Add-GNN: A Dual-Representation Fusion Molecular Property Prediction Based on Graph Neural Networks with Additive Attention
Abstract
:1. Introduction
- A molecular property prediction model is proposed, and molecular graphs and molecular descriptors are fused to represent molecules.
- In the message-passing stage, an additive attention mechanism is designed, which can ideally fuse the features of neighboring nodes and edges.
- The L2-norm is applied to visualize the importance of each atom in a molecular graph.
2. Related Work
2.1. Descriptor-Based Prediction of Molecular Properties
2.2. SMILES-Based Prediction of Molecular Properties
2.3. Graph-Based Prediction of Molecular Properties
3. Materials and Methodology
3.1. Problem Statement
3.2. Add-GNN for Molecular Graph Encoding
3.2.1. Additive Attention
3.2.2. Message-Passing Phase and Readout Phase
3.3. Atomic and Bond Features
3.4. Molecular Descriptors
3.5. Loss Function
3.6. Overview of Add-GNN Network
Algorithm 1: The pseudo-code of the proposed Add-GNN |
4. Experiments
4.1. Benchmark Datasets
4.2. Hyperparameter Optimization
4.3. Performance of the Add-GNN Network Architecture
4.4. Ablation Experiment
4.5. Interpretation of Add-GNN
5. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Atomic Feature | Size | Description |
---|---|---|
Atom symbol | 10 | [C, N, O, F, P, S, Cl, Br, I, other] (one-hot) |
Degree | 7 | number of covalent bonds [0, 1, 2, 3, 4, 5, other] (one-hot) |
Formal charge | 1 | number of electrical charges |
Radical electrons | 1 | number of radical electrons (integer) |
Hybridization | 6 | [, , , , , other] (one-hot) |
Aromaticity | 1 | whether the atom is part of an aromatic system [0/1] (one-hot) |
Hydrogens | 5 | number of connected hydrogens [0, 1, 2, 3, 4] (one-hot) |
Chirality | 1 | whether the atom is a chiral center [0/1] (one-hot) |
Chirality type | 2 | [R, S] (one-hot) |
Bond Feature | Size | Description |
---|---|---|
Bond type | 4 | [single, double, triple, aromatic] (one-hot) |
Conjugation | 1 | whether the bond is conjugated [0/1] (one-hot) |
In ring | 1 | whether the bond is in ring [0/1] (one-hot) |
Stereo | 4 | [StereoNone, StereoAny, StereoZ, StereoE] (one-hot) |
Datasets | Type | Description | Compounds |
---|---|---|---|
F (20%) | Classification | ADMET datasets related to drug discovery | 992 |
FDAMDD | Classification | ADMET datasets related to drug discovery | 1197 |
SkinSen | Classification | ADMET datasets related to drug discovery | 405 |
Bcap37 | Classification | Cell phenotype screening | 275 |
BT-474 | Classification | Cell phenotype screening | 811 |
MDA-MB-361 | Classification | Cell phenotype screening | 367 |
BACE | Classification | Inhibitors of human -secretase 1 | 1513 |
ESOL | Regression | Water solubility | 1128 |
FreeSolv | Regression | Hydration free energy of small molecules | 642 |
PDBbind-C | Regression | Binding affinities for bio-molecular complexes | 168 |
Hyper-Parameters | Description | Range |
---|---|---|
nheads | The number of heads | 2, 4 |
epoch | Training epoch | 200 |
dropout | Dropout rate | [0, 0.5] |
learning rate | Learning rate of the model | 10E-[2.5, 3.5] |
weight decay (L2) | Weight decay factor | 10E-[4, 5] |
layers | Number of Add-GNN encoder layer | 2, 3 |
hidden_channels | Hidden size of embedding layer | 64, 128 |
batch size | Batch size during training | 64, 128 |
GCN | GAT | GIN | MGNN | TrimNet | Add-GNN (Proposed) | |
---|---|---|---|---|---|---|
F (20%) | 0.734 +/− 0.073 | 0.746 +/− 0.028 | 0.757 +/− 0.045 | 0.698 +/− 0.090 | 0.727 +/− 0.040 | 0.746 +/− 0.064 |
rank (ROC-AUC) | 4 | 2 | 1 | 6 | 5 | 3 |
FDAMDD | 0.807 +/− 0.023 | 0.798 +/− 0.040 | 0.795 +/− 0.035 | 0.808 +/− 0.036 | 0.806 +/− 0.024 | 0.808 +/− 0.044 |
rank (ROC-AUC) | 3 | 5 | 6 | 1 | 4 | 2 |
SkinSen | 0.753 +/− 0.046 | 0.715 +/− 0.092 | 0.695 +/− 0.048 | 0.694 +/− 0.043 | 0.738 +/− 0.077 | 0.737 +/− 0.049 |
rank (ROC-AUC) | 1 | 4 | 5 | 6 | 2 | 3 |
Bacp37 | 0.790 +/− 0.034 | 0.754 +/− 0.060 | 0.782 +/− 0.071 | 0.776 +/− 0.065 | 0.776 +/− 0.047 | 0.780 +/− 0.090 |
rank (ROC-AUC) | 1 | 6 | 2 | 5 | 4 | 3 |
BT474 | 0.851 +/− 0.034 | 0.830 +/− 0.045 | 0.823 +/− 0.0331 | 0.825 +/− 0.065 | 0.845 +/− 0.045 | 0.841 +/− 0.043 |
rank (ROC-AUC) | 1 | 4 | 6 | 5 | 2 | 3 |
MDA-MB-361 | 0.913 +/− 0.037 | 0.872+/− 0.066 | 0.887 +/− 0.053 | 0.919 +/− 0.048 | 0.915 +/− 0.021 | 0.885 +/− 0.066 |
rank (ROC-AUC) | 3 | 6 | 4 | 1 | 2 | 5 |
BACE | 0.869 +/− 0.022 | 0.859 +/− 0.023 | 0.863 +/− 0.019 | 0.846 +/− 0.036 | 0.859 +/− 0.019 | 0.878 +/− 0.012 |
rank (ROC-AUC) | 2 | 5 | 3 | 6 | 4 | 1 |
ESOL | 0.800 +/− 0.027 | 0.700 +/− 0.055 | 0.725 +/− 0.054 | 1.056 +/− 0.115 | 0.716 +/− 0.055 | 0.633 +/− 0.047 |
rank (RMSE) | 5 | 2 | 4 | 6 | 3 | 1 |
FreeSolv | 1.153 +/− 0.303 | 0.967 +/− 0.142 | 1.079 +/− 0.116 | 2.143 +/− 0.252 | 1.208 +/− 0.308 | 1.072 +/− 0.212 |
rank (RMSE) | 4 | 1 | 3 | 6 | 5 | 2 |
PDBbind-C | 1.988 +/− 0.155 | 2.056 +/− 0.273 | 2.250 +/− 0.284 | 2.071 +/− 0.250 | 2.001 +/− 0.176 | 1.771 +/− 0.176 |
rank (RMSE) | 2 | 4 | 6 | 5 | 3 | 1 |
Total rank | 26 | 39 | 40 | 47 | 34 | 24 |
Final rank | 2 | 4 | 5 | 6 | 3 | 1 |
Datasets | Split Type | Add-GNN (Without Descriptor) | Add-GNN (With Descriptor) |
---|---|---|---|
ESOL | Random | 0.667 +/− 0.040 | 0.633 +/− 0.047 |
FreeSolv | Random | 1.194 +/− 0.254 | 1.072 +/− 0.212 |
PDBbind-C | Random | 1.863+/− 0.189 | 1.771 +/− 0.176 |
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Zhou, R.; Zhang, Y.; He, K.; Liu, H. Add-GNN: A Dual-Representation Fusion Molecular Property Prediction Based on Graph Neural Networks with Additive Attention. Symmetry 2025, 17, 873. https://doi.org/10.3390/sym17060873
Zhou R, Zhang Y, He K, Liu H. Add-GNN: A Dual-Representation Fusion Molecular Property Prediction Based on Graph Neural Networks with Additive Attention. Symmetry. 2025; 17(6):873. https://doi.org/10.3390/sym17060873
Chicago/Turabian StyleZhou, Ronghe, Yong Zhang, Kai He, and Hao Liu. 2025. "Add-GNN: A Dual-Representation Fusion Molecular Property Prediction Based on Graph Neural Networks with Additive Attention" Symmetry 17, no. 6: 873. https://doi.org/10.3390/sym17060873
APA StyleZhou, R., Zhang, Y., He, K., & Liu, H. (2025). Add-GNN: A Dual-Representation Fusion Molecular Property Prediction Based on Graph Neural Networks with Additive Attention. Symmetry, 17(6), 873. https://doi.org/10.3390/sym17060873