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In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models
Author(s) -
Raevsky Oleg A.,
Polianczyk Daniel E.,
Grigorev Veniamin Yu.,
Raevskaja Olga E.,
Dearden John C.
Publication year - 2015
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201400144
Subject(s) - quantitative structure–activity relationship , molecular descriptor , support vector machine , solubility , linear regression , mean squared error , interpretation (philosophy) , biological system , construct (python library) , predictive modelling , mathematics , chemistry , artificial intelligence , computer science , machine learning , statistics , organic chemistry , biology , programming language
32 Quantitative Structure‐Property Relationship (QSPR) models were constructed for prediction of aqueous intrinsic solubility of liquid and crystalline chemicals. Data sets contained 1022 liquid and 2615 crystalline compounds. Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Random Forest (RF) methods were used to construct global models, and k ‐nearest neighbour ( k NN), Arithmetic Mean Property (AMP) and Local Regression Property (LoReP) were used to construct local models. A set of the best QSPR models was obtained: for liquid chemicals with RMSE (root mean square error) of prediction in the range 0.50–0.60 log unit; for crystalline chemicals 0.80–0.90 log unit. In the case of global models the large number of descriptors makes mechanistic interpretation difficult. The local models use only one or two descriptors, so that a medicinal chemist working with sets of structurally‐related chemicals can readily estimate their solubility. However, construction of stable local models requires the presence of closely related neighbours for each chemical considered. It is probable that a consensus of global and local QSPR models will be the optimal approach for construction of stable predictive QSPR models with mechanistic interpretation.