Predictive Toxicology: Modeling Chemical Induced Toxicological Response Combining Circular Fingerprints with Random Forest and Support Vector Machine

Abstract

Modern drug discovery and toxicological research are under pressure, as the cost of developing and testing new chemicals for potential toxicological risk is rising. Extensive evaluation of chemical products for potential adverse effects is a challenging task, due to the large number of chemicals and the possible hazardous effects on human health. Safety regulatory agencies around the world are dealing with two major challenges. First, the growth of chemicals introduced every year in household products and medicines that need to be tested, and second the need to protect public welfare. Hence, alternative and more efficient toxicological risk assessment methods are in high demand. The Toxicology in the 21st Century (Tox21) consortium a collaborative effort was formed to develop and investigate alternative assessment methods. A collection of 10,000 compounds composed of environmental chemicals and approved drugs were screened for interference in biochemical pathways and released for crowdsourcing data analysis. The physicochemical space covered by Tox21 library was explored, measured by Molecular Weight (MW) and the octanol/water partition coefficient (cLogP). It was found that on average chemical structures had MW of 272.6 Daltons. In case of cLogP the average value was 2.476. Next relationships between assays were examined based on compounds activity profiles across the assays utilizing the Pearson correlation coefficient r. A cluster was observed between the Androgen and Estrogen Receptors and their ligand bind domains accordingly indicating presence of cross talks among the receptors. The highest correlations observed were between NR.AR and NR.AR_LBD, where it was r = 0.66 and between NR.ER and NR.ER_LBD, where it was r = 0.5. Our approach to model the Tox21 data consisted of utilizing circular molecular fingerprints combined with Random Forest and Support Vector Machine by modeling each assay independently. In all of the 12 sub-challenges our modeling approach achieved performance equal to or higher than 0.7 ROC-AUC showing strong overall performance. Best performance was achieved in sub-challenges NR.AR_LBD, NR.ER_LDB and NR.PPAR_gamma, where ROC-AUC of 0.756, 0.790, and 0.803 was achieved accordingly. These results show that computational methods based on machine learning techniques are well suited to support and play critical role in toxicological research.