OPERA models for predicting physicochemical properties and environmental fate endpoints

Abstract

The collection of chemical structure information and associated experimental data for quantitative structure–activity/property relationship (QSAR/QSPR) modeling is facilitated by an increasing number of public databases containing large amounts of useful data. However, the performance of QSAR models highly depends on the quality of the data and modeling methodology used. This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes. This study primarily uses data from the publicly available PHYSPROP database consisting of a set of 13 common physicochemical and environmental fate properties. These datasets have undergone extensive curation using an automated workflow to select only high-quality data, and the chemical structures were standardized prior to calculation of the molecular descriptors. The modeling procedure was developed based on the five Organization for Economic Cooperation and Development (OECD) principles for QSAR models. A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2–15, with an average of 11 descriptors). The sizes of the modeled datasets varied from 150 chemicals for biodegradability half-life to 14,050 chemicals for logP, with an average of 3222 chemicals across all endpoints. The optimal models were built on randomly selected training sets (75%) and validated using fivefold cross-validation (CV) and test sets (25%). The CV Q2 of the models varied from 0.72 to 0.95, with an average of 0.86 and an R2 test value from 0.71 to 0.96, with an average of 0.82. Modeling and performance details are described in QSAR model reporting format and were validated by the European Commission’s Joint Research Center to be OECD compliant. All models are freely available as an open-source, command-line application called OPEn structure–activity/property Relationship App (OPERA). OPERA models were applied to more than 750,000 chemicals to produce freely available predicted data on the U.S. Environmental Protection Agency’s CompTox Chemistry Dashboard.Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0263-1) contains supplementary material, which is available to authorized users.

Property Estimation of Per- and Polyfluoroalkyl Substances: A Comparative Assessment of Estimation Methods

“…Models for 13 common physicochemical properties and environmental endpoints have been developed. The OPERA suite of QSPR models can provide physicochemical property estimates for both the neutral and ionized form of ionizable organics, because all chemicals are passed through QSAR‐ready preparation (Mansouri et al 2018). The aim of the NICEATM model estimates is to create methods and gather data to strengthen the science base for risk assessment (National Institute of Environmental Health Sciences.…”

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

“…The best models were selected and implemented in OPERA, a free and open-source suite of QSAR models (Mansouri et al 2016b(Mansouri et al , 2018. Both OPERA's global and local AD approaches, as well as the accuracy estimation procedure, were applied to the predictions (Mansouri et al 2018).…”

High-Throughput Screening to Predict Chemical-Assay Interference

“…From each curated structure, a set of 677 2D descriptors was computed. A set of 647 descriptors including topological and physicochemical descriptors were computed using RDKit tool kit 26 implemented in the PyDPI python library 27 , and an additional set of 30 physicochemical descriptors were computed using the OPERA models 28 .…”