NfκBin: A Machine Learning based Method for Screening Nf-κB Inhibitors.

Abstract

Nuclear Factor kappa B (NF-{kappa}B) is a transcription factor whose upregulation is associated in chronic inflammatory diseases, including rheumatoid arthritis, inflammatory bowel disease, and asthma. In order to develop therapeutic strategies targeting NF-{kappa}B-related diseases, we developed a computational approach to predict drugs capable of inhibiting NF-{kappa}B signaling pathways. In this study, we utilized a dataset comprising 1,149 inhibitors and 1,332 non-inhibitors retrieved from PubChem. Chemical descriptors were computed using the PaDEL software, and relevant features were selected using advanced feature selection techniques. Initially, machine learning models were constructed using 2D descriptors, 3D descriptors, and molecular fingerprints, achieving maximum AUC values of 0.66, 0.56, and 0.66, respectively. To improve feature selection, we applied univariate analysis and SVC-L1 regularization to identify features that can effectively differentiate inhibitors from non-inhibitors. Using these selected features, we developed machine learning models, our support vector classifier achieved an AUC of 0.75 on the validation dataset. All models were trained using five-fold cross-validation and rigorously evaluated on an independent validation dataset, which was not used during training or hyperparameter optimization. Finally, our best-performing model was employed to screen FDA-approved drugs for potential NF-{kappa}B inhibitors. Notably, most of the predicted inhibitors corresponded to drugs previously identified as inhibitors in experimental studies, underscoring the models predictive reliability. Our best- performing models have been integrated into a standalone software and web server, Nf{kappa}Bin, to enable the scientific community to perform high-throughput screening of Nf-{kappa}B inhibitors from chemical libraries. (https://webs.iiitd.edu.in/raghava/nfkbin/). Highlights1) Inhibitors of Nf-{kappa}B can be used to manage disease like rheumatoid arthritis, asthma. 2) A novel tool Nf{kappa}Bin developed for screening Nf-{kappa}B inhibitors from chemical library. 3) Cutting-edge feature selection techniques used to identify the most relevant chemical descriptors. 4) Models were developed and validated on a robust dataset of inhibitors and non-inhibitors. 5) Integrated best-performing models into the Nf{kappa}Bin software and web server.