A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914030268973056 |
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| author | Berry, Katherine Cheng, Liang |
| author_facet | Berry, Katherine Cheng, Liang |
| contents | Graph Neural Networks (GNNs) have gained traction in the complex domain of drug discovery because of their ability to process graph-structured data such as drug molecule models. This approach has resulted in a myriad of methods and models in published literature across several categories of drug discovery research. This paper covers the research categories comprehensively with recent papers, namely molecular property prediction, including drug-target binding affinity prediction, drug-drug interaction study, microbiome interaction prediction, drug repositioning, retrosynthesis, and new drug design, and provides guidance for future work on GNNs for drug discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges Berry, Katherine Cheng, Liang Machine Learning I.2; I.2.1; J.3 Graph Neural Networks (GNNs) have gained traction in the complex domain of drug discovery because of their ability to process graph-structured data such as drug molecule models. This approach has resulted in a myriad of methods and models in published literature across several categories of drug discovery research. This paper covers the research categories comprehensively with recent papers, namely molecular property prediction, including drug-target binding affinity prediction, drug-drug interaction study, microbiome interaction prediction, drug repositioning, retrosynthesis, and new drug design, and provides guidance for future work on GNNs for drug discovery. |
| title | A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges |
| topic | Machine Learning I.2; I.2.1; J.3 |
| url | https://arxiv.org/abs/2509.07887 |