Prediction of the Phase Composition Profile of Three-Compound Mixtures in Liquid-Liquid Equilibrium: A Chemoinformatics Approach.

Machine-learning models were developed to predict the composition profile of a three-compound mixture in liquid-liquid equilibrium (LLE), given the global composition at certain temperature and pressure. A chemoinformatics approach was explored, based on the MOLMAP technology to encode molecules and mixtures. The chemical systems involved an ionic liquid (IL) and two organic molecules. Two complementary models have been optimized for the IL-rich and IL-poor phases. The two global optimized models are highly accurate, and were validated with independent test sets, where combinations of molecule1+molecule2+IL are different from those in the training set. These results highlight the MOLMAP encoding scheme, based on atomic properties to train models that learn relationships between features of complex multi-component chemical systems and their profile of phase compositions.

QSPR modeling of selectivity at infinite dilution of ionic liquids.

The intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and laboratory practice. The most promising approach is a straightforward comparison of selectivity at infinite dilution between potential candidates. However, selectivity at infinite dilution values are rarely available for most compounds so a theoretical estimation is highly desired. In this study, we suggest a Quantitative Structure-Property Relationship (QSPR) approach to the modelling of the selectivity at infinite dilution of ionic liquids. Additionally, auxiliary models were developed to overcome the potential bias from big activity coefficient at infinite dilution from the solute. Data from SelinfDB database was used as training and internal validation sets in QSPR model development. External validation was done with the data from literature. The selection of the best models was done using decision functions that aim to diminish bias in prediction of the data points associated with the underrepresented ionic liquids or extreme temperatures. The best models were used for the virtual screening for potential azeotrope breakers of aniline + n-dodecane mixture. The subject of screening was a combinatorial library of ionic liquids, created based on the previously unused combinations of cations and anions from SelinfDB and the test set extractants. Both selectivity at infinite dilution and auxiliary models show good performance in the validation. Our models' predictions were compared to the ones of the COSMO-RS, where applicable, displaying smaller prediction error. The best ionic liquid to extract aniline from n-dodecane was suggested.

Water Solubility Trends in Ionic Liquids: The Quantitative Structure-Property Relationship Model versus Molecular Dynamics.

The knowledge of water solubility in ionic liquids (ILs) is an important property with an impact on the design of many physical and chemical processes, like the purification of organic compounds or the establishment of decontamination procedures. The development of methods to predict or establish solubility trends in ILs is, therefore, extremely relevant, as it may avoid expensive and time-consuming experimental procedures. In this work, we compare results of water solubility in ILs predicted by a quantitative structure-property relationship (QSPR) model with trends found using aggregation studies in molecular dynamics (MD) simulation results. This study was performed for ILs combining the cations 1-butyl-1-methylpyrrolidinium and 1-butyl-1-methylmorpholinium, with the anions bis(pentafluoroethylsulfonyl)imide (BETI-), trifluoromethanesulfonate (TF-), and tetrafluoroborate (BF4-). Both methods indicated that, at 298.15 K, the water solubility in ILs was almost independent of the investigated cations. However, if the IL is composed of a hydrophobic anion, a slight increase in the mixability of the IL with water may be observed if the cation can form H-bonds. The QSPR model indicated that the hydrophobic BETI- anion leads to solubilities (xH2O ∼ 0.33), approximately half of those predicted when the cations are combined with TF- and BF4- anions (xH2O ∼ 0.60). The MD results suggested that this difference is essentially related to the ability of the water molecules to interact with the anion. This interaction involves the formation of networks of molecules, where H2O is completely solvated by anions. These structures make the formation of interactions between water molecules difficult, which are responsible for their segregation from solution and, therefore, to liquid-liquid phase separation. For the investigated ILs, the MD data also suggest that the solubility trends are inversely proportional to the number of "isolated" anions relative to ···AN-H2O-AN-H2O··· networks.

The Solubility of Gases in Ionic Liquids: A Chemoinformatic Predictive and Interpretable Approach.

This work comprises the study of solubilities of gases in ionic liquids (ILs) using a chemoinformatic approach. It is based on the codification, of the atomic inter-component interactions, cation/gas and anion/gas, which are used to obtain a pattern of activation in a Kohonen Neural Network (MOLMAP descriptors). A robust predictive model has been obtained with the Random Forest algorithm and used the maximum proximity as a confidence measure of a given chemical system compared to the training set. The encoding method has been validated with molecular dynamics. This encoding approach is a valuable estimator of attractive/repulsive interactions of a generical chemical system IL+gas. This method has been used as a fast/visual form of identification of the reasons behind the differences observed between the solubility of CO2 and O2 in 1-butyl-3-methylimidazolium hexafluorophosphate (BMIM PF6 ) at identical temperature and pressure (TP) conditions, The effect of variable cation and anion effect has been evaluated.

Examining the evidence of non-monotonic dose-response in Androgen Receptor agonism high-throughput screening assay.

Modern High-Throughput Screening (HTS) techniques allow to determine in vitro bioactivity of tens of thousands of chemicals within a relatively short period of time and tested compounds are usually interpreted as either active or inactive. The interpretation is mostly based on the assumption of monotonic dose-response. This approach ignores potential abnormal dose-response relationships, such as non-monotonic dose-response (NMDR). NMDR presents a serious challenge to toxicologists and pharmacologists, since they undermine the usefulness of such concepts as lowest-observed-adverse-effect level (LOAEL) and no-observed-adverse-effect level (NOAEL). The possible presence of the NMDR in Androgen receptor (AR) agonism was examined for a structurally diverse set of chemicals (~8 300 unique compounds) from Tox21 project library. The source of activity data is Tox21 AR agonism luciferase-based HTS on the MDA-MB-453 cell line. The examination of curve fitting for 35,328 dose-response data entries was based on modified version of existing criteria for determination of NMDR. The bias that arises from compounds' cytotoxicity and interference with firefly luciferase protein was also studied. The examination has shown evidence of NMDR for several compounds, including known AR antagonists (e. g. Cyproterone acetate) and other known endocrine disruptors (e. g. Tranilast). Compounds were divided into 3 groups based on chemical class, known biological activity profile and the shape of dose-response curve. The challenges of using HTS data to determine NMDR and benefits of this analysis are discussed.

QSPR Modeling of Liquid-liquid Equilibria in Two-phase Systems of Water and Ionic Liquid.

The increasing application of new ionic liquids (IL) creates the need of liquid-liquid equilibria data for both miscible and quasi-immiscible systems. In this study, equilibrium concentrations at different temperatures for ionic liquid+water two-phase systems were modeled using a Quantitative-Structure-Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. Models were subjected to the Y-scrambling test for robustness assessment. The best models have also been validated using an external test set that is not part of the ILThermo database. A two-phase equilibrium diagram for one of the external test set IL is presented for better visualization of the results and potential derivation of tie lines.

QSAR modelling of a large imbalanced aryl hydrocarbon activation dataset by rational and random sampling and screening of 80,086 REACH pre-registered and/or registered substances.

The Aryl hydrocarbon receptor (AhR) plays important roles in many normal and pathological physiological processes, including endocrine homeostasis, foetal development, cell cycle regulation, cellular oxidation/antioxidation, immune regulation, metabolism of endogenous and exogenous substances, and carcinogenesis. An experimental data set for human in vitro AhR activation comprising 324,858 substances, of which 1,982 were confirmed actives, was used to test an in-house-developed approach to rationally select Quantitative Structure-Activity Relationship (QSAR) training set substances from an unbalanced data set. In the first iteration, active and inactive substances were selected by random to make QSAR models. Then, more inactive substances were added to the training set in two further iterations based on incorrect or out-of-domain predictions to produce larger models. The resulting 'rational' model, comprising 832 actives and four times as many inactives, i.e. 3,328, was compared to a model with a training set of same size and proportion of inactives chosen entirely by random. Both models underwent robust cross-validation and external validation showing good statistical performance, with the rational model having external validation sensitivity of 85.1% and specificity of 97.1%, compared to the random model with sensitivity 89.1% and specificity 91.3%. Furthermore, we integrated the training sets for both models with the 93 external validation test set actives and 372 randomly selected inactives to make two final models. They also underwent external validations for specificity and cross-validations, which confirmed that good predictivity was maintained. All developed models were applied to predict 80,086 EU REACH substances. The rational and random final models had 63.1% and 56.9% coverage of the REACH set, respectively, and predicted 1,256 and 3,214 substances as actives. The final models as well as predictions for AhR activation for 650,000 substances will be published in the Danish (Q)SAR Database and can, for example, be used for priority setting, in read-across predictions and in weight-of-evidence assessments of chemicals.

Aminoalkoxyfluorenones and aminoalkoxybiphenyls: DNA binding modes.

Many evidences suggest that DNA-drug interaction in the minor groove and the intercalation of drugs into DNA may play critical roles in antiviral, antimicrobial, and antitumor activities. As a continuous effort to develop novel antiviral agents, the series of planar fluorenone (3a-7d) were synthesized and used along with nonplanar biphenyls (11a-d) for the comparative analysis of their interaction with DNA. The chemical structure of new compounds was confirmed by 1H NMR, 13C NMR and mass spectra as well as elemental analysis. DNA affinity of 3a-7d and 11a-d was evaluated by ethidium bromide displacement assay. Affinity constant (lgKa) of 3a-7d was found to be approximately two orders of magnitude higher than constants of corresponding 11a-d. The molecular docking of aminoalkoxybiphenyls (11a-d) into minor grove of five different nucleotide sequences (d(CCIICICCII), d(CGCGTTAACGCG), d(CGCGATATCGCG), d(GGCCAATTGG), d(GGATATATCC)) demonstrated their binding capacity to the specific DNA site. The linear least squares fitting technique was successfully applied to derive an equation describing the relationship between lgKa and SF.

R-based Tool for a Pairwise Structure-activity Relationship Analysis.

The Structure-Activity Relationship analysis is a complex process that can be enhanced by computational techniques. This article describes a simple tool for SAR analysis that has a graphic user interface and a flexible approach towards the input of molecular data. The application allows calculating molecular similarity represented by Tanimoto index & Euclid distance, as well as, determining activity cliffs by means of Structure-Activity Landscape Index. The calculation is performed in a pairwise manner either for the reference compound and other compounds or for all possible pairs in the data set. The results of SAR analysis are visualized using two types of plot. The application capability is demonstrated by the analysis of a set of COX2 inhibitors with respect to Isoxicam. This tool is available online: it includes manual and input file examples.

Virtual screening, synthesis and biological evaluation of DNA intercalating antiviral agents.

This paper describes computer-aided design of new anti-viral agents against Vaccinia virus (VACV) potentially acting as nucleic acid intercalators. Earlier obtained experimental data for DNA intercalation affinities and activities against Vesicular stomatitis virus (VSV) have been used to build, respectively, pharmacophore and QSAR models. These models were used for virtual screening of a database of 245 molecules generated around typical scaffolds of known DNA intercalators. This resulted in 12 hits which then were synthesized and tested for antiviral activity against VaV together with 43 compounds earlier studied against VSV. Two compounds displaying high antiviral activity against VaV and low cytotoxicity were selected for further antiviral activity investigations.

Chemical Space Mapping and Structure-Activity Analysis of the ChEMBL Antiviral Compound Set.

Curation, standardization and data fusion of the antiviral information present in the ChEMBL public database led to the definition of a robust data set, providing an association of antiviral compounds to seven broadly defined antiviral activity classes. Generative topographic mapping (GTM) subjected to evolutionary tuning was then used to produce maps of the antiviral chemical space, providing an optimal separation of compound families associated with the different antiviral classes. The ability to pinpoint the specific spots occupied (responsibility patterns) on a map by various classes of antiviral compounds opened the way for a GTM-supported search for privileged structural motifs, typical for each antiviral class. The privileged locations of antiviral classes were analyzed in order to highlight underlying privileged common structural motifs. Unlike in classical medicinal chemistry, where privileged structures are, almost always, predefined scaffolds, privileged structural motif detection based on GTM responsibility patterns has the decisive advantage of being able to automatically capture the nature ("resolution detail"-scaffold, detailed substructure, pharmacophore pattern, etc.) of the relevant structural motifs. Responsibility patterns were found to represent underlying structural motifs of various natures-from very fuzzy (groups of various "interchangeable" similar scaffolds), to the classical scenario in medicinal chemistry (underlying motif actually being the scaffold), to very precisely defined motifs (specifically substituted scaffolds).

Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility.

A model developed to predict aqueous solubility at different temperatures has been proposed based on quantitative structure-property relationships (QSPR) methodology. The prediction consists of two steps. The first one predicts the value of k parameter in the linear equation lgSw=kT+c, where Sw is the value of solubility and T is the value of temperature. The second step uses Random Forest technique to create high-efficiency QSPR model. The performance of the model is assessed using cross-validation and external test set prediction. Predictive capacity of developed model is compared with COSMO-RS approximation, which has quantum chemical and thermodynamic foundations. The comparison shows slightly better prediction ability for the QSPR model presented in this publication. © 2016 Wiley Periodicals, Inc.