A Concise Bond-Distance Summation Descriptor for Effective Melting Point Prediction of Boronic Acids

Le résumé fourni par la source

Predicting the melting points of boronic acids is crucial for guiding synthetic strategies and understanding their physicochemical behaviors. In this study, we introduce a novel bond-distance summation descriptor, a concise 20-component vector that numerically encodes the molecular structure by summing atomic numbers over the shortest paths from the boron atom. We benchmarked this descriptor against four established feature extraction methods Coulomb Matrix, Mordred, Morgan Fingerprints, and Molecular ACCess System (MACCS) and evaluated the predictive accuracy of five machine learning models: Decision Tree, Random Forest, XGBoost, LightGBM, and Support Vector Machine. Despite having far fewer features than the high-dimensional Mordred and Morgan representations, our 20-length descriptor achieves competitive results, particularly when paired with XGBoost, which consistently exhibits superior performance in terms of Mean Absolute Error (MAE) and R2 score. These findings underscore the potential of a concise, interpretable descriptor for effective melting point prediction, paving the way for the future integration of this scheme into broader cheminformatics applications.

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