Multi-Conformation Enhanced Equivariant Graph Neural Network: Advancing Melting Point Prediction Accuracy for Organic Small Molecules.

ABSTRACT: The melting points of organic small molecules are governed by their three-dimensional (3D) conformations. We developed a novel framework termed 3D ^x G-MP (where x denotes the number of molecular conformations introduced into the model) to address the lack of 3D conformational information in existing melting point prediction approaches. This framework integrates a 3D equivariant graph neural network (EGNN) with 3D structural information and conformational data, and was trained on an extensive data set of 237,406 melting points. The 3D³G-MP model, which was augmented with three low-energy conformations per molecule, outperformed traditional methods (XGBoost, long short-term memory, two-dimensional (2D) graph attention networks) in melting point prediction,

SUBMITTER: Huang T 

PROVIDER: S-EPMC12594000 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature