values of Q 2 =0.830 and R 2 =0.837 using six variables. Our models compare favorably with other previously published models that use the same data set. The obtained results suggest that these descriptors provide an effective alternative for determining aquatic toxicity of benzene derivatives., (Copyright © 2017 Elsevier B.V. All rights reserved.) more...

The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to determine correctly the mechanism of action of a compound, quantitative structure-activity relationship (QSAR) methods, which have proved their interest in toxicity prediction, can be used. In this work, several QSAR models for the prediction of MOA of 221 phenols to the ciliated protozoan Tetrahymena pyriformis, using Chemistry Development Kit descriptors, are reported. Four machine learning techniques (ML), k-nearest neighbours, support vector machine, classification trees, and artificial neural networks, have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. They showed global accuracy values between 95.9% and 97.7% and area under Receiver Operator Curve values between 0.978 and 0.998; additionally, false alarm rate values were below 8.2% for training set. In order to validate our models, cross-validation (10-folds-out) and external test-set were performed with good behaviour in all cases. These models, obtained with ML techniques, were compared with others previously reported by other researchers, and the improvement was significant. more...

Background: In previous reports, Marrero-Ponce et al. proposed algebraic formalisms for characterizing topological (2D) and chiral (2.5D) molecular features through atom- and bond-based ToMoCoMD-CARDD (acronym for Topological Molecular Computational Design-Computer Aided Rational Drug Design) molecular descriptors. These MDs codify molecular information based on the bilinear, quadratic and linear algebraic forms and the graph-theoretical electronic-density and edge-adjacency matrices in order to consider atom- and bond-based relations, respectively. These MDs have been successfully applied in the screening of chemical compounds of different therapeutic applications ranging from antimalarials, antibacterials, tyrosinase inhibitors and so on. To compute these MDs, a computational program with the same name was initially developed. However, this in house software barely offered the functionalities required in contemporary molecular modeling tasks, in addition to the inherent limitations that made its usability impractical. Therefore, the present manuscript introduces the QuBiLS-MAS (acronym for Quadratic, Bilinear and N-Linear mapS based on graph-theoretic electronic-density Matrices and Atomic weightingS) software designed to compute topological (0-2.5D) molecular descriptors based on bilinear, quadratic and linear algebraic forms for atom- and bond-based relations., Results: The QuBiLS-MAS module was designed as standalone software, in which extensions and generalizations of the former ToMoCoMD-CARDD 2D-algebraic indices are implemented, considering the following aspects: (a) two new matrix normalization approaches based on double-stochastic and mutual probability formalisms; (b) topological constraints (cut-offs) to take into account particular inter-atomic relations; (c) six additional atomic properties to be used as weighting schemes in the calculation of the molecular vectors; (d) four new local-fragments to consider molecular regions of interest; (e) number of lone-pair electrons in chemical structure defined by diagonal coefficients in matrix representations; and (f) several aggregation operators (invariants) applied over atom/bond-level descriptors in order to compute global indices. This software permits the parallel computation of the indices, contains a batch processing module and data curation functionalities. This program was developed in Java v1.7 using the Chemistry Development Kit library (version 1.4.19). The QuBiLS-MAS software consists of two components: a desktop interface (GUI) and an API library allowing for the easy integration of the latter in chemoinformatics applications. The relevance of the novel extensions and generalizations implemented in this software is demonstrated through three studies. Firstly, a comparative Shannon's entropy based variability study for the proposed QuBiLS-MAS and the DRAGON indices demonstrates superior performance for the former. A principal component analysis reveals that the QuBiLS-MAS approach captures chemical information orthogonal to that codified by the DRAGON descriptors. Lastly, a QSAR study for the binding affinity to the corticosteroid-binding globulin using Cramer's steroid dataset is carried out., Conclusions: From these analyses, it is revealed that the QuBiLS-MAS approach for atom-pair relations yields similar-to-superior performance with regard to other QSAR methodologies reported in the literature. Therefore, the QuBiLS-MAS approach constitutes a useful tool for the diversity analysis of chemical compound datasets and high-throughput screening of structure-activity data. more...

Background: To know the ability of a compound to penetrate the blood-brain barrier (BBB) is a challenging task; despite the numerous efforts realized to predict/measure BBB passage, they still have several drawbacks., Method: The prediction of the permeability through the BBB is carried out using classification trees. A large data set of 497 compounds (recently published) is selected to develop the tree model., Results: The best model shows an accuracy higher than 87.6% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 86.1% and 87.9%, correspondingly. We give a brief explanation, in structural terms, of how our model describes the passage of molecules through the BBB. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results., Conclusion: Finally, we can say that, the present model would be a valuable tool in the early stages of drug discovery process of neuropharmaceuticals., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.) more...

Biomagnification of organic pollutants in food webs has been usually associated to hydrophobicity and other molecular descriptors. However, direct information on atoms and substituent positions in a molecular scaffold that most affect this biological property is not straightforward using traditional QSPR techniques. This work reports the QSPR modeling of biomagnification factors (logBMF) of a series of aromatic organochlorine compounds using three MIA-QSPR (multivariate image analysis applied to QSPR) approaches. The MIA-QSPR model based on augmented molecular images (described with atoms represented as circles with sizes proportional to the respective van der Waals radii and having colors numerically proportional to the Pauling's electronegativity) encoded better the logBMF data. The average results for the main statistical parameters used to attest the model's predictability were r 2 =0.85, q 2 =0.72 and r 2 test =0.85. In addition, chemical insights on substituents and respective positions at the biphenyl rings A and B, and dibenzo-p-dioxin and dibenzofuran motifs are given to aid the design of more ecofriendly derivatives., (Copyright © 2016 Elsevier Inc. All rights reserved.) more...

Background: There are a great number of tools that can be used in QSAR/QSPR studies; they are implemented in several programs that are reviewed in this report. The usefulness of new tools can be proved through comparison, with previously published approaches. In order to perform the comparison, the most usual is the use of several benchmark datasets such as DRAGON and Sutherland's datasets., Methods: Here, an exploratory study of Atomic Weighted Vectors (AWVs), a new tool useful for drug discovery using different datasets, is presented. In order to evaluate the performance of the new tool, several statistics and QSAR/QSPR experiments are performed. Variability analyses are used to quantify the information content of the AWVs obtained from the tool, by means of an information theory-based algorithm., Results: Principal components analysis is used to analyze the orthogonality of these descriptors, for which the new MDs from AWVs provide different information from those codified by DRAGON descriptors (0-2D). The QSAR models are obtained for every Sutherland's dataset, according to the original division into training/test sets, by means of the multiple linear regression with genetic algorithm (MLR-GA). These models have been validated and compared favorably to several previously published approaches, using the same benchmark datasets., Conclusion: The obtained results show that this tool should be a useful strategy for the QSAR/QSPR studies, despite its simplicity., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.) more...

Novel N-tuple topological/geometric cutoffs to consider specific inter-atomic relations in the QuBiLS-MIDAS framework are introduced in this manuscript. These molecular cutoffs permit the taking into account of relations between more than two atoms by using (dis-)similarity multi-metrics and the concepts related with topological and Euclidean-geometric distances. To this end, the kth two-, three- and four-tuple topological and geometric neighbourhood quotient (NQ) total (or local-fragment) spatial-(dis)similarity matrices are defined, to represent 3D information corresponding to the relations between two, three and four atoms of the molecular structures that satisfy certain cutoff criteria. First, an analysis of a diverse chemical space for the most common values of topological/Euclidean-geometric distances, bond/dihedral angles, triangle/quadrilateral perimeters, triangle area and volume was performed in order to determine the intervals to take into account in the cutoff procedures. A variability analysis based on Shannon's entropy reveals that better distribution patterns are attained with the descriptors based on the cutoffs proposed (QuBiLS-MIDAS NQ-MDs) with regard to the results obtained when all inter-atomic relations are considered (QuBiLS-MIDAS KA-MDs - 'Keep All'). A principal component analysis shows that the novel molecular cutoffs codify chemical information captured by the respective QuBiLS-MIDAS KA-MDs, as well as information not captured by the latter. Lastly, a QSAR study to obtain deeper knowledge of the contribution of the proposed methods was carried out, using four molecular datasets (steroids (STER), angiotensin converting enzyme (ACE), thermolysin inhibitors (THER) and thrombin inhibitors (THR)) widely used as benchmarks in the evaluation of several methodologies. One to four variable QSAR models based on multiple linear regression were developed for each compound dataset following the original division into training and test sets. The results obtained reveal that the novel cutoff procedures yield superior performances relative to those of the QuBiLS-MIDAS KA-MDs in the prediction of the biological activities considered. From the results achieved, it can be suggested that the proposed N-tuple topological/geometric cutoffs constitute a relevant criteria for generating MDs codifying particular atomic relations, ultimately useful in enhancing the modelling capacity of the QuBiLS-MIDAS 3D-MDs. more...

A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH•) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a satisfactory performance for the training ( R 2 = 0.713 ) and test set ( Q ext 2 = 0.654 ) , respectively. To gain greater insight on the relevance of the MD contained in the MLP model, sensitivity and principal component analyses were performed. Moreover, structural and mechanistic interpretation was carried out to comprehend the relationship of the variables in the model with the modeled property. The constructed MLP model was employed to predict the radical scavenging ability for a group of coumarin-type compounds. Finally, in order to validate the model's predictions, an in vitro assay for one of the compounds (4-hydroxycoumarin) was performed, showing a satisfactory proximity between the experimental and predicted pIC50 values. more...

The bioconcentration factor (BCF) is an important parameter used to estimate the propensity of chemicals to accumulate in aquatic organisms from the ambient environment. While simple regressions for estimating the BCF of chemical compounds from water solubility or the n-octanol/water partition coefficient have been proposed in the literature, these models do not always yield good correlations and more descriptive variables are required for better modeling of BCF data for a given series of organic pollutants, such as some herbicides. Thus, the logBCF values for a set of carbonyl herbicides comprising amide, urea, carbamate and thiocarbamate groups were quantitatively modeled using multivariate image analysis (MIA) descriptors, derived from colored image representations for chemical structures. The logBCF model was calibrated and vigorously validated (r(2) = 0.79, q(2) = 0.70 and rtest(2) = 0.81), providing a comprehensive three-parameter linear equation after variable selection (logBCF = 5.682 - 0.00233 × X9774 - 0.00070 × X813 - 0.00273 × X5144); the variables represent pixel coordinates in the multivariate image. Finally, chemical interpretation of the obtained models in terms of the structural characteristics responsible for the enhanced or reduced logBCF values was performed, providing key leads in the prospective development of more eco-friendly synthetic herbicides., (Copyright © 2016 Elsevier Ltd. All rights reserved.) more...

This report examines the interpretation of the Graph Derivative Indices (GDIs) from three different perspectives (i.e., in structural, steric and electronic terms). It is found that the individual vertex frequencies may be expressed in terms of the geometrical and electronic reactivity of the atoms and bonds, respectively. On the other hand, it is demonstrated that the GDIs are sensitive to progressive structural modifications in terms of: size, ramifications, electronic richness, conjugation effects and molecular symmetry. Moreover, it is observed that the GDIs quantify the interaction capacity among molecules and codify information on the activation entropy. A structure property relationship study reveals that there exists a direct correspondence between the individual frequencies of atoms and Hückel's Free Valence, as well as between the atomic GDIs and the chemical shift in NMR, which collectively validates the theory that these indices codify steric and electronic information of the atoms in a molecule. Taking in consideration the regularity and coherence found in experiments performed with the GDIs, it is possible to say that GDIs possess plausible interpretation in structural and physicochemical terms. more...

Background: Recently, novel 3D alignment-free molecular descriptors (also known as QuBiLS-MIDAS) based on two-linear, three-linear and four-linear algebraic forms have been introduced. These descriptors codify chemical information for relations between two, three and four atoms by using several (dis-)similarity metrics and multi-metrics. Several studies aimed at assessing the quality of these novel descriptors have been performed. However, a deeper analysis of their performance is necessary. Therefore, in the present manuscript an assessment and statistical validation of the performance of these novel descriptors in QSAR studies is performed., Results: To this end, eight molecular datasets (angiotensin converting enzyme, acetylcholinesterase inhibitors, benzodiazepine receptor, cyclooxygenase-2 inhibitors, dihydrofolate reductase inhibitors, glycogen phosphorylase b, thermolysin inhibitors, thrombin inhibitors) widely used as benchmarks in the evaluation of several procedures are utilized. Three to nine variable QSAR models based on Multiple Linear Regression are built for each chemical dataset according to the original division into training/test sets. Comparisons with respect to leave-one-out cross-validation correlation coefficients[Formula: see text] reveal that the models based on QuBiLS-MIDAS indices possess superior predictive ability in 7 of the 8 datasets analyzed, outperforming methodologies based on similar or more complex techniques such as: Partial Least Square, Neural Networks, Support Vector Machine and others. On the other hand, superior external correlation coefficients[Formula: see text] are attained in 6 of the 8 test sets considered, confirming the good predictive power of the obtained models. For the [Formula: see text] values non-parametric statistic tests were performed, which demonstrated that the models based on QuBiLS-MIDAS indices have the best global performance and yield significantly better predictions in 11 of the 12 QSAR procedures used in the comparison. Lastly, a study concerning to the performance of the indices according to several conformer generation methods was performed. This demonstrated that the quality of predictions of the QSAR models based on QuBiLS-MIDAS indices depend on 3D structure generation method considered, although in this preliminary study the results achieved do not present significant statistical differences among them., Conclusions: As conclusions it can be stated that the QuBiLS-MIDAS indices are suitable for extracting structural information of the molecules and thus, constitute a promissory alternative to build models that contribute to the prediction of pharmacokinetic, pharmacodynamics and toxicological properties on novel compounds.Graphical abstractComparative graphical representation of the performance of the novel QuBiLS-MIDAS 3D-MDs with respect to other methodologies in QSAR modeling of eight chemical datasets. more...

In the present study, a generalized approach for molecular structure characterization is introduced, based on the relation frequency matrix (F) representation of the molecular graph and the subsequent calculation of the corresponding discrete derivative (finite difference) over a pair of elements (atoms). In earlier publications (22- 24), an unique event, named connected subgraphs, (based on the Kier-Hall's subgraphs) was systematically employed for the computation of the matrix F. The present report is a generalization of this notion, in which eleven additional events are introduced, classified in three categories, namely, topological (terminal paths, vertex path incidence, quantum subgraphs, walks of length k, Sach's subgraphs), fingerprints (MACCs, E-state and substructure fingerprints) and atomic contributions (Ghose and Crippen atom-types for hydrophobicity and refractivity) for F generation. The events are intended to capture diverse information by the generation or search of different kinds of substructures from the graph representation of a molecule. The discrete derivative over duplex atom relations are calculated for each event, and the resulting derivatives, local vertex invariants (LOVIs) are finally obtained. These LOVIs are subsequently employed as the basis for the calculation of global and local indices over groups of atoms (heteroatoms, halogens, methyl carbons, etc.), by using norms, means, statistics and classical algorithms as aggregator (fusion) operators. These indices were implemented in our house software DIVATI (Derivative Type Indices, a new module of TOMOCOMDCARDD system). DIVATI provides a friendly and cross-platform graphical user interface, developed in the Java programming language and is freely available at: http: //www.tomocomd.com. Factor analysis shows that the presented events are rather orthogonal and collect diverse information about the chemical structure. Finally, QSPR models were built to describe the logP and logK of 34 furylethylenes derivatives using the eleven events. Generally, the equations obtained according to these events showed high correlations, with the Sach's sub-graphs and Multiplicity events showing the best behavior in the description of logK (Q2 LOO value of 99.06%) and logP (Q2 LOO value of 98.1 %), respectively. These results show that these new eventbased indices constitute a powerful approach for chemoinformatics studies. more...

Background: Many QSAR studies have been developed to predict acute toxicity over several biomarkers like Pimephales promelas, Daphnia magna and Tetrahymena pyriformis. Regardless of the progress made in this field there are still some gaps to be resolved such as the prediction of aquatic toxicity over the protozoan T. pyriformis still lack a QSAR study focused in accomplish the OECD principles., Methods: Atom-based quadratic indices are used to obtain quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity. Our models agree with the principles required by the OECD for QSAR models to regulatory purposes. The database employed consists of 392 substituted benzenes with toxicity values measured in T. pyriformis (defined endpoint), was divided using cluster analysis in two series (training and test sets)., Results: We obtain (with an unambiguous algorithm) two good multiple linear regression models for non-stochastic (R2=0.807 and s=0.334) and stochastic (R2=0.817 and s=0.321), quadratic indices. The models were internally validated using leave-one-out, bootstrapping as well as Y-scrambling experiments. We also perform an external validation using the test set, achieving values of R2 pred values of 0.754 and 0.760, showing that our models have appropriate measures of goodness- of-fit, robustness and predictivity. Moreover, we define a domain of applicability for our best models., Conclusion: The achieved results demonstrated that, the atom-based quadratic indices could provide an attractive alternative to the experiments currently used for determining toxicity, which are costly and time-consuming. more...

For a decade, the multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) approach has been successfully used in the modeling of several chemical and biological properties of chemical compounds. However, the key pitfall of this method has been its exclusive applicability to congeneric datasets due to the prerequisite of aligning the chemical images with respect to the basic molecular scaffold. The present report aims to explore the use of the 2D-discrete Fourier transform (2D-DFT) as a means of opening way to the modeling, for the first time, of structurally diverse noncongruent chemical images. The usability of the 2D-DFT in QSAR modeling of noncongruent chemical compounds is assessed using a structurally diverse dataset of 100 compounds, with reported inhibitory activity against MCF-7 human breast cancer cell line. An analysis of the statistical parameters of the built regression models validates their robustness and high predictive power. Additionally, a comparison of the results obtained with the 2D-DFT MIA-QSAR approach with those of the DRAGON molecular descriptors is performed, revealing superior performance for the former. This result represents a milestone in the MIA-QSAR context, as it opens way for the possibility of screening for new molecular entities with the desired chemical or therapeutic utility., (© 2015 Wiley Periodicals, Inc.) more...

Motivation: The large variety of antimicrobial peptide (AMP) databases developed to date are characterized by a substantial overlap of data and similarity of sequences. Our goals are to analyze the levels of redundancy for all available AMP databases and use this information to build a new non-redundant sequence database. For this purpose, a new software tool is introduced., Results: A comparative study of 25 AMP databases reveals the overlap and diversity among them and the internal diversity within each database. The overlap analysis shows that only one database (Peptaibol) contains exclusive data, not present in any other, whereas all sequences in the LAMP_Patent database are included in CAMP_Patent. However, the majority of databases have their own set of unique sequences, as well as some overlap with other databases. The complete set of non-duplicate sequences comprises 16 990 cases, which is almost half of the total number of reported peptides. On the other hand, the diversity analysis identifies the most and least diverse databases and proves that all databases exhibit some level of redundancy. Finally, we present a new parallel-free software, named Dover Analyzer, developed to compute the overlap and diversity between any number of databases and compile a set of non-redundant sequences. These results are useful for selecting or building a suitable representative set of AMPs, according to specific needs., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.) more...

In the present study, we introduce novel 3D protein descriptors based on the bilinear algebraic form in the ℝ(n) space on the coulombic matrix. For the calculation of these descriptors, macromolecular vectors belonging to ℝ(n) space, whose components represent certain amino acid side-chain properties, were used as weighting schemes. Generalization approaches for the calculation of inter-amino acidic residue spatial distances based on Minkowski metrics are proposed. The simple- and double-stochastic schemes were defined as approaches to normalize the coulombic matrix. The local-fragment indices for both amino acid-types and amino acid-groups are presented in order to permit characterizing fragments of interest in proteins. On the other hand, with the objective of taking into account specific interactions among amino acids in global or local indices, geometric and topological cut-offs are defined. To assess the utility of global and local indices a classification model for the prediction of the major four protein structural classes, was built with the Linear Discriminant Analysis (LDA) technique. The developed LDA-model correctly classifies the 92.6% and 92.7% of the proteins on the training and test sets, respectively. The obtained model showed high values of the generalized square correlation coefficient (GC(2)) on both the training and test series. The statistical parameters derived from the internal and external validation procedures demonstrate the robustness, stability and the high predictive power of the proposed model. The performance of the LDA-model demonstrates the capability of the proposed indices not only to codify relevant biochemical information related to the structural classes of proteins, but also to yield suitable interpretability. It is anticipated that the current method will benefit the prediction of other protein attributes or functions., (Copyright © 2015 Elsevier Ltd. All rights reserved.) more...

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., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.) more...

The features and theoretical background of a new and free computational program for chemometric analysis denominated IMMAN (acronym for Information theory-based CheMoMetrics ANalysis) are presented. This is multi-platform software developed in the Java programming language, designed with a remarkably user-friendly graphical interface for the computation of a collection of information-theoretic functions adapted for rank-based unsupervised and supervised feature selection tasks. A total of 20 feature selection parameters are presented, with the unsupervised and supervised frameworks represented by 10 approaches in each case. Several information-theoretic parameters traditionally used as molecular descriptors (MDs) are adapted for use as unsupervised rank-based feature selection methods. On the other hand, a generalization scheme for the previously defined differential Shannon's entropy is discussed, as well as the introduction of Jeffreys information measure for supervised feature selection. Moreover, well-known information-theoretic feature selection parameters, such as information gain, gain ratio, and symmetrical uncertainty are incorporated to the IMMAN software ( http://mobiosd-hub.com/imman-soft/ ), following an equal-interval discretization approach. IMMAN offers data pre-processing functionalities, such as missing values processing, dataset partitioning, and browsing. Moreover, single parameter or ensemble (multi-criteria) ranking options are provided. Consequently, this software is suitable for tasks like dimensionality reduction, feature ranking, as well as comparative diversity analysis of data matrices. Simple examples of applications performed with this program are presented. A comparative study between IMMAN and WEKA feature selection tools using the Arcene dataset was performed, demonstrating similar behavior. In addition, it is revealed that the use of IMMAN unsupervised feature selection methods improves the performance of both IMMAN and WEKA supervised algorithms. Graphic representation for Shannon's distribution of MD calculating software. more...

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., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.) more...

A series of quinolon-4(1H)-imines have been recently discovered as antimalarials, targeting both the exoerythrocytic and erythrocytic stages of the parasite's development stages, which correspond to the phase of clinical symptoms. Endowed with chemical and metabolic stability, the quinolon-4(1H)- imines are thus presented as promissory dual-stage antimalarials. Three versions of multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) methods, namely traditional MIA-QSAR, augmented MIA-QSAR (aug-MIA-QSAR) and color-encoded aug-MIA-QSAR (aug- MIA-QSARcolor), were applied to model the antimalarial activities in this series of compounds. The multiple linear regression models indicated that the aug-MIA-QSAR method is more predictive and reliable than the others (R(2) = 0.8079, R(2)cv = 0.6647 and R(2)pred = 0.9691) for this series of compounds. The selected aug- MIA-QSAR descriptors were used for pattern recognition using discriminant analysis by partial least squares (PLS-DA), in order to separate compounds with low, moderate and high bioactivities. more...

Theoretical models for exploring the antischistosomal activity of a dataset of 18 synthetic neolignans are built using the multivariate image analysis applied to structure-activity relationships (MIA-SAR) approach. The obtained models were validated using the accuracy (Acc) in leave-one-out cross-validation, external validation and Y-randomization procedures, yielding correct classification superior to 80%, 70% and 60%, respectively. Additionally, a comparison was made of the models obtained from binary (black and white) and coloured images; the colours (pixel values) were selected to correspond to chemical properties. It was observed that the models obtained from coloured images with pixel values corresponding to electronegativity (known as the aug-MIA-SAR(colour) approach) generally yielded superior statistical parameters compared with those obtained from binary images (MIA-SAR) and randomly coloured images (atoms are coloured according to their type) with atomic sizes corresponding to Van der Waals radius (aug-MIA-SAR), respectively. Mechanistic interpretation of the influence of different substituents on the antischistosomal activity revealed that methoxy substituents in the R1 (or R2) and R5 positions of the neolignan scaffold are indispensable for the antischistosomal activity. The obtained results provide knowledge of the possible structural modifications to yield novel neolignan compounds with antischistosomal activity. more...