MDM-DTA: Message Passing Neural Network with molecular descriptors and Mixture of Experts for drug–target affinity prediction
Open accessAbstract
Background and objective Drug-target affinity (DTA) prediction is a pivotal task in computational drug discovery, enabling the estimation of binding affinities between small molecules and their target proteins. This process is essential for reducing the costs, development time, and risks inherent in traditional drug development pipelines. Current DTA prediction models primarily rely on separate extraction and concatenation of drug and protein features. However, these models often fail to account for the complex semantic relationships within protein sequences, which limits their ability to accurately predict affinity. Methods In response to these challenges, we propose MDM-DTA, a novel framework leveraging a Mixture of Experts (MoE) strategy to integrate diverse molecular and protein representations. For drug representation, MDM-DTA utilizes molecular graphs, which are processed via Message Passing Neural Networks (MPNNs), alongside molecular descriptors that are passed through a three-layer convolutional neural network (CNN). Protein features are extracted using a deep convolutional network enhanced with Squeeze-and-Excitation (SE) mechanisms to capture inter-channel dependencies. Furthermore, protein sequence semantics are encoded through pre-trained embeddings from a knowledge-guided Bidirectional Encoder Representations from Transformers (BERT) model and the Evolutionary Scale Modeling 2 (ESM2) model, enabling the model to capture contextual relationships within protein sequences. Results Extensive experiments on three benchmark datasets demonstrate that MDM-DTA consistently outperforms state-of-the-art models of similar complexity in terms of predictive accuracy. The incorporation of both structural and semantic features significantly enhances the model's ability to predict drug-target binding affinities, highlighting the importance of a multi-modal representation approach. Conclusions The proposed MDM-DTA framework effectively integrates both molecular and semantic protein representations, providing superior performance in DTA prediction tasks. The results underscore the potential of MDM-DTA to improve the accuracy of computational drug discovery models, facilitating the identification of novel drug candidates and advancing the field of in silico drug development.
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- Authors
- Dai, Y., Tan, X., Wang, H., Ma, G., Xiong, Y., & Qiu, X.
- Affiliations
- Shanghai University of Engineering Science (CN)
- Corresponding author
- Qiu, Xihe
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2026-02
- Volume
- 274
- Pages
- 109163
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W4416690888
- Cited by
- 0
- References
- 44
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (82303695)
- Shanghai Municipal Natural Science Foundation (23ZR1425400)
- Zhongshan Hospital
- Shanghai Municipal Health Commission
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- drug-target affinity prediction, message passing neural networks, mixture of experts, molecular descriptors, molecular graphs, protein sequence embeddings, BERT, ESM-2, multimodal representation, drug binding affinity, computational drug discovery
- MeSH terms
- Algorithms, Humans, Protein Binding, Proteins, Neural Networks, Computer, Computational Biology, Drug Discovery
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