Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances.

Intrinsic aqueous solubility is a foundational property for understanding the chemical, technological, pharmaceutical, and environmental behavior of drug substances. Despite years of solubility research, molecular structure-based prediction of the intrinsic aqueous solubility of drug substances is still under active investigation. This paper describes the authors' systematic data-driven modelling in which two fit-for-purpose training data sets for intrinsic aqueous solubility were collected and curated, and three quantitative structure-property relationships were derived to make predictions for the most recent solubility challenge. All three models perform well individually, while being mechanistically transparent and easy to understand. Molecular descriptors involved in the models are related to the following key steps in the solubility process: dissociation of the molecule from the crystal, formation of a cavity in the solvent, and insertion of the molecule into the solvent. A consensus modeling approach with these models remarkably improved prediction capability and reduced the number of strong outliers by more than two times. The performance and outliers of the second solubility challenge predictions were analyzed retrospectively. All developed models have been published in the QsarDB.org repository according to FAIR principles and can be used without restrictions for exploring, downloading, and making predictions.

Machine Learning Quantitative Structure-Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons.

Ionic liquids (ILs) are known for their unique characteristics as solvents and electrolytes. Therefore, new ILs are being developed and adapted as innovative chemical environments for different applications in which their properties need to be understood on a molecular level. Computational data-driven methods provide means for understanding of properties at molecular level, and quantitative structure-property relationships (QSPRs) provide the framework for this. This framework is commonly used to study the properties of molecules in ILs as an environment. The opposite situation where the property is considered as a function of the ionic liquid does not exist. The aim of the present study was to supplement this perspective with new knowledge and to develop QSPRs that would allow the understanding of molecular interactions in ionic liquids based on the structure of the cationic moiety. A wide range of applications in electrochemistry, separation and extraction chemistry depends on the partitioning of solutes between the ionic liquid and the surrounding environment that is characterized by the gas-ionic liquid partition coefficient. To model this property as a function of the structure of a cationic counterpart, a series of ionic liquids was selected with a common bis-(trifluoromethylsulfonyl)-imide anion, [Tf2N]-, for benzene, hexane and cyclohexane. MLR, SVR and GPR machine learning approaches were used to derive data-driven models and their performance was compared. The cross-validation coefficients of determination in the range 0.71-0.93 along with other performance statistics indicated a strong accuracy of models for all data series and machine learning methods. The analysis and interpretation of descriptors revealed that generally higher lipophilicity and dispersion interaction capability, and lower polarity in the cations induces a higher partition coefficient for benzene, hexane, cyclohexane and hydrocarbons in general. The applicability domain analysis of models concluded that there were no highly influential outliers and the models are applicable to a wide selection of cation families with variable size, polarity and aliphatic or aromatic nature.

Binary and multi-class classification for androgen receptor agonists, antagonists and binders.

Androgens and androgen receptor regulate a variety of biological effects in the human body. The impaired functioning of androgen receptor may have different adverse health effects from cancer to infertility. Therefore, it is important to determine whether new chemicals have any binding activity and act as androgen agonists or antagonists before commercial use. Due to the large number of chemicals that require experimental testing, the computational methods are a viable alternative. Therefore, the aim of the present study was to develop predictive QSAR models for classifying compounds according to their activity at the androgen receptor. A large data set of chemicals from the CoMPARA project was used for this purpose and random forest classification models have been developed for androgen binding, agonistic, and antagonistic activity. In addition, a unique effort has been made for multi-class approach that discriminates between inactive compounds, agonists and antagonists simultaneously. For the evaluation set, the classification models predicted agonists with 80% of accuracy and for the antagonists' and binders' the respective metrics were 72% and 78%. Combining agonists, antagonists and inactive compounds into a multi-class approach added complexity to the modelling task and resulted to 64% prediction accuracy for the evaluation set. Considering the size of the training data sets and their imbalance, the achieved evaluation accuracy is very good. The final classification models are available for exploring and predicting at QsarDB repository (https://doi.org/10.15152/QDB.236).

Logistic Classification Models for pH-Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System.

Permeability is used to describe and evaluate the absorption of drug substances in the human gastrointestinal tract (GIT). Permeability is largely dependent on fluctuating pH that causes the ionization of drug substances and also influences regional absorption in the GIT. Therefore, classification models that characterize permeability at wide ranges of pH were derived in the current study. For this, drug substances were described with six data series that were measured with a parallel artificial membrane permeability assay (PAMPA), including a permeability profile at four pH values (3, 5, 7.4, and 9), and the highest and intrinsic membrane permeability. Logistic regression classification models were developed and compared by using two distinct sets of descriptors: (1) a hydrophobicity descriptor, the logarithm of the octanol-water partition (logPow) or distribution (logD) coefficient and (2) theoretical molecular descriptors. In both cases, models have good classification and descriptive capabilities for the training set (accuracy: 0.76-0.91). Triple validation with three sets of drug substances shows good prediction capability for all models: validation set (accuracy: 0.73-0.91), external validation set (accuracy: 0.72-0.9), and the permeability classes of FDA reference drugs for the biopharmaceutical classification system (BCS) (accuracy: 0.72-0.88). The identification of BCS permeability classes was further improved with decision trees that consolidated predictions from models with each descriptor type. These decision trees have higher confidence and accuracy (0.91 for theoretical molecular descriptors and 0.81 for hydrophobicity descriptors) than the individual models in assigning drug substances into BCS permeability classes. A detailed analysis of classification models and related decision trees suggests that they are suitable for predicting classes of permeability for passively transported drug substances, including specifically within the BCS framework. All developed models are available at the QsarDB repository ( http://dx.doi.org/10.15152/QDB.206 ).

Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints.

BACKGROUND: Quantitative and qualitative structure­activity relationships (QSARs) have been used to understand chemical behavior for almost a century. The main source of QSAR models is the scientific literature, but the open question is how well these models are documented. OBJECTIVES: The main aim of this study was to critically analyze the publication practices of QSARs with regard to transparency, potential reproducibility, and independent verification. The focus was on the level of technical completeness of the published QSARs. METHODS: A total of 1,533 QSAR articles reporting 79 individual endpoints, mostly in environmental and health science, were reviewed. The QSAR parameters required for technical completeness were grouped into five categories: chemical structures, experimental endpoint values, descriptor values, mathematical representation of the model, and predicted endpoint values. The data were summarized and discussed using Circos plots. RESULTS: Altogether, 42.5% of the reviewed articles were found to be potentially reproducible. The potential reproducibility for different endpoint groups varied; the respective rates were 39% for physical and chemical properties, 52% for ecotoxicity, 56% for environmental fate, 30% for human health, and 32% for toxicokinetics. The reproducibility of QSARs is discussed and placed in the context of the reproducibility of the experimental methods. Included are 65 references to open QSAR datasets as examples of models restored from scientific articles. DISCUSSION: Strikingly poor documentation of QSARs was observed, which reduces the transparency, availability, and consequently, the application of research results in scientific, industrial, and regulatory areas. A list of the components needed to ensure the best practices for QSAR reporting is provided, allowing long-term use and preservation of the models. This list also allows an assessment of the reproducibility of models by interested parties such as journal editors, reviewers, regulators, evaluators, and potential users. https://doi.org/10.1289/EHP3264.

Virtual Screening for HIV Protease Inhibitors Using a Novel Database Filtering Procedure.

A virtual screening to find novel inhibitors for HIV protease was performed on the ZINC database.1 A critical part in virtual screening and associated techniques is preliminary database filtering and size reduction and for that purpose a novel feature matrix matching procedure was used. The reduction of ∼14 million available ligands to a subset of 14299 ligands was achieved with a structure based approach where the analysis of the 3D structure of the active site of the protease produced a graph with hydrogen bond donor, hydrogen bond acceptor and hydrophobic subsites represented as graph nodes. A similar treatment was also applied to the compound database content and the comparison of binding site and ligand graphs was used to preselect potentially active ligands. The resulting set was further subjected to docking. The algorithm used was able to find several novel as well as previously known and experimentally tested ligands, demonstrating the validity of the approach.

QSAR DataBank - an approach for the digital organization and archiving of QSAR model information.

BACKGROUND: Research efforts in the field of descriptive and predictive Quantitative Structure-Activity Relationships or Quantitative Structure-Property Relationships produce around one thousand scientific publications annually. All the materials and results are mainly communicated using printed media. The printed media in its present form have obvious limitations when they come to effectively representing mathematical models, including complex and non-linear, and large bodies of associated numerical chemical data. It is not supportive of secondary information extraction or reuse efforts while in silico studies poses additional requirements for accessibility, transparency and reproducibility of the research. This gap can and should be bridged by introducing domain-specific digital data exchange standards and tools. The current publication presents a formal specification of the quantitative structure-activity relationship data organization and archival format called the QSAR DataBank (QsarDB for shorter, or QDB for shortest). RESULTS: The article describes QsarDB data schema, which formalizes QSAR concepts (objects and relationships between them) and QsarDB data format, which formalizes their presentation for computer systems. The utility and benefits of QsarDB have been thoroughly tested by solving everyday QSAR and predictive modeling problems, with examples in the field of predictive toxicology, and can be applied for a wide variety of other endpoints. The work is accompanied with open source reference implementation and tools. CONCLUSIONS: The proposed open data, open source, and open standards design is open to public and proprietary extensions on many levels. Selected use cases exemplify the benefits of the proposed QsarDB data format. General ideas for future development are discussed.

Combined approach using ligand efficiency, cross-docking, and antitarget hits for wild-type and drug-resistant Y181C HIV-1 reverse transcriptase.

New hits against HIV-1 wild-type and Y181C drug-resistant reverse transcriptases were predicted taking into account the possibility of some of the known metabolism interactions. In silico hits against a set of antitargets (i.e., proteins or nucleic acids that are off-targets from the desired pharmaceutical target objective) are used to predict a simple, visual measure of possible interactions for the ligands, which helps to introduce early safety considerations into the design of compounds before lead optimization. This combined approach consists of consensus docking and scoring: cross-docking to a group of wild-type and drug-resistant mutant proteins, ligand efficiency (also called binding efficiency) indices as new ranking measures, pre- and postdocking filters, a set of antitargets and estimation, and minimization of atomic clashes. Diverse, small-molecule compounds with new chemistry (such as a triazine core with aromatic side chains) as well as known drugs for different applications (oxazepam, chlorthalidone) were highly ranked to the targets having binding interactions and functional group spatial arrangements similar to those of known inhibitors, while being moderate to low binders to the antitargets. The results are discussed on the basis of their relevance to medicinal and computational chemistry. Optimization of ligands to targets and off-targets or antitargets is foreseen to be critical for compounds directed at several simultaneous sites.

Pseudouridylation of 23S rRNA helix 69 promotes peptide release by release factor RF2 but not by release factor RF1.

Pseudouridine [Ψ] is a frequent base modification in the ribosomal RNA [rRNA] and may be involved in the modulation of the conformational flexibility of rRNA helix-loop structures during protein synthesis. Helix 69 of 23S rRNA contains pseudouridines at the positions 1911, 1915 and 1917 which are formed by the helix 69-specific synthase RluD. The growth defect caused by the lack of RluD can be rescued by mutations in class I release factor RF2, indicating a role for helix 69 pseudouridines in translation termination. We investigated the role of helix 69 pseudouridines in peptide release by release factors RF1 and RF2 in an in vitro system consisting of purified components of the Escherichia coli translation apparatus. Lack of all three pseudouridines in helix 69 compromised the activity of RF2 about 3-fold but did not significantly affect the activity of RF1. Reintroduction of pseudouridines into helix 69 by RluD-treatment restored the activity of RF2 in peptide release. A Ψ-to-C substitution at the 1917 position caused an increase in the dissociation rate of RF1 and RF2 from the postrelease ribosome. Our results indicate that the presence of all three pseudouridines in helix 69 stimulates peptide release by RF2 but has little effect on the activity of RF1. The interactions around the pseudouridine at the 1917 position appear to be most critical for a proper interaction of helix 69 with release factors.

Ribosomal intersubunit bridge B2a is involved in factor-dependent translation initiation and translational processivity.

Intersubunit bridges are important for holding together subunits in the 70S ribosome. Moreover, a number of intersubunit bridges have a role in modulating the activity of the ribosome during translation. Ribosomal intersubunit bridge B2a is formed by the interaction between the conserved 23S rRNA helix-loop 69 (H69) and the top of the 16S rRNA helix 44. Within the 70S ribosome, bridge B2a contacts translation factors and the A-site tRNA. In addition to bridging the subunits, bridge B2a has been invoked in a number of other ribosomal functions from initiation to termination. In the present work, single-nucleotide substitutions were inserted at positions 1912 and 1919 of Escherichia coli 23S rRNA (helix 69), which are involved in important intrahelical and intersubunit tertiary interactions in bridge B2a. The resulting ribosomes had a severely reduced activity in a cell-free translation elongation assay, but displayed a nearly wild-type-level peptidyl transferase activity. In vitro reassociation efficiency decreased with all of the H69 variant 50S subunits, but was severest with the A1919C and DeltaH69 variants. The mutations strongly affected initiation-factor-dependent 70S initiation complex formation, but exhibited a minor effect on the nonenzymatic initiation process. The mutations decreased ribosomal processivity in vitro and caused a progressive depletion of 50S subunits in polysomal fractions in vivo. Mutations at position 1919 decreased the stability of a dipeptidyl-tRNA in the A-site, whereas the binding of the dipeptidyl-tRNA was rendered more stable with 1912 and DeltaH69 mutations. Our results suggest that the H69 of 23S rRNA functions as a control element during enzymatic steps of translation.

Design of multi-binding-site inhibitors, ligand efficiency, and consensus screening of avian influenza H5N1 wild-type neuraminidase and of the oseltamivir-resistant H274Y variant.

The binding sites of wild-type avian influenza A H5N1 neuraminidase, as well as those of the Tamiflu (oseltamivir)-resistant H274Y variant, were explored computationally to design inhibitors that target simultaneously several adjacent binding sites of the open conformation of the virus protein. The compounds with the best computed free energies of binding, in agreement by two docking methods, consensus scoring, and ligand efficiency values, suggest that mimicking a polysaccharide, beta-lactam, and other structures, including known drugs, could be routes for multibinding site inhibitor design. This new virtual screening method based on consensus scoring and ligand efficiency indices is introduced, which allows the combination of pharmacodynamic and pharmacokinetic properties into unique measures.

Modeling the toxicity of chemicals to Tetrahymena pyriformis using heuristic multilinear regression and heuristic back-propagation neural networks.

During the last years, considerable effort has been devoted to model the toxicity of chemicals to Tetrahymena pyriformis for medium and large sized data sets using various artificial neural network (ANN) techniques. Motivation behind this has been to model highly complex relationships with nonlinear character making it possible to describe wide structural diversity within one model. The current work compares the performance of two heuristic methods in developing quantitative structure-activity relationship (QSAR) models: the best multilinear regression (BMLR) approach and the heuristic back-propagation neural networks (hBNN). The modeling is based on a diverse data set of 1371 organic chemicals with toxicity data (log(1/IGC50)) collected from the literature. The toxicity values correspond to the static 40-h Tetrahymena pyriformis population growth impairment assay. The comparison of the two methods showed that the BMLR approach produces acceptable QSAR models (R2 = 0.726), whereas the hBNN method produced a statistically more significant model (R2 = 0.826) for the given endpoint. The hBNN method was able to relate different descriptors to the toxicity than the BMLR method. Both models were validated with an external prediction set. The descriptors in the models were analyzed and discussed.

QSPR modeling of solubility of polyaromatic hydrocarbons and fullerene in 1-octanol and n-heptane.

Solubility of polyaromatic hydrocarbons (PAH) and carbon nanostructures is important both from the technical and environmental points of view. In the present work, two general quantitative structure-property relationship (QSPR) models were developed, describing the solubility of PAH-s and fullerene (C60) in two different condensed media (1-octanol and n-heptane). Statistically good QSPR models were obtained by using forward selection techniques from large space of theoretical molecular descriptors. The physical meaning of the models is discussed and analyzed.

Open computing grid for molecular science and engineering.

Grid is an emerging infrastructure for distributed computing that provides secure and scalable mechanisms for discovering and accessing remote software and data resources. Applications built on this infrastructure have great potential for addressing and solving large scale chemical, pharmaceutical, and material science problems. The article describes the concept behind grid computing and will present the OpenMolGRID system that is an open computing grid for molecular science and engineering. This system provides grid enabled components, such as a data warehouse for chemical data, software for building QSPR/QSAR models, and molecular engineering tools for generating compounds with predefined chemical properties or biological activities. The article also provides an overview about the availability of chemical applications in the grid.

A general QSPR treatment for dielectric constants of organic compounds.

Multilinear regression and neural network methods have been used to develop QSPR models for the prediction of the dielectric constant (epsilon) and Kirkwood function (epsilon - 1)/(2epsilon + 1) of organic liquids. Both methods can provide acceptable models for the prediction of these properties. The QSPR models developed from the training set of 155 diverse compounds use theoretical molecular descriptors encoding electronic properties of the molecule and the intermolecular interaction between molecules. The QSPR models for the Kirkwood function appear to be more reliable than the models for the dielectric constant. The average prediction error of the best model for the dielectric constant is 27.0%. The average prediction error of the best model for the Kirkwood function is 4.1%.