Prediction of aqueous solubility of organic compounds by the general solubility equation (GSE)

The revised general solubility equation (GSE) is used along with four different methods including Huuskonen's artificial neural network (ANN) and three multiple linear regression (MLR) methods to estimate the aqueous solubility of a test set of the 21 pharmaceutically and environmentally interesting compounds. For the selected test sets, it is clear that the GSE and ANN predictions are more accurate than MLR methods. The GSE has the advantages of being simple and thermodynamically sound. The only two inputs used in the GSE are the Celsius melting point (MP) and the octanol water partition coefficient (K(ow)). No fitted parameters and no training data are used in the GSE, whereas other methods utilize a large number of parameters and require a training set. The GSE is also applied to a test set of 413 organic nonelectrolytes that were studied by Huuskonen. Although the GSE uses only two parameters and no training set, its average absolute errors is only 0.1 log units larger than that of the ANN, which requires many parameters and a large training set. The average absolute error AAE is 0.54 log units using the GSE and 0.43 log units using Huuskonen's ANN modeling. This study provides evidence for the GSE being a convenient and reliable method to predict aqueous solubilities of organic compounds.     

Prediction of drug solubility by the general solubility equation (GSE)

The revised general solubility equation (GSE) proposed by Jain and Yalkowsky is used to estimate the aqueous solubility of a set of organic nonelectrolytes studied by Jorgensen and Duffy. The only inputs used in the GSE are the Celsius melting point (MP) and the octanol water partition coefficient (K(ow)). These are generally known, easily measured, or easily calculated. The GSE does not utilize any fitted parameters. The average absolute error for the 150 compounds is 0.43 compared to 0.56 with Jorgensen and Duffy's computational method, which utilitizes five fitted parameters. Thus, the revised GSE is simpler and provides a more accurate estimation of aqueous solubility of the same set of organic compounds. It is also more accurate than the original version of the GSE.     

ADMET evaluation in drug discovery. 11. PharmacoKinetics Knowledge Base (PKKB): a comprehensive database of pharmacokinetic and toxic properties for drugs

Good and extensive experimental ADMET (absorption, distribution, metabolism, excretion, and toxicity) data is critical for developing reliable in silico ADMET models. Here we develop a PharmacoKinetics Knowledge Base (PKKB) to compile comprehensive information about ADMET properties into a single electronic repository. We incorporate more than 10?000 experimental ADMET measurements of 1685 drugs into the PKKB. The ADMET properties in the PKKB include octanol/water partition coefficient, solubility, dissociation constant, intestinal absorption, Caco-2 permeability, human bioavailability, plasma protein binding, blood-plasma partitioning ratio, volume of distribution, metabolism, half-life, excretion, urinary excretion, clearance, toxicity, half lethal dose in rat or mouse, etc. The PKKB provides the most extensive collection of freely available data for ADMET properties up to date. All these ADMET properties, as well as the pharmacological information and the calculated physiochemical properties are integrated into a web-based information system. Eleven separated data sets for octanol/water partition coefficient, solubility, blood-brain partitioning, intestinal absorption, Caco-2 permeability, human oral bioavailability, and P-glycoprotein inhibitors have been provided for free download and can be used directly for ADMET modeling. The PKKB is available online at http://cadd.suda.edu.cn/admet.     

A deep learning approach for the blind logP prediction in SAMPL6 challenge

Water octanol partition coefficient serves as a measure for the lipophilicity of a molecule and is important in the field of drug discovery. A novel method for computational prediction of logarithm of partition coefficient (logP) has been developed using molecular fingerprints and a deep neural network. The machine learning model was trained on a dataset of 12,000 molecules and tested on 2000 molecules. In this article, we present our results for the blind prediction of logP for the SAMPL6 challenge. While the best submission achieved a RMSE of 0.41 logP units, our submission had a RMSE of 0.61 logP units. Overall, we ranked in the top quarter out of the 92 submissions that were made. Our results show that the deep learning model can be used as a fast, accurate and robust method for high throughput prediction of logP of small molecules.     

Accurate solubility prediction with error bars for electrolytes: a machine learning approach

Accurate in silico models for predicting aqueous solubility are needed in drug design and discovery and many other areas of chemical research. We present a statistical modeling of aqueous solubility based on measured data, using a Gaussian Process nonlinear regression model (GPsol). We compare our results with those of 14 scientific studies and 6 commercial tools. This shows that the developed model achieves much higher accuracy than available commercial tools for the prediction of solubility of electrolytes. On top of the high accuracy, the proposed machine learning model also provides error bars for each individual prediction.     

Microscopic structure and solvation in dry and wet octanol

Octanol-water partition coefficients are extraordinarily successful for correlating and predicting numerous processes of pharmacological and environmental importance. The amphiphilic nature of the 1-octanol molecules allows the octanol phase to mimic the complexities of many different environments ranging from biomembranes to soil. However, the structural details of the octanol phase and whether its structure is altered upon water saturation are not yet fully understood. Configurational-bias Monte Carlo simulations in the Gibbs ensemble demonstrate that a diverse spectrum of hydrogen-bonded aggregates exists in dry and wet 1-octanol, and that water saturation substantially alters the 1-octanol environment from predominantly linear aggregates in dry octanol to larger cylindrical micelles with water cores in wet octanol. These simulation results are able to reconcile the conflicting views (chain-like or water-centered aggregates vs spherical micelles) of the 1-octanol structure inferred from thermodynamic arguments, spectroscopic measurements, and diffraction experiments. Calculated partition constants quantify the influence of water saturation on the solubility characteristics of the dry and wet octanol phases.     

三嗪类化合物溶解度参数及毒性构－效关系

测定了１２种三嗪类化合物的水溶解度，辛醇水分配系数和对发光菌的毒性，并用分子连结性指数建立了预测三嗪类化合物的溶解度，辛醇水分配系数及对发光菌毒性的定量结构活性相关方程，其中１０种化合物文献中未见报道。