AI/ML modeling to enhance the capability of in vitro and in vivo tests in predicting human carcinogenicity

Abstract

This study aimed to develop an in silico model for predicting human carcinogenicity using advanced deep learning techniques, specifically Graph Neural Networks (GNN), through a multitask learning (MTL) approach. The MTL framework leveraged auxiliary tasks, including mutagenicity, genotoxicity, animal carcinogenicity, androgen and estrogen receptor binding, to enhance the model's predictive capabilities for the primary task of human carcinogenicity. Three distinct GNN architectures were used alongside various combinations of auxiliary tasks to evaluate the variations in performance metrics. Results demonstrated that multitask learning significantly enhances the predictive performance of GNN models compared to single-task learning for predicting human carcinogenicity. The best performed MTL model achieved an area under the curve of 0.89, along with a balanced accuracy of 82 %, and sensitivity and specificity values of 0.75 and 0.89, respectively. The developed multitask learning (MTL) models function on tasks that represent assays for identifying both genotoxic and non-genotoxic carcinogens, thereby enhancing the model's capability to predict human carcinogenic risk with greater accuracy. The advanced GNN models demonstrated effectiveness in addressing data imbalance issues frequently observed in biological datasets, mitigating the bias that typically favors one class over another. Overall, these results underscore the promise of GNN-based MTL models for reliable chemical screening and prioritization, particularly in predicting human carcinogenicity.