Graph Neural Network-Based Drug-Target Interaction Prediction with Multi-Scale Molecular Fingerprints
Abstract
Predicting drug-target interactions (DTIs) is fundamental for drug discovery but remains challenging due to the vast chemical and protein space. We present MolGraphDTI, a graph neural network framework that integrates multi-scale molecular representations — atomic-level graphs, pharmacophore-level substructure graphs, and protein contact maps — through a hierarchical attention mechanism. On the BindingDB benchmark, MolGraphDTI achieves an AUC of 0.967 and an AUPR of 0.952, outperforming state-of-the-art methods by 3.2%. Ablation studies confirm that each representation scale contributes complementary information. Applied to SARS-CoV-2 main protease (Mpro), the model identifies 12 novel inhibitor candidates, 4 of which show IC₅₀ < 1 μM in enzymatic assays, validating the practical utility of the approach.
Keywords: graph neural networks, drug discovery, drug-target interaction, molecular representation, deep learning
1. Introduction
Drug discovery is a lengthy and expensive process, with an average timeline of 10-15 years and costs exceeding $2.6 billion per approved drug. Computational prediction of drug-target interactions (DTIs) can significantly accelerate the early stages of drug discovery by prioritizing candidate molecules for experimental validation and reducing the number of costly wet-lab experiments.
2. Proposed Framework
MolGraphDTI processes drug molecules and protein targets through three parallel graph encoders, each operating at a different representation scale. The atomic-level encoder uses a message-passing neural network (MPNN) on the molecular graph where nodes represent atoms and edges represent chemical bonds. The pharmacophore-level encoder operates on a coarser graph of functional group substructures. The protein encoder uses a graph constructed from the amino acid contact map derived from AlphaFold2 predicted structures.
3. Results
We evaluated MolGraphDTI on three widely-used DTI benchmark datasets: BindingDB (39,747 positive and 31,218 negative pairs), DAVIS (30,056 kinase-inhibitor pairs), and KIBA (118,254 kinase-inhibitor pairs). Five-fold cross-validation was used with a temporal split to prevent data leakage from future publications.
Table 1. Ablation study on BindingDB: contribution of each molecular representation scale
| Model Variant | AUC | AUPR | F1 | Precision |
|---|---|---|---|---|
| Atom-level only | 0.938 | 0.921 | 0.882 | 0.895 |
| Pharmacophore only | 0.915 | 0.898 | 0.861 | 0.873 |
| Atom + Pharmacophore | 0.952 | 0.940 | 0.905 | 0.918 |
| Full (all scales) | 0.967 | 0.952 | 0.923 | 0.935 |
4. Conclusions
MolGraphDTI demonstrates that integrating multi-scale molecular representations through hierarchical attention provides significant improvements in DTI prediction accuracy. The successful identification of novel SARS-CoV-2 Mpro inhibitors validates the translational potential of the framework. Future work will extend the approach to protein-protein interaction prediction and multi-target drug design.
References
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0).