The reduction of original database to only solid state organic compounds resulted in small size of the datasets applied in the study. Due to such limitation of datasets it is most likely that some chemical compounds classes are underrepresented. This phenomenon is well known in cheminformatics as class imbalanced data . Cheng-Wei et al calculated a curated number of molecular descriptors of solvents which appears to be the correct way to preserve database diversity. Since its vulnerability to database correspondence to compound being assessed, the method should be provided with proper database. Alternatively machine could be trained on the go with a subset of bigger database chosen on compound's similarity(eg. by utilising extended similarity indices ). With highly probable emergence of new datasets, machine learning based approaches to QSPR will undoubtedly improve their performance.