Predicting the Octanol Solubility of Organic Compounds

Octanol/Air Partition Coefficient─A General-Purpose Fragment Model to Predict Log Koa from Molecular Structure

The octanol/air partition coefficient K_oa is important for assessing the bioconcentration of airborne xenobiotics in foliage and in air-breathing organisms. Moreover, K_oa informs about compound partitioning to aerosols and indoor dust, and complements the octanol/water partition coefficient K_ow and the air/water partition coefficient K_aw for multimedia fate modeling. Experimental log K_oa at 25 °C has been collected from literature for 2161 compounds with molecular weights from 16 to 959 Da. The curated data set covers 18.2 log units (from -1.0 to 17.2). A newly developed fragment model for predicting log K_oa from molecular structure outperforms COSMOtherm, EPI-Suite KOAWIN, OPERA, and linear solvation energy relationships (LSERs) regarding the root-mean-squared error (rms) and the maximum negative and positive errors (mne and mpe) (rms: 0.57 vs 0.86 vs 1.09 vs 1.19 vs 1.05-1.53, mne: -2.55 vs -3.95 vs -7.51 vs -7.54 vs (-5.63) - (-7.34), mpe: 2.91 vs 5.97 vs 7.54 vs 4.24 vs 6.89-10.2 log units). The prediction capability, statistical robustness, and sound mechanistic basis are demonstrated through initial separation into a training and prediction set (80:20%), mutual leave-50%-out validation, and target value scrambling in terms of temporarily wrong compound-K_oa allocations. The new general-purpose model is implemented in a fully automatized form in the ChemProp software available to the public. Regarding K_oa indirectly determined through K_ow and K_aw, a new approach is developed to convert from wet to dry octanol, enabling higher consistency in experimental (and thus also predicted) K_oa.

Phaeodactylum tricornutum as a model organism for testing the membrane penetrability of sulphonamide carbonic anhydrase inhibitors

Carbonic anhydrases (CAs) are ubiquitous metalloenzymes, which started to be investigated in detail in pathogenic, as well as non-pathogenic species since their pivotal role is to accelerate the physiological CO₂ hydration/dehydration reaction significantly. Here, we propose the marine unicellular diatom Phaeodactylum tricornutum as a model organism for testing the membrane penetrability of CA inhibitors (CAIs). Seven inhibitors belonging to the sulphonamide type and possessing a diverse scaffold have been explored for their in vitro inhibition of the whole diatom CAs and the in vivo inhibitory effect on the growth of P. tricornutum. Interesting, inhibition of growth was observed, in vivo, demonstrating that this diatom is a good model for testing the cell wall penetrability of this class of pharmacological agents. Considering that many pathogens are difficult and dangerous to grow in the laboratory, the growth inhibition of P. tricornutum with different such CAIs may be subsequently used to design inhibition studies of CAs from pathogenic organisms.

Estimating the Aqueous Solubility of Pharmaceutical Hydrates

Estimation of crystalline solute solubility is well documented throughout the literature. However, the anhydrous crystal form is typically considered with these models, which is not always the most stable crystal form in water. In this study, an equation which predicts the aqueous solubility of a hydrate is presented. This research attempts to extend the utility of the ideal solubility equation by incorporating desolvation energetics of the hydrated crystal. Similar to the ideal solubility equation, which accounts for the energetics of melting, this model approximates the energy of dehydration to the entropy of vaporization for water. Aqueous solubilities, dehydration and melting temperatures, and log P values were collected experimentally and from the literature. The data set includes different hydrate types and a range of log P values. Three models are evaluated, the most accurate model approximates the entropy of dehydration (ΔSd) by the entropy of vaporization (ΔSvap) for water, and utilizes onset dehydration and melting temperatures in combination with log P. With this model, the average absolute error for the prediction of solubility of 14 compounds was 0.32 log units.

Solubility of organic compounds in octanol: Improved predictions based on the geometrical fragment approach

Two new models are introduced to predict the solubility of chemicals in octanol (S_oct), taking advantage of the extensive character of log(S_oct) through a decomposition of molecules into so-called geometrical fragments (GF). They are extensively validated and their compliance with regulatory requirements is demonstrated. The first model requires just a molecular formula as input. Despite an extreme simplicity, it performs as well as an advanced random forest model involving 86 descriptors, with a root mean square error (RMSE) of 0.64 log units for an external test set of 100 molecules. For the second one, which requires the melting point T_m as input, introducing GF descriptors reduces the RMSE from about 0.7 to <0.5 log units, a performance that could previously be obtained only through the use of Abraham descriptors. A script is provided for easy application of the models, taking into account the limits of their applicability domains.

Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction

The task of learning an expressive molecular representation is central to developing quantitative structure-activity and property relationships. Traditional approaches rely on group additivity rules, empirical measurements or parameters, or generation of thousands of descriptors. In this paper, we employ a convolutional neural network for this embedding task by treating molecules as undirected graphs with attributed nodes and edges. Simple atom and bond attributes are used to construct atom-specific feature vectors that take into account the local chemical environment using different neighborhood radii. By working directly with the full molecular graph, there is a greater opportunity for models to identify important features relevant to a prediction task. Unlike other graph-based approaches, our atom featurization preserves molecule-level spatial information that significantly enhances model performance. Our models learn to identify important features of atom clusters for the prediction of aqueous solubility, octanol solubility, melting point, and toxicity. Extensions and limitations of this strategy are discussed.

Prediction of 1-octanol solubilities using data from the Open Notebook Science Challenge

Background

1-Octanol solubility is important in a variety of applications involving pharmacology and environmental chemistry. Current models are linear in nature and often require foreknowledge of either melting point or aqueous solubility. Here we extend the range of applicability of 1-octanol solubility models by creating a random forest model that can predict 1-octanol solubilities directly from structure.

Results

We created a random forest model using CDK descriptors that has an out-of-bag (OOB) R² value of 0.66 and an OOB mean squared error of 0.34. The model has been deployed for general use as a Shiny application.

Conclusion

The 1-octanol solubility model provides reasonably accurate predictions of the 1-octanol solubility of organic solutes directly from structure. The model was developed under Open Notebook Science conditions which makes it open, reproducible, and as useful as possible.Graphical abstract

Estimating the physicochemical properties of polyhalogenated aromatic and aliphatic compounds using UPPER: part 2. Aqueous solubility, octanol solubility and octanol-water partition coefficient

The UPPER (Unified Physicochemical Property Estimation Relationships) model uses additive and non-additive parameters to estimate 20 biologically relevant properties of organic compounds. The model has been validated by Lian and Yalkowsky (2014) on a data set of 700 hydrocarbons. Recently, Admire et al. (2014) expanded the model to predict the boiling and melting points of 1288 polyhalogenated benzenes, biphenyls, dibenzo-p-dioxins, diphenyl ethers, anisoles and alkanes. In this work, 19 new group descriptors are determined and used to predict the aqueous solubilities, octanol solubilities and the octanol-water coefficients.

The solubility of liquid and solid compounds in dry octan-1-ol

Using literature data on solubilities, equations have been constructed for the correlation of solubilities of liquids and solids in dry octanol, as log Soct (M). The best equation statistically uses Abraham descriptors together with the compound melting point. For 282 compounds the equation standard deviation is no more than 0.47 log units. If the melting point term is omitted the standard deviation rises to 0.63 log units. It is suggested that if Abraham descriptors are available, these equations represent the most satisfactory equations for the correlation and estimation of solubilities in dry octanol. If these descriptors are not available, then the simple equation of Yalkowsky can be used, although for 223 compounds the equation standard deviation rises to 0.71 log units.