Machine Learning in Prediction of Intrinsic Aqueous Solubility of Drug-like Compounds: Generalization, Complexity or Predictive Ability?

04 August 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Here, we present a collection of publicly available
intrinsic aqueous solubility data of 829 drug-like
compounds. Four different machine learning algorithms
(random forest, light GBM, partial least squares and
LASSO) coupled with multi-stage permutation
importance for feature selection and Bayesian hyperparameter optimization were employed for the
prediction of solubility based on chemical structural
information. Our results have shown that LASSO
yielded the best predictive ability on an external test set
with and RMSE(test) of 0.70 log points and 105 features
in the model. Taking into account the number of
descriptors as well, an RF model achieved the best
balance between complexity and predictive ability with
an RMSE(test) of 0.72 with only 17 features. We
propose a ranking score for choosing the best model, as
test set performance is only one of the factors in creating
an applicable model. The ranking score is a weighted
combination of generalization, number of features
involved and test set performance

The data related to this paper can be downloaded from 10.5281/zenodo.3968754


intrinsic solubility
random forest
drug discovery
Drug-like Molecules Aqueous solubilities

Supplementary weblinks


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.