Pruned Machine Learning Models to Predict Aqueous Solubility

Alexander L. Perryman, Daigo Inoyama, Jimmy S. Patel, Sean Ekins, Joel S. Freundlich

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Solubility is a key metric for therapeutic compounds. Conversely, insoluble compounds cloud the accuracy of assays at all stages of chemical biology and drug discovery. Herein, we disclose naïve Bayesian classifier models to predict aqueous solubility. Publicly accessible aqueous solubility data were used to create two full, or nonpruned, training sets. These two sets were also combined to create a full fused set, and a training set comprised of a literature collation of solubility data was also considered as a reference. We tested different extents of data pruning on the training sets and constructed machine learning models that were evaluated with two independent, external test sets that contained compounds that were different from the training sets. The best pruned and fused model was significantly more accurate, in comparison to either the full model or the full fused model, with the prediction of these external test sets. By carefully removing data from the training set, less information can be used to create more accurate machine learning models for aqueous solubility. This knowledge and the curated training sets should prove useful to future machine learning approaches.

Original languageEnglish (US)
Pages (from-to)16562-16567
Number of pages6
JournalACS Omega
Volume5
Issue number27
DOIs
StatePublished - Jul 14 2020

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)

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