Jun 2026· 2026 8th International Conference on Electronic Engineering and Informatics (EEI)· pp. 1169-1173· 0 citations· 11 references
Abstract
The rapid and accurate prediction of organic molecule properties is a key issue in drug screening, functional material design, and chemical reaction optimization. Due to the problem that traditional molecular descriptors rely on manual feature engineering and are difficult to fully express the topological structure of molecules, this paper constructs an organic molecule property prediction model based on graph neural networks. The molecule is represented as a graph structure composed of atomic nodes and chemical bond edges, and the local chemical environment and global molecular representation are learned through the message passing mechanism. Experiments selected three typical molecular property prediction tasks of ESOL, FreeSolv, and Lipophilicity, and compared with models such as random forest, support vector regression, multi-layer perceptron, GCN, GAT, and MPNN. The results show that the proposed attention-enhanced graph neural network model achieves the best or near-best performance on all three datasets. Specifically, the test set RMSE of the ESOL dataset drops to 0.52, and R2 increases to 0.86; the RMSE of the FreeSolv dataset drops to 1.08, and R2 reaches 0.83; the RMSE of the Lipophilicity dataset drops to 0.57, and R2 reaches 0.78. The ablation experiments further indicate that by introducing edge features, attention reading mechanism, and molecule descriptor fusion, the model prediction error is reduced by 6.8%, 5.4%, and 4.7% respectively. The research results show that graph neural networks can effectively capture the structure-property relationship of organic molecules, providing an effective method for rapid prediction of molecular properties and computer-aided molecular design.
A novel Dual-Attention Multimodal framework for Graphs and Sequence-based representations, so-called DAM-GS, which provides a promising solution for molecular property prediction with broad applications in drug discovery and computational molecular science.
Bay Van Nguyen, Vinh Truong, Ha Duong Thi Hong et al.· Journal of Chemical Informat...· 0 citations
The SBMR-CNN model demonstrates highly competitive accuracy, outperforming the CM, Uni-Mol+, and MPNN-2D benchmarks, while closely approaching the performance of the more computationally intensive MPNN-3D and SOAP descriptors, as well as the RF-MF model.
Abdulaziz W. Alherz, C. Tezak, Mohammed S. Alhajeri· Industrial & Engineering...· 0 citations
An Algebraic Graph Neural Network model designed to encode molecular structures into a low-dimensional graph representation while preserving critical biochemical interactions is introduced, demonstrating superior performance in binding affinity prediction compared to state-of-the-art scoring functions.
Augustine Ouru, Xi Chen, Cameron Yeagle et al.· Computational and Mathematic...· 0 citations
Results indicate that integrating heterogeneous structural cues through coarse- and fine-grained feature interaction provides an effective and scalable solution for DDI prediction.
Density functional theory (DFT) serves as a reliable tool for atomistic molecular simulations, while machine learning potentials have become powerful complements to balance accuracy and efficiency. In this work, we release OpenGEM26 (Open Generated Ensemble of Molecules, 2026), a large-scale dataset comprising 200,000 unique molecules and 4.4 million conformations composed of H, C, N, O, S and Cl with up to ten heavy atoms. All calculations are carried out at the {\omega}B97X-D/Def2-SVP and Def2-TZVP levels with dispersion corrections, and complete structural optimization trajectories and abundant non-equilibrium structures are recorded. Statistical analyses confirm that this dataset covers a broader conformational space than QM9 in terms of energy, bond lengths and bond angles. A graph neural network-based potential GPTFF-mol is trained using the new dataset, achieving an energy mean absolute error of 16 meV/molecule, which is equivalent to 0.82meV/atom, and superior force prediction performance compared with ANI-2x. Validated by butane rotation and keto-enol tautomerization tests, the model accurately describes molecular dynamical behaviors and reaction barriers at distorted geometries. This work provides a high-quality resource and robust ML potential for efficient simulations of sulfur- and chlorine-containing organic molecules.
Yifan Huang, Fankai Xie, Jiangnan Zheng et al.· 0 citations
Experiments show that PWAV generally improves over classical fingerprint descriptors within learned models and achieves competitive performance relative to established external baselines on several endpoints, positioning PWAV as a competitive and chemically transparent component for hybrid molecular property prediction, rather than as a replacement for domain-specific benchmark systems.
M. Afzal, S. Siddiqi· Physica Scripta· 0 citations