Abstract
We describe three machine learning models submitted to the 2019 Solubility Challenge. All are founded on tree-like classifiers, with one model being based on Random Forest and another on the related Extra Trees algorithm. The third model is a consensus predictor combining the former two with a Bagging classifier. We call this consensus classifier Vox Machinarum, and here discuss how it benefits from the Wisdom of Crowds. On the first 2019 Solubility Challenge test set of 100 low-variance intrinsic aqueous solubilities, Extra Trees is our best classifier. One the other, a high-variance set of 32 molecules, we find that Vox Machinarum and Random Forest both perform a little better than Extra Trees, and almost equally to one another. We also compare the gold standard solubilities from the 2019 Solubility Challenge with a set of literature-based solubilities for most of the same compounds.
Highlights
Aqueous solubility remains one of the most significant challenges in drug development, with failure to produce bioavailable compounds potentially denying patients much-needed therapeutic interventions, while costing pharmaceutical companies years of time and hundreds of millions of dollars, euros or pounds
A dataset of druglike organic compounds of known intrinsic aqueous solubility was prepared from the following sources: DLS-100 [27,28,29], 2008 Solubility Challenge [23,25], Bergström et al (2004) [30], and Wassvik et al (2006) [31]
The multilayer perceptron (MLP) is a feed-forward neural network, of a kind which we previously found to be the most effective single ML method in an earlier solubility prediction study using the DLS-100 dataset [28,29]
Summary
Aqueous solubility remains one of the most significant challenges in drug development, with failure to produce bioavailable compounds potentially denying patients much-needed therapeutic interventions, while costing pharmaceutical companies years of time and hundreds of millions of dollars, euros or pounds. First principles approaches have made some progress in recent years, and in the longer term may provide the most satisfactory means of computing solubility [5,6,7,8,9,10,11]. Currently such first principles methods require a substantial amount of computer time and, despite potentially providing more theoretical insight, are generally less accurate in their quantitative predictions [3] than are the more empirical informatics approaches.
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