Hybrid Machine Learning and Graph Neural Network Approaches for Accurate Melting Point Prediction of Organic Crystals

Melting point prediction plays a pivotal role in chemistry, materials science, and pharmaceuticals, where it informs compound identification, purity assessment, and design of thermally robust materials. Despite advances in computational modeling, current methods face challenges in accuracy, interpretability, and computational efficiency, largely due to limited experimental datasets and the nonlinear, discontinuous relationship between molecular structure and melting behavior. In this study, we curate a harmonized dataset of 117 752 unique organic crystals by integrating and standardizing multiple public sources, emphasizing reproducibility and molecular uniqueness. We systematically evaluate classical machine learning models using molecular embeddings derived from SMILES strings, including ChemBERTa, MoLFormer, Morgan fingerprints, and Uni-Mol, and compare them with advanced graph neural network (GNN) architectures such as our custom implementation and the widely used Chemprop framework. Our analysis demonstrates that gradient-boosted tree models offer notable computational efficiency, while GNNs and transformer-based models like MoLFormer provide superior predictive accuracy, particularly when enhanced with attention mechanisms. By integrating ChemBERTa and MoLFormer embeddings in the TabM model, we achieve a mean absolute error (MAE) of 26.75 K and an RMSE of 35.36 K, surpassing individual model performance. These results highlight the benefits of hybrid modeling strategies that balance interpretability, scalability, and accuracy, offering a robust framework for accelerating materials discovery and pharmaceutical development through improved melting point prediction. Nevertheless, Chemprop exhibits considerably higher accuracy but demands approximately five times greater resources during training.