Machine learning prediction of electron Iso-density surface areas from molecular descriptors

Electron iso-density surfaces (EIS) provide quantum mechanically rigorous descriptions of molecular size governing intermolecular interactions and binding affinity. We developed machine learning models predicting EIS surface area from molecular descriptors using 288 diverse organic molecules. Eight algorithms were evaluated including Ridge regression, Gaussian Process Regression, Random Forest, and XGBoost using 5-fold cross-validation. Gaussian Process Regression achieved best test set performance ( R 2 = 0 . 9896 ), with mean absolute errors below 4.0 Å 2 (3% uncertainty). SHAP analysis revealed atomic count, molecular weight, and connectivity dominate predictions, enabling computationally efficient property estimation with chemical interpretability.