Rafael Zamora-ResendizDr. Aijuan DongDr. Dana LawrenceAnthony Rispoli2024-04-252024-04-252024-04-25http://hdl.handle.net/11603/33255The urgency of the COVID-19 pandemic has accelerated drug discovery efforts, prompting advancements in computational methods. This study aims to predict protein-ligand (PL) binding affinities using atomic-resolution structural data from SARS-CoV-2 interactions with ligands. Utilizing data from large-scale ensemble-docking experiments, Multivariate Linear Regression (MLR) and Random Forest (RF) regression models were trained. Despite marginal improvement with RF, both models struggled to establish reliable predictions, highlighting the complexity of PL binding affinity prediction. Future work entails exploring larger RF models, integrating deep learning approaches, and developing novel predictor features for enhanced predictive capabilities.11 pagesen-USCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/protein-ligand bindingSARS-CoV-2binding affinity predictionmachine-learningmolecular dynamics simulationsText