Improving Vapor Pressure Prediction Through Integration of Multiple Molecular Representations: A Super Learner Approach

Abstract

Accurate prediction of vapor pressure is essential in chemical engineering, environmental science, and pharmaceutical development, impacting the volatility and stability of compounds. Traditional methods often fall short for complex and new molecular structures. This study introduces an advanced machine learning approach, integrating graph neural networks (GNNs), and CHEM-BERT models to improve prediction accuracy. Utilizing the largest dataset to date, we derived comprehensive chemical descriptors and fingerprints. We evaluated 19 predictive models, including ridge regression, random forest, support vector regression, and feed-forward neural networks, trained on diverse features like PaDEL and Morgan fingerprints, chemical descriptors, and Chem-BERT embeddings. Central to our methodology is the super learner architecture, which combines 19 multiple models to enhance accuracy. The super learner achieved a root mean squared error (RMSE) of 0.8200, outperforming individual models and previous reports. These successful results highlight the effectiveness of integrating GNNs and Chem-BERT for capturing detailed molecular information, setting a new benchmark for vapor pressure prediction. This study underscores the value of advanced machine learning techniques and comprehensive datasets, offering a robust tool for researchers and paving the way for future advancements in chemical property prediction.