Systematic Development of Vanadium Catalysts for Sustainable Epoxidation of Small Alkenes and Allylic Alcohols.

The catalytic epoxidation of small alkenes and allylic alcohols includes a wide range of valuable chemical applications, with many works describing vanadium complexes as suitable catalysts towards sustainable process chemistry. But, given the complexity of these mechanisms, it is not always easy to sort out efficient examples for streamlining sustainable processes and tuning product optimization. In this review, we provide an update on major works of tunable vanadium-catalyzed epoxidations, with a focus on sustainable optimization routes. After presenting the current mechanistic view on vanadium catalysts for small alkenes and allylic alcohols' epoxidation, we argue the key challenges in green process development by highlighting the value of updated kinetic and mechanistic studies, along with essential computational studies.

MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions.

Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents.

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure-activity relationship (mt-QSAR) models based on the Box-Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.

Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis.

BACKGROUND: Preeclampsia is a multifactorial disease with unknown pathogenesis. Even when recent studies explored this disease using several bioinformatics tools, the main objective was not directed to pathogenesis. Additionally, consensus prioritization was proved to be highly efficient in the recognition of genes-disease association. However, not information is available about the consensus ability to early recognize genes directly involved in pathogenesis. Therefore our aim in this study is to apply several theoretical approaches to explore preeclampsia; specifically those genes directly involved in the pathogenesis. METHODS: We firstly evaluated the consensus between 12 prioritization strategies to early recognize pathogenic genes related to preeclampsia. A communality analysis in the protein-protein interaction network of previously selected genes was done including further enrichment analysis. The enrichment analysis includes metabolic pathways as well as gene ontology. Microarray data was also collected and used in order to confirm our results or as a strategy to weight the previously enriched pathways. RESULTS: The consensus prioritized gene list was rationally filtered to 476 genes using several criteria. The communality analysis showed an enrichment of communities connected with VEGF-signaling pathway. This pathway is also enriched considering the microarray data. Our result point to VEGF, FLT1 and KDR as relevant pathogenic genes, as well as those connected with NO metabolism. CONCLUSION: Our results revealed that consensus strategy improve the detection and initial enrichment of pathogenic genes, at least in preeclampsia condition. Moreover the combination of the first percent of the prioritized genes with protein-protein interaction network followed by communality analysis reduces the gene space. This approach actually identifies well known genes related with pathogenesis. However, genes like HSP90, PAK2, CD247 and others included in the first 1% of the prioritized list need to be further explored in preeclampsia pathogenesis through experimental approaches.

Advanced In Silico Approaches for Drug Discovery: Mining Information from Multiple Biological and Chemical Data Through mtk- QSBER and pt-QSPR Strategies.

The last decade has been seeing an increase of public-private partnerships in drug discovery, mostly driven by factors such as the decline in productivity, the high costs, time, and resources needed, along with the requirements of regulatory agencies. In this context, traditional computer-aided drug discovery techniques have been playing an important role, enabling the identification of new molecular entities at early stages. However, recent advances in chemoinformatics and systems pharmacology, alongside with a growing body of high quality, publicly accessible medicinal chemistry data, have led to the emergence of novel in silico approaches. These novel approaches are able to integrate a vast amount of multiple chemical and biological data into a single modeling equation. The present review analyzes two main kinds of such cutting-edge in silico approaches. In the first subsection, we discuss the updates on multitasking models for quantitative structure-biological effect relationships (mtk- QSBER), whose applications have been significantly increasing in the past years. In the second subsection, we provide detailed information regarding a novel approach that combines perturbation theory with quantitative structure-property relationships modeling tools (pt- QSPR). Finally, and most importantly, we show that the joint use of mtk-QSBER and pt- QSPR modeling tools are apt to guide drug discovery through its multiple stages: from in vitro assays to preclinical studies and clinical trials.

Multitasking models for quantitative structure-biological effect relationships: current status and future perspectives to speed up drug discovery.

INTRODUCTION: Drug discovery is the process of designing new candidate medications for the treatment of diseases. Over many years, drugs have been identified serendipitously. Nowadays, chemoinformatics has emerged as a great ally, helping to rationalize drug discovery. In this sense, quantitative structure-activity relationships (QSAR) models have become complementary tools, permitting the efficient virtual screening for a diverse number of pharmacological profiles. Despite the applications of current QSAR models in the search for new drug candidates, many aspects remain unresolved. To date, classical QSAR models are able to predict only one type of biological effect (activity, toxicity, etc.) against only one type of generic target. AREAS COVERED: The present review discusses innovative and evolved QSAR models, which are focused on multitasking quantitative structure-biological effect relationships (mtk-QSBER). Such models can integrate multiple kinds of chemical and biological data, allowing the simultaneous prediction of pharmacological activities, toxicities and/or other safety profiles. EXPERT OPINION: The authors strongly believe, given the potential of mtk-QSBER models to simultaneously predict the dissimilar biological effects of chemicals, that they have much value as in silico tools for drug discovery. Indeed, these models can speed up the search for efficacious drugs in a number of areas, including fragment-based drug discovery and drug repurposing.

Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents.

BACKGROUND: Gram-positive cocci are increasingly antibiotic-resistant bacteria responsible for causing serious diseases. Chemoinformatics can help to rationalize the discovery of more potent and safer antibacterial drugs. We have developed a chemoinformatic model for simultaneous prediction of anti-cocci activities, and profiles involving absorption, distribution, metabolism, elimination and toxicity (ADMET). RESULTS: A dataset containing 48,874 cases from many different chemicals assayed under dissimilar experimental conditions was created. The best model displayed accuracies around 93% in both training and prediction (test) sets. Quantitative contributions of several fragments to the biological effects were calculated and analyzed. Multiple biological effects of the investigational drug JNJ-Q2 were correctly predicted. CONCLUSION: Our chemoinformatic model can be used as powerful tool for virtual screening of promising anti-cocci agents.

Matrix trace operators: from spectral moments of molecular graphs and complex networks to perturbations in synthetic reactions, micelle nanoparticles, and drug ADME processes.

The study of quantitative structure-property relationships (QSPR) is important to study complex networks of chemical reactions in drug synthesis or metabolism or drug-target interaction networks. A difficult but possible goal is the prediction of drug absorption, distribution, metabolism, and excretion (ADME) process with a single QSPR model. For this QSPR modelers need to use flexible structural parameters useful for the description of many different systems at different structural scales (multi-scale parameters). Also they need to use powerful analytical methods able to link in a single multi-scale hypothesis structural parameters of different target systems (multi-target modeling) with different experimental properties of these systems (multi-output models). In this sense, the QSPR study of complex bio-molecular systems may benefit substantially from the combined application of spectral moments of graph representations of complex systems with perturbation theory methods. On one hand, spectral moments are almost universal parameters that can be calculated to many different matrices used to represent the structure of the states of different systems. On the other hand, perturbation methods can be used to add "small" variation terms to parameters of a known state of a given system in order to approach to a solution of another state of the same or similar system with unknown properties. Here we present one state-of-art review about the different applications of spectral moments to describe complex bio-molecular systems. Next, we give some general ideas and formulate plausible linear models for a general-purpose perturbation theory of QSPR problems of complex systems. Last, we develop three new QSPR-Perturbation theory models based on spectral moments for three different problems with multiple in-out boundary conditions that are relevant to biomolecular sciences. The three models developed correctly classify more than pairs 115,600; 48,000; 134,900 cases of the effects of in-out perturbations in intra-molecular carbolithiations, drug ADME process, or self-aggregation of micelle nanoparticles of drugs or surfactants. The Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp) of these models were >90% in all cases. The first model predicts variations in the yield or enantiomeric excess due to structural variations or changes in the solvent, temperature, temperature of addition, or time of reaction. The second model predicts changes in >18 parameters of biological effects for >3000 assays of ADME properties and/or interactions between 31,723 drugs and 100 targets (metabolizing enzymes, drug transporters, or organisms). The third model predicts perturbations due to changes in temperature, solvent, salt concentration, and/or structure of anions or cations in the self-aggregation of micelle nanoparticles of drugs and surfactants.

Review of current chemoinformatic tools for modeling important aspects of CYPs-mediated drug metabolism. Integrating metabolism data with other biological profiles to enhance drug discovery.

The study of the metabolism of xenobiotics by the human body is an essential stage in the complex and expensive process of drug discovery, being one of the main causes of disapproval and/or withdrawal of drugs. Regarding this, enzymes known as cytochromes P450 (CYPs) play a very decisive role in the biotransformation of many chemicals. For this reason, the use of chemoinformatics to predict and /or analyze from different points of view CYPs-mediated drug metabolism, can help to reduce time and financial resources. This work is focused on the most remarkable advances in the last 5 years of the chemoinformatics tools towards the virtual analysis of CYPsmediated drug metabolism. First, a brief section is dedicated to the applicability of chemoinformatics in different areas associated with drug metabolism. Then, both the models for prediction of CYPs substrates and those allowing the assessment of sites of metabolism (SOM) are discussed. At the same time, the principal limitations of the current chemoinformatic tools are pointed out. Finally, and taking into account that metabolism is an essential step in the whole process of designing any drug, we introduce here as a case of study, the first multitasking model for quantitative-structure biological effect relationships (mtk-QSBER). The purpose of this model is to integrate different types of biological profiles such as ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles and antistaphylococci activities. The mtk-QSBER model was created by employing a heterogeneous dataset of more than 66000 cases tested in 6510 different experimental conditions. The model displayed a total accuracy higher than 94%. To the best of our knowledge, this is the first attempt to complement metabolism assays with other relevant biological data in order to speed up the discovery of efficacious antistaphylococci agents.

Chemoinformatics profiling of ionic liquids--uncovering structure-cytotoxicity relationships with network-like similarity graphs.

Ionic liquids (ILs) constitute one of the hottest areas in chemistry since they have become increasingly popular as reaction and extraction media. Their almost limitless structural possibilities, as opposed to limited structural variations within molecular solvents, make ILs "designer solvents." They also have been widely promoted as "green solvents" although their claimed relative nontoxicity has been frequently questioned. The Thinking in Structure-Activity Relationships (T-SAR) approach has proved to be an efficient method to gather relevant toxicological information of analog series of ILs. However, when data sets significantly grow in size and structural diversity, the use of computational models becomes essential. We provided such a computational solution in a previous work by introducing a reliable, predictive, simple, and chemically interpretable Classification and Regression Tree (CART) classifier enabling the prioritization of ILs with a favorable cytotoxicity profile. Even so, an efficient and exhaustive mining of SAR information goes beyond analog compound series and the applicability domain of quantitative SAR modeling. So, we decided to complement our previous findings based on the use of the CART classifier by applying the network-like similarity graph (NSG) approach to the mining of relevant structure-cytotoxicity relationship (SCR) trends. Finally, the SCR information concurrently gathered by both, quantitative (CART classifier) and qualitative (NSG) approaches was used to design a focused combinatorial library enriched with potentially safe ILs.

QSAR modeling for the antimalarial activity of 1,4-naphthoquinonyl derivatives as potential antimalarial agents.

Malaria has been known as one of the major causes of morbidity and mortality on a large scale in tropical countries until now. In the past decades, many scientific groups have focused their attention on looking for ideal drugs to this disease. So far, this research area is still a hot topic. In the present study, the antimalarial activity of 1, 4- naphthoquinonyl derivatives was modeled by linear and nonlinear statistical methods, that is to say, by forward stepwise multilinear regression (MLR) and radial basis function neural networks (RBFNN). The derived QSAR models have been statistically validated both internally - by means of the Leave One Out (LOO) and Leave Many Out (LMO) crossvalidation, and Y-scrambling techniques, as well as externally (by means of an external test set). The statistical parameters provided by the MLR model were R(2) =0.7876, LOOq(2) =0.7068, RMS =0.3377, R0 2 =0.7876, k =1.0000 for the training set,and R(2) =0.7648, q(2) ext =0.7597, RMS=0.2556, R0 2=0.7598, k=1.0417 for the external test set. The RBFNN model gave the following statistical results, namely: R(2)=0.8338, LOOq(2)=0.5869, RMS=0.2781, R0 2 = 0.8335, k=1.0000 for the training set, and R(2) =0.7586, q(2) ext =0.7189, RMS=0.2788, R0 2=0.7129, k=1.0284 for the external test set. Overall, these results suggest that the QSAR MLR-based model is a simple, reliable, credible and fast tool for the prediction and virtual screening of 1, 4-naphoquinone derivatives with high antimalarial activity. In addition, the energies of the highest occupied molecular orbital were found to have high correlation with the activity.

Prediction of the baseline toxicity of non-polar narcotic chemical mixtures by QSAR approach.

Environmental contaminants are frequently encountered as mixtures, and research on mixture toxicity is a hot topic until now. In the present study, the mixture toxicity of non-polar narcotic chemical was modeled by linear and nonlinear statistical methods, that is to say, by forward stepwise multilinear regression (MLR) and radial basis function neural networks (RBFNNs) from molecular descriptors that are calculated and be defined as composite descriptors according to the fractional concentrations of the mixture components. The statistical parameters provided by the MLR model were R(2)=0.9512, RMS=0.3792, F=1402.214 and LOOq(2)=0.9462 for the training set, and R(2)=0.9453, RMS=0.3458, F=276.671 and q(ext)(2)=0.9450 for the external test set. The RBFNN model gave the following statistical results, namely: R(2)=0.9779, RMS=0.2498, F=3188.202 and LOOq(2)=0.9746 for the training set, and R(2)=0.9763, RMS=0.2358, F=660.631 and q(ext)(2)=0.9745, for the external test set. Overall, these results suggest that the QSAR MLR-based model is a simple, reliable, credible and fast tool for the prediction mixture toxicity of non-polar narcotic chemicals. The RBFNN model gave even improved results. In addition, ε(LUMO+1) (the energy of the second lowest unoccupied molecular orbital) and PPSA (total charge weighted partial positively surface area) were found to have high correlation with the mixture toxicity.

Current drug design of anti-HIV agents through the inhibition of C-C chemokine receptor type 5.

Human immunodeficiency virus (HIV) is the responsible causal agent of acquired immunodeficiency syndrome (AIDS), a condition in humans in which the immune system begins to fail, allowing the entry of opportunistic infections. HIV infection in humans is considered pandemic by the World Health Organization (WHO). HIV needs to use a protein as a co-receptor to enter its target cells. Several chemokine receptors can in principle act as viral co-receptors, but the chemokine (C-C motif) receptor 5 (CCR5) is likely the most physiologically important co-receptor during natural infection. For this reason the development of new CCR5 inhibitors like anti-HIV agents, constitutes a challenge for the scientific community. The present review will focus on the current state of the design of novel anti-HIV drugs, and how the existing computer aided-drug design methodologies, have been effective in the search of new anti-HIV agents. In addition, a QSAR model based on substructural descirptors is presented as a rapid, rational and promising alternative for the discovery of anti-HIV agents through the inhibition of the CCR5.

Current computational approaches towards the rational design of new insecticidal agents.

Pesticides are chemicals with a great impact in the economy of any country. They are employed for the eradication of pests. Insects constitute one of these pests which are extremely difficult to control. With the passage of the time, insects have become resistant to pesticides, causing huge crop losses and diseases in humans. For this reason, there is an increasing need for the design of more potent insecticides. The present review is focused on the current state of the application of computational approaches as essential tools for the design of novel insecticidal agents. Also, a model based on a substructural approach is presented as a rational, efficient and promising alternative for the discovery of new insecticides.

On the electronic structure of cocaine and its metabolites.

This work aims at describing the electronic features of cocaine and how they are modified by the different substituents present in its metabolites. The QTAIM analysis of B3LYP and MP2 electron densities obtained with the 6-311++G** 6d basis set for cocaine and its principal metabolites indicates: (i) its positive charge is shared among the amino hydrogen, those of the methylamino group, and all of the hydrogens attached to the bicycle structure; (ii) the zwitterionic structure of benzoylecgonine can be described as two partial charges of 0.63 au, the negative one shared by the oxygens of the carboxylate group, whereas the positive charge is distributed among all the hydrogens that bear the positive charge in cocaine; (iii) its hydrogen bond is strengthened in the derivatives without benzoyloxy group and is also slightly strengthened as the size of the alkyl ester group at position 2 increases.