In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

There
are little available toxicity data on the vast majority of
chemicals in commerce. High-throughput screening (HTS) studies, such
as those being carried out by the U.S. Environmental Protection Agency
(EPA) ToxCast program in partnership with the federal Tox21 research
program, can generate biological data to inform models for predicting
potential toxicity. However, physicochemical properties are also needed
to model environmental fate and transport, as well as exposure potential.
The purpose of the present study was to generate an open-source quantitative
structure–property relationship (QSPR) workflow to predict
a variety of physicochemical properties that would have cross-platform
compatibility to integrate into existing cheminformatics workflows.
In this effort, decades-old experimental property data sets available
within the EPA EPI Suite were reanalyzed using modern cheminformatics
workflows to develop updated QSPR models capable of supplying computationally
efficient, open, and transparent HTS property predictions in support
of environmental modeling efforts. Models were built using updated
EPI Suite data sets for the prediction of six physicochemical properties:
octanol–water partition coefficient (logP), water solubility
(logS), boiling point (BP), melting point (MP), vapor pressure (logVP),
and bioconcentration factor (logBCF). The coefficient of determination
(R2) between the estimated values and
experimental data for the six predicted properties ranged from 0.826
(MP) to 0.965 (BP), with model performance for five of the six properties
exceeding those from the original EPI Suite models. The newly derived
models can be employed for rapid estimation of physicochemical properties
within an open-source HTS workflow to inform fate and toxicity prediction
models of environmental chemicals.