An interpretable geometric graph neural network for enhancing the generalizability of drug–target interaction prediction Article Swipe
An Xiong
,
Zhenjie Luo
,
Ying Xia
,
Quan Zou
,
Zilong Zhang
,
Tao Wang
,
Lesong Wei
,
Feifei Cui
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s12915-025-02456-9
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s12915-025-02456-9
Comprehensive benchmarking across in-domain and cross-domain DTI prediction tasks demonstrates that GPS-DTI outperforms existing methods, underscoring its strong generalization capability. Notably, the model achieves state-of-the-art performance on drug-target affinity (DTA) tasks and shows robust adaptability when evaluated on an independent Coronavirus Disease 2019 (COVID-19)-related test set. Furthermore, visualization of cross-attention maps offers interpretable insights into key molecular interactions, highlighting the potential of GPS-DTI in real-world drug discovery applications.
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An interpretable geometric graph neural network for enhancing the generalizability of drug–target interaction predictionWork title
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2025-11-26Full publication date if available
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An Xiong, Zhenjie Luo, Ying Xia, Quan Zou, Zilong Zhang, Tao Wang, Lesong Wei, Feifei CuiList of authors in order
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https://doi.org/10.1186/s12915-025-02456-9Publisher landing page
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| referenced_works | https://openalex.org/W2985881898, https://openalex.org/W2785947426, https://openalex.org/W2153838454, https://openalex.org/W4211219865, https://openalex.org/W3000043291, https://openalex.org/W4387770440, https://openalex.org/W2592742128, https://openalex.org/W4386715538, https://openalex.org/W3208753658, https://openalex.org/W2100672820, https://openalex.org/W2078653892, https://openalex.org/W1986240377, https://openalex.org/W4391607488, https://openalex.org/W1544009106, https://openalex.org/W2911964244, https://openalex.org/W2119821739, https://openalex.org/W4403782367, https://openalex.org/W4366089579, https://openalex.org/W2148512505, https://openalex.org/W2104950117, https://openalex.org/W4318981550, https://openalex.org/W3028589594, https://openalex.org/W4307178567, https://openalex.org/W2860192827, https://openalex.org/W4390955747, https://openalex.org/W3096561213, https://openalex.org/W3032123378, https://openalex.org/W4399940476, https://openalex.org/W4285041843, https://openalex.org/W2781821160, https://openalex.org/W4393092671, https://openalex.org/W4327550249, https://openalex.org/W3018980093, https://openalex.org/W3206585172, https://openalex.org/W4388595306, https://openalex.org/W4386758612, https://openalex.org/W2898364362, https://openalex.org/W2162027701, https://openalex.org/W2784573272, https://openalex.org/W2096864392, https://openalex.org/W2170146596, https://openalex.org/W3157427301, https://openalex.org/W4200139236, https://openalex.org/W4401171136 |
| referenced_works_count | 44 |
| abstract_inverted_index.an | 38 |
| abstract_inverted_index.in | 63 |
| abstract_inverted_index.of | 48, 61 |
| abstract_inverted_index.on | 26, 37 |
| abstract_inverted_index.DTI | 6 |
| abstract_inverted_index.and | 4, 31 |
| abstract_inverted_index.its | 16 |
| abstract_inverted_index.key | 55 |
| abstract_inverted_index.the | 21, 59 |
| abstract_inverted_index.2019 | 42 |
| abstract_inverted_index.drug | 65 |
| abstract_inverted_index.into | 54 |
| abstract_inverted_index.maps | 50 |
| abstract_inverted_index.set. | 45 |
| abstract_inverted_index.test | 44 |
| abstract_inverted_index.that | 10 |
| abstract_inverted_index.when | 35 |
| abstract_inverted_index.(DTA) | 29 |
| abstract_inverted_index.model | 22 |
| abstract_inverted_index.shows | 32 |
| abstract_inverted_index.tasks | 8, 30 |
| abstract_inverted_index.across | 2 |
| abstract_inverted_index.offers | 51 |
| abstract_inverted_index.robust | 33 |
| abstract_inverted_index.strong | 17 |
| abstract_inverted_index.Disease | 41 |
| abstract_inverted_index.GPS-DTI | 11, 62 |
| abstract_inverted_index.Notably, | 20 |
| abstract_inverted_index.achieves | 23 |
| abstract_inverted_index.affinity | 28 |
| abstract_inverted_index.existing | 13 |
| abstract_inverted_index.insights | 53 |
| abstract_inverted_index.methods, | 14 |
| abstract_inverted_index.discovery | 66 |
| abstract_inverted_index.evaluated | 36 |
| abstract_inverted_index.in-domain | 3 |
| abstract_inverted_index.molecular | 56 |
| abstract_inverted_index.potential | 60 |
| abstract_inverted_index.prediction | 7 |
| abstract_inverted_index.real-world | 64 |
| abstract_inverted_index.Coronavirus | 40 |
| abstract_inverted_index.capability. | 19 |
| abstract_inverted_index.drug-target | 27 |
| abstract_inverted_index.independent | 39 |
| abstract_inverted_index.outperforms | 12 |
| abstract_inverted_index.performance | 25 |
| abstract_inverted_index.Furthermore, | 46 |
| abstract_inverted_index.adaptability | 34 |
| abstract_inverted_index.benchmarking | 1 |
| abstract_inverted_index.cross-domain | 5 |
| abstract_inverted_index.demonstrates | 9 |
| abstract_inverted_index.highlighting | 58 |
| abstract_inverted_index.underscoring | 15 |
| abstract_inverted_index.Comprehensive | 0 |
| abstract_inverted_index.applications. | 67 |
| abstract_inverted_index.interactions, | 57 |
| abstract_inverted_index.interpretable | 52 |
| abstract_inverted_index.visualization | 47 |
| abstract_inverted_index.generalization | 18 |
| abstract_inverted_index.cross-attention | 49 |
| abstract_inverted_index.state-of-the-art | 24 |
| abstract_inverted_index.(COVID-19)-related | 43 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5110758704, https://openalex.org/A5100453730 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I150229711, https://openalex.org/I4210123686, https://openalex.org/I899859998 |
| citation_normalized_percentile |