This study addresses the challenge of assessing the carcinogenic potential of hazardous chemical mixtures, such as PFAS, which are known to contribute significantly to cancer development. Here, we propose a novel framework called HNNMixCancer that utilizes a Hybrid Neural Network (HNN) integrated into a machine-learning framework. This framework incorporates a mathematical model to simulate chemical mixtures and enables the creation of classification models for binary and multiclass classification and regression. Through extensive experimentation, we demonstrate that our HNN model outperforms other methodologies, including Random Forest, Bootstrap Aggregating, Adaptive Boosting, Support Vector Regressor, Gradient Boosting, Kernel Ridge, Decision Tree with AdaBoost, and KNeighbors, achieving a superior accuracy of 92.7% in binary classification. To address the limited availability of experimental data and enrich the training data, we generate an assumption-based virtual library of chemical mixtures using a known carcinogenic and non-carcinogenic single chemical for all the classification models. Remarkably, in this case, all methods achieve accuracies exceeding 98% for binary classification. In external validation tests, our HNN method achieves the highest accuracy of 80.5%. Furthermore, in multiclass classification, the HNN demonstrates an overall accuracy of 96.3%, outperforming RF, Bagging, and AdaBoost, which achieved 91.4%, 91.7%, and 80.2%, respectively. In regression models, HNN, RF, SVR, GB, KR, DT with AdaBoost, and KN achieve average R2 values of 0.96, 0.90, 0.77, 0.94, 0.96, 0.96, and 0.97, respectively, showcasing their effectiveness in predicting the concentration at which a chemical mixture becomes carcinogenic. Our method exhibits exceptional predictive power in prioritizing carcinogenic chemical mixtures, even when relying on assumption-based mixtures. This capability is particularly valuable for toxicology studies that lack experimental data on the carcinogenicity and toxicity of chemical mixtures. To our knowledge, this study introduces the first method for predicting the carcinogenic potential of chemical mixtures. The HNNMixCancer framework offers a novel alternative for dose-dependent carcinogen prediction. Ongoing efforts involve implementing the HNN method to predict mixture toxicity and expanding the application of HNNMixCancer to include multi-mixtures.

Host ribosome-associated scaffold protein Receptor for Activated C Kinase 1 (RACK1) is utilized by a diverse group of human viruses for Internal Ribosomal Entry Sites (IRES) – mediated translation of viral mRNAs. We recently reported inhibition of herpes virus by small molecules targeting the RACK1 functional site. Here, we tested these molecules against HIV-1 and HCV, as HIV-1 contains two potential IRES sites and HCV translation occurs exclusively through IRES. Compounds significantly downregulated activities of HIV-1- and HCV-related dicistronic reporter constructs in transfected HEK293T cells. The compounds also strongly downregulated production of the HIV-1 capsid protein p24 in HIV-infected cells, as well as production of HIV-1 Gag precursor p55 and p55-derived proteins p24 and p17 in cells infected with the HIV-1 virus. Hepatitis C virus (HCV) IRES activities were also significantly inhibited by RACK1 inhibitor compounds. Since a number of human and plant pathogenic viruses are reported to use IRES, the RACK1 compounds can be established as broad host-targeted antivirals.