In Silico Prediction of Aqueous Solubility: A Multimodel Protocol Based on Chemical Similarity

Aqueous solubility is one of the most important ADMET
properties
to assess and to optimize during the drug discovery process. At present,
accurate prediction of solubility remains very challenging and there
is an important need of independent benchmarking of the existing in
silico models such as to suggest solutions for their improvement.
In this study, we developed a new protocol for improved solubility
prediction by combining several existing models available in commercial
or free software packages. We first performed an evaluation of ten
in silico models for aqueous solubility prediction on several data
sets in order to assess the reliability of the methods, and we proposed
a new diverse data set of 150 molecules as relevant test set, SolDiv150.
We developed a random forest protocol to evaluate the performance
of different fingerprints for aqueous solubility prediction based
on molecular structure similarity. Our protocol, called a “multimodel
protocol”, allows selecting the most accurate model for a compound
of interest among the employed models or software packages, achieving r2 of 0.84 when applied to SolDiv150. We also
found that all models assessed here performed better on druglike molecules
than on real drugs, thus additional improvement is needed in this
direction. Overall, our approach enlarges the applicability domain
as demonstrated by the more accurate results for solubility prediction
obtained using our protocol in comparison to using individual models.