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

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

Understanding the Effect of Deposition Technique on the Structure-Property Relationship of Polyaniline Thin Films Applied in Potentiometric pH Sensor.

The comprehension of potentiometric pH sensors with polymeric thin films for new and advanced applications is a constant technological need. The present study aimed to explore the relationship between the sensitivity and correlation coefficient of potentiometric pH sensors and the structure-property relationship of polyaniline thin films. The effect of the deposition method on the sample's properties was evaluated. Galvanostatically electrodeposited and spin-coated polyaniline thin films were used as the sensing stage. Samples were electrodeposited with a current density of 0.5 mA/cm2 for 300, 600, and 1200 s and were spin coated for 60 s with an angular velocity of 500, 1000, and 2000 rpm. The electrodeposited set of films presented higher average sensitivity, 73.4 ± 1.3 mV/pH, compared to the spin-coated set, 59.2 ± 2.5 mV/pH. The electrodeposited films presented higher sensitivity due to their morphology, characterized by a larger roughness and thickness compared to spin-coated ones, favoring the potentiometric response. Also, their oxidation state, evaluated with cyclic voltammetry and UV-VIS spectroscopy, corroborates their sensing performance. The understanding of the structure-property relationship of the polymeric films affecting the pH detection is discussed based on the characteristics of the deposition method used.

Sulfonated Block Copolymers: Synthesis, Chemical Modification, Self-Assembly Morphologies, and Recent Applications.

Scientific research based on the self-assembly behavior of block copolymers (BCs) comprising charged-neutral segments has emerged as a novel strategy mainly looking for the optimization of efficiency in the generation and storage of electrical energy. The sulfonation reaction re- presents one of the most commonly employed methodologies by scientific investigations to reach the desired amphiphilic character, leading to enough ion concentration to modify and control the entire self-assembly behavior of the BCs. Recently, several works have studied and exploited these changes, inducing improvement on the mechanical properties, ionic conduction capabilities, colloidal solubility, interface activity, and stabilization of dispersed particles, among others. This review aims to present a description of recent works focused on obtaining amphiphilic block copolymers, specifically those that were synthesized by a living/controlled polymerization method and that have introduced the amphiphilic character by the sulfonation of one of the segments. Additionally, relevant works that have evidenced morphological and/or structural changes regarding the pristine BC as a result of the chemical modification are discussed. Finally, several emerging practical applications are analyzed to highlight the main drawbacks and challenges that should be addressed to overcome the development and understanding of these complex systems.

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches.

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43-0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

Chemical Multiverse: An Expanded View of Chemical Space.

Technological advances and practical applications of the chemical space concept in drug discovery, natural product research, and other research areas have attracted the scientific community's attention. The large- and ultra-large chemical spaces are associated with the significant increase in the number of compounds that can potentially be made and exist and the increasing number of experimental and calculated descriptors, that are emerging that encode the molecular structure and/or property aspects of the molecules. Due to the importance and continued evolution of compound libraries, herein, we discuss definitions proposed in the literature for chemical space and emphasize the convenience, discussed in the literature to use complementary descriptors to obtain a comprehensive view of the chemical space of compound data sets. In this regard, we introduce the term chemical multiverse to refer to the comprehensive analysis of compound data sets through several chemical spaces, each defined by a different set of chemical representations. The chemical multiverse is contrasted with a related idea: consensus chemical space.

Optimization of physicochemical properties is a strategy to improve drug-likeness associated with activity: Novel active and selective compounds against Trypanosoma cruzi.

Trypanosoma cruzi is the causing agent of Chagas disease, a parasitic infection without efficient treatment for chronic patients. Despite the efforts, no new drugs have been approved for this disease in the last 60 years. Molecular modifications based on a natural product led to the development of a series of compounds (LINS03 series) with promising antitrypanosomal activity, however previous chemometric analysis revealed a significant impact of excessive lipophilicity and low aqueous solubility on potency of amine and amide derivatives. Therefore, this work reports different modifications in the core structure to achieve adequate balance of the physicochemical properties along with biological activity. A set of 34 analogues were designed considering predicted properties related to lipophilicity/hydrosolubility and synthesized to assess their activity and selective toxicity towards the parasite. Results showed that this strategy contributed to improve the drug-likeness of the series while considerable impacts on potency were observed. The rational analysis of the obtained data led to the identification of seven active piperazine amides (28-34, IC50 8.7 to 35.3 µM against intracellular amastigotes), devoid of significant cytotoxicity to mammalian cells. The addition of water-solubilizing groups and privileged substructures such as piperazines improved the physicochemical properties and overall drug-likeness of these compounds, increased potency and maintained selectivity towards the parasite. The obtained results brought important structure-activity relationship (SAR) data and new lead structures for further modifications were identified to achieve improved antitrypanosoma compounds.

Tubulin Inhibitors: A Chemoinformatic Analysis Using Cell-Based Data.

Inhibiting the tubulin-microtubules (Tub-Mts) system is a classic and rational approach for treating different types of cancers. A large amount of data on inhibitors in the clinic supports Tub-Mts as a validated target. However, most of the inhibitors reported thus far have been developed around common chemical scaffolds covering a narrow region of the chemical space with limited innovation. This manuscript aims to discuss the first activity landscape and scaffold content analysis of an assembled and curated cell-based database of 851 Tub-Mts inhibitors with reported activity against five cancer cell lines and the Tub-Mts system. The structure-bioactivity relationships of the Tub-Mts system inhibitors were further explored using constellations plots. This recently developed methodology enables the rapid but quantitative assessment of analog series enriched with active compounds. The constellations plots identified promising analog series with high average biological activity that could be the starting points of new and more potent Tub-Mts inhibitors.

ADME prediction with KNIME: A retrospective contribution to the second "Solubility Challenge".

Computational models for predicting aqueous solubility from the molecular structure represent a promising strategy from the perspective of drug design and discovery. Since the first "Solubility Challenge", these initiatives have marked the state-of-art of the modelling algorithms used to predict drug solubility. In this regard, the quality of the input experimental data and its influence on model performance has been frequently discussed. In our previous study, we developed a computational model for aqueous solubility based on recursive random forest approaches. The aim of the current commentary is to analyse the performance of this already trained predictive model on the molecules of the second "Solubility Challenge". Even when our training set has inconsistencies related to the pH, solid form and temperature conditions of the solubility measurements, the model was able to predict the two sets from the second "Solubility Challenge" with statistics comparable to those of the top ranked models. Finally, we provided a KNIME automated workflow to predict aqueous solubility of new drug candidates, during the early stages of drug discovery and development, for ensuring the applicability and reproducibility of our model.

New Poly(imide)s Bearing Alkyl Side-Chains: A Study on the Impact of Size and Shape of Lateral Groups on Thermal, Mechanical, and Gas Transport Properties.

A set of five new aromatic poly(imide)s (PIs) incorporating pendant acyclic alkyl moieties were synthesized. The difference among them was the length and bulkiness of the pendant group, which comprises of linear alkyl chains from three to six carbon atoms, and a tert-butyl moiety. The effect of the side group length on the physical, thermal, mechanical, and gas transport properties was analyzed. All PIs exhibited low to moderate molecular weights (Mn ranged between 27.930-58.970 Da, and Mw ranged between 41.760-81.310 Da), good solubility in aprotic polar solvents, except for PI-t-4, which had a tert-butyl moiety and was soluble even in chloroform. This behaviour was probably due to the most significant bulkiness of the side group that increased the interchain distance, which was corroborated by the X-ray technique (PI-t-4 showed two d-spacing values: 5.1 and 14.3 Å). Pure gas permeabilities for several gases were reported (PI-3 (Barrer): He(52); H2(46); O2(5.4); N2(1.2); CH4(1.1); CO2(23); PI-t-4 (Barrer): He(139); H2(136); O2(16.7); N2(3.3); CH4(2.3); CO2(75); PI-5 (Barrer): He(44); H2(42); O2(5.9); N2(1.4); CH4(1.2); CO2(27); PI-6 (Barrer): He(45); H2(43); O2(6.7); N2(1.7); CH4(1.7); CO2(32)). Consistent higher volume in the side group was shown to yield the highest gas permeability. All poly(imide)s exhibited high thermal stability with 10% weight loss degradation temperature between 448-468 °C and glass transition temperature between 240-270 °C. The values associated to the tensile strength (45-87 MPa), elongation at break (3.2-11.98%), and tensile modulus (1.43-2.19 GPa) were those expected for aromatic poly(imide)s.

ADME prediction with KNIME: In silico aqueous solubility consensus model based on supervised recursive random forest approaches.

In-silico prediction of aqueous solubility plays an important role during the drug discovery and development processes. For many years, the limited performance of in-silico solubility models has been attributed to the lack of high-quality solubility data for pharmaceutical molecules. However, some studies suggest that the poor accuracy of solubility prediction is not related to the quality of the experimental data and that more precise methodologies (algorithms and/or set of descriptors) are required for predicting aqueous solubility for pharmaceutical molecules. In this study a large and diverse database was generated with aqueous solubility values collected from two public sources; two new recursive machine-learning approaches were developed for data cleaning and variable selection, and a consensus model based on regression and classification algorithms was created. The modeling protocol, which includes the curation of chemical and experimental data, was implemented in KNIME, with the aim of obtaining an automated workflow for the prediction of new databases. Finally, we compared several methods or models available in the literature with our consensus model, showing results comparable or even outperforming previous published models.

Finding Constellations in Chemical Space Through Core Analysis.

Herein we introduce the constellation plots as a general approach that merges different and complementary molecular representations to enhance the information contained in a visual representation and analysis of chemical space. The method is based on a combination of a sub-structure based representation and classification of compounds with a "classical" coordinate-based representation of chemical space. A distinctive outcome of the method is that organizing the compounds in analog series leads to the formation of groups of molecules, aka "constellations" in chemical space. The novel approach is general and can be used to rapidly identify, for instance, insightful and "bright" Structure-Activity Relationships (StARs) in chemical space that are easy to interpret. This kind of analysis is expected to be especially useful for lead identification in large datasets of unannotated molecules, such as those obtained through high-throughput screening. We demonstrate the application of the method using two datasets of focused inhibitors designed against DNMTs and AKT1.

Role of Trifluoromethyl Substitution in Design of Antimalarial Quinolones: a Comprehensive Review.

Malaria represents a significant health issue, and novel effective drugs are needed to address parasite resistance that has emerged to the current drug arsenal. The most popular antimalarial drugs are focused on the 7-chloro-4-aminoquinoline [e.g., chloroquine (CQ), amodiaquine (AQ), isoquine (IQ), and tebuquine (TBQ)], artemisinin, and atovaquone systems. Recently, endochin has been used as a platform to design a variety of novel potent and safe antimalarial agents named endochin-like quinolones (ELQs). Also, antimalarial quinolones have been constructed from other quinolones drugs such as ICI-56780 and floxacrine. Trifluoromethyl substitution has provided a significant increase in the antimalarial response of many of the designed ELQs against Plasmodium-resistant strains and for in vivo models. In particular, attachment of a substituted trifluoromethoxy (or trifluoromethyl in some cases) biaryl side chain at 2-, 3-, 4-, or 6-position of the quinolone core has shown to be crucially important to generate selective and potent novel ELQs. Furthermore, 6-chloro and 7-methoxy moieties on the quinolone core have been identified as essential pharmacophores when the trifluoromethoxy biaryl side chain is placed at 2- or 3-position of the quinolone core. Methyl or ethyl ester attached at 3-position is essential when the trifluoromethoxy aryl side chain is attached at 6- or 7-position of the quinolone core. Some promising ELQs are currently under clinical trials, representing an excellent platform for the design of new potent, selective, effective, and safe antimalarial drugs against emergent resistance malarial models.

A general approach for retrosynthetic molecular core analysis.

Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure-activity relationships analysis. In this context, analog series-based scaffolds (ASBS) are synthetically relevant core structures that represent individual series of analogs. As an extension to ASBS, we herein introduce the development of a general conceptual framework that considers all putative cores of molecules in a compound data set, thus softening the often applied "single molecule-single scaffold" correspondence. A putative core is here defined as any substructure of a molecule complying with two basic rules: (a) the size of the core is a significant proportion of the whole molecule size and (b) the substructure can be reached from the original molecule through a succession of retrosynthesis rules. Thereafter, a bipartite network consisting of molecules and cores can be constructed for a database of chemical structures. Compounds linked to the same cores are considered analogs. We present case studies illustrating the potential of the general framework. The applications range from inter- and intra-core diversity analysis of compound data sets, structure-property relationships, and identification of analog series and ASBS. The molecule-core network herein presented is a general methodology with multiple applications in scaffold analysis. New statistical methods are envisioned that will be able to draw quantitative conclusions from these data. The code to use the method presented in this work is freely available as an additional file. Follow-up applications include analog searching and core structure-property relationships analyses.

Computational modeling of human oral bioavailability: what will be next?

INTRODUCTION: The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

The conformation-independent QSPR approach for predicting the oxidation rate constant of water micropollutants.

In advanced water treatment processes, the degradation efficiency of contaminants depends on the reactivity of the hydroxyl radical toward a target micropollutant. The present study predicts the hydroxyl radical rate constant in water (k OH) for 118 emerging micropollutants, by means of quantitative structure-property relationships (QSPR). The conformation-independent QSPR approach is employed, together with a large number of 15,251 molecular descriptors derived with the PaDEL, Epi Suite, and Mold2 freewares. The best multivariable linear regression (MLR) models are found with the replacement method variable subset selection technique. The proposed five-descriptor model has the following statistics for the training set: [Formula: see text], RMS train = 0.21, while for the test set is [Formula: see text], RMS test = 0.11. This QSPR serves as a rational guide for predicting oxidation processes of micropollutants.

Predicting the bioconcentration factor through a conformation-independent QSPR study.

The ANTARES dataset is a large collection of known and verified experimental bioconcentration factor data, involving 851 highly heterogeneous compounds from which 159 are pesticides. The BCF ANTARES data were used to derive a conformation-independent QSPR model. A large set of 27,017 molecular descriptors was explored, with the main intention of capturing the most relevant structural characteristics affecting the studied property. The structural descriptors were derived with different freeware tools, such as PaDEL, Epi Suite, CORAL, Mold2, RECON, and QuBiLs-MAS, and so it was interesting to find out the way that the different descriptor tools complemented each other in order to improve the statistical quality of the established QSPR. The best multivariable linear regression models were found with the Replacement Method variable sub-set selection technique. The proposed QSPR model improves previous reported models of the bioconcentration factor in the present dataset.

Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data.

Soil sorption of insecticides employed in agriculture is an important parameter to probe the environmental fate of organic chemicals. Therefore, methods for the prediction of soil sorption of new agrochemical candidates, as well as for the rationalization of the molecular characteristics responsible for a given sorption profile, are extremely beneficial for the environment. A quantitative structure-property relationship method based on chemical structure images as molecular descriptors provided a reliable model for the soil sorption prediction of 24 widely used organophosphorus insecticides. By means of contour maps obtained from the partial least squares regression coefficients and the variable importance in projection scores, key molecular moieties were targeted for possible structural modification, in order to obtain novel and more environmentally friendly insecticide candidates. The image-based descriptors applied encode molecular arrangement, atoms connectivity, groups size, and polarity; consequently, the findings in this work cannot be achieved by a simple relationship with hydrophobicity, usually described by the octanol-water partition coefficient.

Discovery of Novel Antischistosomal Agents by Molecular Modeling Approaches.

Schistosomiasis, a chronic neglected tropical disease caused by Schistosoma worms, is reported in nearly 80 countries. Although the disease affects approximately 260 million people, the treatment relies exclusively on praziquantel, a drug discovered in the mid-1970s that lacks efficacy against the larval stages of the parasite. In addition, the dependence on a single treatment has raised concerns about drug resistance, and reduced susceptibility has already been found in laboratory and field isolates. Therefore, novel therapies for schistosomiasis are needed, and several approaches have been used to that end. One of these strategies, molecular modeling, has been increasingly integrated with experimental techniques, resulting in the discovery of novel antischistosomal agents.

Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds.

We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds by means of Quantitative Structure-Property Relationships (QSPR). A conformation-independent representation of the chemical structure is established. The 17,538 molecular descriptors derived with PaDEL and EPI Suite softwares are simultaneously analyzed through linear regressions obtained with the Replacement Method variable subset selection technique. The best predictive three-descriptors QSPR is developed on a reduced training set of 93 chemicals, having an acceptable predictive capability on 550 test set compounds. We also establish a model with a single optimal descriptor derived from CORAL freeware. The present approach compares fairly well with a previously reported one that uses Dragon descriptors.

Conformational and Molecular Structures of α,ß-Unsaturated Acrylonitrile Derivatives: Photophysical Properties and Their Frontier Orbitals.

We report single crystal X-ray diffraction (hereafter, SCXRD) analyses of derivatives featuring the electron-donor N-ethylcarbazole or the (4-diphenylamino)phenyl moieties associated with a -CN group attached to a double bond. The compounds are (2Z)-3-(4-(diphenylamino)-phenyl)-2-(pyridin-3-yl)prop-2-enenitrile (I), (2Z)-3-(4-(diphenylamino)phenyl)-2-(pyridin-4-yl)-prop-2-enenitrile (II) and (2Z)-3-(9-ethyl-9H-carbazol-3-yl)-2-(pyridin-2-yl)enenitrile (III). SCXRD analyses reveal that I and III crystallize in the monoclinic space groups P2/c with Z' = 2 and C2/c with Z' = 1, respectively. Compound II crystallized in the orthorhombic space group Pbcn with Z' = 1. The molecular packing analysis was conducted to examine the pyridine core effect, depending on the ortho, meta- and para-positions of the nitrogen atom, with respect to the optical properties and number of independent molecules (Z'). It is found that the double bond bearing a diphenylamino moiety introduced properties to exhibit a strong π-π-interaction in the solid state. The compounds were examined to evaluate the effects of solvent polarity, the role of the molecular structure, and the molecular interactions on their self-assembly behaviors. Compound I crystallized with a cell with two conformers, anti and syn, due to interaction with solvent. DFT calculations indicated the anti and syn structures of I are energetically stable (less than 1 eV). Also electrochemical and photophysical properties of the compounds were investigated, as well as the determination of optimization calculations in gas and different solvent (chloroform, cyclohexane, methanol, ethanol, tetrahydrofuran, dichloromethane and dimethyl sulfoxide) in the Gaussian09 program. The effect of solvent by PCM method was also investigated. The frontier HOMO and LUMO energies and gap energies are reported.