Construction and Performance Evaluation of Organic Molecule Property Prediction Model Based on Graph Neural Network

Dual-Attention Multimodal Framework for Molecular Property Prediction

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.

Molecular Property Prediction via Sparse Binary Matrix Representation and Convolutional Neural Networks

A simple and interpretable matrix-based representation is presented for predicting molecular properties, specifically individual HOMO and LUMO frontier orbital energies and their resulting energy gaps, of functionalized organic molecules using a Convolutional Neural Network (CNN). Each molecule is encoded as a sparse binary matrix (SBMR) that captures the identity and position of substituents on a fixed molecular backbone. The model was initially benchmarked across four molecular families: n-butane, i-butane, cyclobutadiene, and quinone, achieving a combined RMSE of 4.0 kcal mol–1 for gap predictions compared to DFT-computed references, with over 85% of predictions falling within ±5% error. To contextualize this performance, the model was benchmarked against six established featurization methods spanning 2D topology and 3D physics-based approaches: the Coulomb Matrix (CM), Smooth Overlap of Atomic Positions (SOAP), 2D and 3D Message-Passing Neural Networks (MPNN), Random Forest with Morgan Fingerprints (RF-MF), and Uni-Mol+. 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. This is achieved while offering distinct advantages through a dramatically smaller feature space and less stringent input data requirements. To demonstrate extensibility to complex catalytic systems, the architecture was applied to a combinatorial data set of 1,4-dihydropyridine derivatives, a class of redox mediators utilized in electrochemical and biochemical applications. For these highly functionalized heterocycles, the model successfully decoupled the energy gap into its constituent levels, predicting HOMO and LUMO energies with an RMSE of 2.8 and 2.5 kcal mol–1, respectively. The resulting framework couples high predictive accuracy with representational interpretability, offering a transparent and customizable tool for property prediction with direct applications in molecular screening, rational design, and electrocatalyst optimization.

An algebraic graph neural network model for protein-ligand binding affinity prediction

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.

Multi-GraphDDI: Multi-Feature Fusion and Interaction for Graph-Based Drug-Drug Interaction Prediction.

Graph Neural Network Force Fields (GPTFF-mol) for Organic Molecules from Optimization Trajectories (OpenGEM26)

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.

Path-weighted atom vectors and ChemBERTa fusion for predicting physicochemical properties

Molecular property prediction is central to cheminformatics and environmental chemistry, where accurate modeling of physicochemical properties supports risk assessment and molecular design. Classical descriptors and recent advances such as ChemBERTa have enabled learning chemically contextual representations directly from SMILES, while the integration of structured descriptors with transformer-based embeddings offers a promising pathway toward accurate and interpretable prediction. In this study, we introduce Path-Weighted Atom Vectors (PWAVs), a descriptor family that captures atom-level, environment-aware structural information. We evaluate PWAV both as a standalone representation and in combination with ChemBERTa embeddings through a gated fusion architecture incorporating modality dropout, FiLM conditioning, and auxiliary supervision. Experiments on six physicochemical property datasets ( log P, log S, log BCF, boiling point, melting point, and vapor pressure) show that PWAV generally improves over classical fingerprint descriptors within learned models and achieves competitive performance relative to established external baselines on several endpoints. The strongest gains are observed for boiling point, aqueous solubility, and partition coefficient prediction, where descriptor-embedding fusion yields the best results among the learned models considered. Ablation analyses demonstrate that PWAV contributes complementary structural information beyond SMILES-only ChemBERTa representations, while SHapley Additive exPlanations-based interpretability shows that predictive signal is concentrated within a compact subset of features, enabling an efficient reduced representation (PWAV-64). Nested cross-validation further confirms the robustness of PWAV within the XGBoost framework. Overall, PWAV provides a compact, interpretable, and extensible descriptor framework that integrates effectively with modern representation-learning approaches. These results position PWAV as a competitive and chemically transparent component for hybrid molecular property prediction, rather than as a replacement for domain-specific benchmark systems.