Machine Learning - driven insights for predicting the impact of nanoparticles on the functionality of biomolecules, Illustrated by the case of DNA Damage-Inducible Transcript 3 (CHOP) inhibitors

Abstract

This study introduces a pioneering machine learning (ML)-based approach for predicting the impact of nanoparticle (NP) carriers on the functionality of attached small biomolecules. It was hypothesised that NP interactions induce measurable perturbations in the atomic environment of the small biomolecules, which are reliably captured by chemical shifts in 13C and 1H NMR spectroscopy. Ten datasets were generated by combining 13C, 1H NMR spectroscopy data, derived from SMILES notations and molecular features provided by PubChem. The resulting datasets were used to train predictive models via traditional ML algorithms (Scikit-learn) and Deep Neural Network DNN (PyTorch). The methodology was demonstrated through a quantitative high-throughput screening (qHTS) focused on DNA Damage-Inducible Transcript 3 (CHOP) inhibitors. The optimal ML performance was achieved by the Random Forest Classifier, which was trained on 19,184 samples and tested on 4,000, resulting in 81.1% accuracy, 83.4% precision, 77.7% recall, 80.4% F1-score, 81.1% ROC, and a five-fold cross-validation score of 0.821. Complementing the main study, two computational approaches were developed to enhance CHOP inhibitor prediction. The first identifies the most desirable/undesirable functional groups for CHOP inhibition. The second, a CID_SID ML model, achieved 90.1% accuracy in predicting whether compounds designed for other purposes possess CHOP inhibition potential.