Binary Classification of a Large Collection of Environmental Chemicals from Estrogen Receptor Assays by Quantitative Structure–Activity Relationship and Machine Learning Methods

• Published in: Journal of chemical information and modeling Vol. 53; no. 12; pp. 3244 - 3261
• Main Authors: Zang, Qingda, Rotroff, Daniel M, Judson, Richard S
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 23.12.2013
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Chemical compounds; Chemicals; Chemistry; Computer science; control theory; systems; Computer systems performance. Reliability; Data processing. List processing. Character string processing; Discriminant Analysis; Endocrine Disruptors - classification; Endocrine Disruptors - pharmacology; Environmental Monitoring; Estrogens; Exact sciences and technology; General and physical chemistry; General. Nomenclature, chemical documentation, computer chemistry; High-Throughput Screening Assays; Humans; Learning and adaptive systems; Memory organisation. Data processing; Molecular structure; Quantitative Structure-Activity Relationship; Receptors, Estrogen - agonists; Receptors, Estrogen - antagonists & inhibitors; Receptors, Estrogen - metabolism; Risk Assessment; Software; Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry; Water Pollutants, Chemical - classification; Water Pollutants, Chemical - pharmacology; High throughput screening; Estrogen receptor; Agonist; Very large databases; Modeling; Information loss; Computational chemistry; Structure activity relation; Information quality; Vector support machine; Program proof; Antagonist; Skewed distribution; Pattern extraction; Binary classification; Discriminant analysis; Small signal; Chemical structure; Virtual screening; Cluster; Oversampling; Medical screening; Experimental study; Random decision forests; In vitro; Classification and Regression Tree; Subsampling; Scene analysis; Program test; Property structure relationship; Artificial intelligence
• DOI: 10.1021/ci400527b
• ISSN: 1549-9596; 1549-960X

There are thousands of environmental chemicals subject to regulatory decisions for endocrine disrupting potential. The ToxCast and Tox21 programs have tested ∼8200 chemicals in a broad screening panel of in vitro high-throughput screening (HTS) assays for estrogen receptor (ER) agonist and antagonist activity. The present work uses this large data set to develop in silico quantitative structure–activity relationship (QSAR) models using machine learning (ML) methods and a novel approach to manage the imbalanced data distribution. Training compounds from the ToxCast project were categorized as active or inactive (binding or nonbinding) classes based on a composite ER Interaction Score derived from a collection of 13 ER in vitro assays. A total of 1537 chemicals from ToxCast were used to derive and optimize the binary classification models while 5073 additional chemicals from the Tox21 project, evaluated in 2 of the 13 in vitro assays, were used to externally validate the model performance. In order to handle the imbalanced distribution of active and inactive chemicals, we developed a cluster-selection strategy to minimize information loss and increase predictive performance and compared this strategy to three currently popular techniques: cost-sensitive learning, oversampling of the minority class, and undersampling of the majority class. QSAR classification models were built to relate the molecular structures of chemicals to their ER activities using linear discriminant analysis (LDA), classification and regression trees (CART), and support vector machines (SVM) with 51 molecular descriptors from QikProp and 4328 bits of structural fingerprints as explanatory variables. A random forest (RF) feature selection method was employed to extract the structural features most relevant to the ER activity. The best model was obtained using SVM in combination with a subset of descriptors identified from a large set via the RF algorithm, which recognized the active and inactive compounds at the accuracies of 76.1% and 82.8% with a total accuracy of 81.6% on the internal test set and 70.8% on the external test set. These results demonstrate that a combination of high-quality experimental data and ML methods can lead to robust models that achieve excellent predictive accuracy, which are potentially useful for facilitating the virtual screening of chemicals for environmental risk assessment.