Machine Learning Models Based on Enlarged Chemical Spaces for Screening Carcinogenic Chemicals.

Abstract

Machine learning (ML) models for screening carcinogenic chemicals are critical for the sound management of chemicals. Previous models were built on small-scale datasets and lacked applicability domain (AD) characterization that is necessary for regulatory applications of the models. In the current study, an enlarged dataset containing 1697 compounds (940 carcinogens and 757 non-carcinogens) was curated and employed to construct screening models based on 12 types of molecular fingerprints, four ML algorithms, and two graph neural networks. The AD of the optimal model was defined by a state-of-the-art characterization methodology (AD_SAL) based on the analysis of structure-activity landscapes (SALs). Results showed that an optimal model based on the random forest algorithm with the PubChem fingerprints outperformed previous ones, with an area under the receiver operating characteristic curve of 86.2% on the validation set imposed with the AD_SAL. The optimal model, coupled with the AD_SAL, was employed to screen carcinogenic chemicals in the Inventory of Existing Chemical Substances of China (IECSC) and plastic additives datasets, identifying 1282 chemicals from the IECSC and 841 plastic additives as carcinogenic chemicals. The screening model coupled with AD_SAL may serve as a promising tool for prioritizing chemicals of carcinogenic concern, facilitating the sound management of chemicals.