This work focuses on deep learning-based molecular property prediction and generalization. By integrating pretrained molecular models, graph neural networks, and physicochemical descriptors, we developed a predictive framework for the melting points of organic molecules. Using a dataset containing more than 230,000 organic compounds, the framework combines local molecular topological representations with global physicochemical information, including molecular polarity, shape, and energetics, and evaluates model generalization under multiple validation settings. The results show that global physicochemical information can consistently complement data-driven local topological representations and provide reproducible predictive gains for molecules with unseen structural motifs. This work offers a new perspective on multi-source information integration and robust generalization assessment for complex molecular property prediction.
Link：https://pubs.rsc.org/ra/article/doi/10.1039/d6ra05490k/1296807/Global-descriptors-informed-learning-for-organic