Dual modality feature fused neural network integrating binding site information for drug target affinity prediction
Abstract Accurately predicting binding affinities between drugs and targets is crucial for drug discovery but remains challenging due to the complexity of modeling interactions between small drug and large targets. This study proposes DMFF-DTA, a dual-modality neural network model integrates sequenc...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01464-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571234022326272 |
|---|---|
| author | Haohuai He Guanxing Chen Zhenchao Tang Calvin Yu-Chian Chen |
| author_facet | Haohuai He Guanxing Chen Zhenchao Tang Calvin Yu-Chian Chen |
| author_sort | Haohuai He |
| collection | DOAJ |
| description | Abstract Accurately predicting binding affinities between drugs and targets is crucial for drug discovery but remains challenging due to the complexity of modeling interactions between small drug and large targets. This study proposes DMFF-DTA, a dual-modality neural network model integrates sequence and graph structure information from drugs and proteins for drug-target affinity prediction. The model introduces a binding site-focused graph construction approach to extract binding information, enabling more balanced and efficient modeling of drug-target interactions. Comprehensive experiments demonstrate DMFF-DTA outperforms state-of-the-art methods with significant improvements. The model exhibits excellent generalization capabilities on completely unseen drugs and targets, achieving an improvement of over 8% compared to existing methods. Model interpretability analysis validates the biological relevance of the model. A case study in pancreatic cancer drug repurposing demonstrates its practical utility. This work provides an interpretable, robust approach to integrate multi-view drug and protein features for advancing computational drug discovery. |
| format | Article |
| id | doaj-art-54b246629c6b469288210b9b187fab35 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-54b246629c6b469288210b9b187fab352025-02-02T12:43:45ZengNature Portfolionpj Digital Medicine2398-63522025-01-018111410.1038/s41746-025-01464-xDual modality feature fused neural network integrating binding site information for drug target affinity predictionHaohuai He0Guanxing Chen1Zhenchao Tang2Calvin Yu-Chian Chen3State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate SchoolState Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate SchoolState Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate SchoolState Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate SchoolAbstract Accurately predicting binding affinities between drugs and targets is crucial for drug discovery but remains challenging due to the complexity of modeling interactions between small drug and large targets. This study proposes DMFF-DTA, a dual-modality neural network model integrates sequence and graph structure information from drugs and proteins for drug-target affinity prediction. The model introduces a binding site-focused graph construction approach to extract binding information, enabling more balanced and efficient modeling of drug-target interactions. Comprehensive experiments demonstrate DMFF-DTA outperforms state-of-the-art methods with significant improvements. The model exhibits excellent generalization capabilities on completely unseen drugs and targets, achieving an improvement of over 8% compared to existing methods. Model interpretability analysis validates the biological relevance of the model. A case study in pancreatic cancer drug repurposing demonstrates its practical utility. This work provides an interpretable, robust approach to integrate multi-view drug and protein features for advancing computational drug discovery.https://doi.org/10.1038/s41746-025-01464-x |
| spellingShingle | Haohuai He Guanxing Chen Zhenchao Tang Calvin Yu-Chian Chen Dual modality feature fused neural network integrating binding site information for drug target affinity prediction npj Digital Medicine |
| title | Dual modality feature fused neural network integrating binding site information for drug target affinity prediction |
| title_full | Dual modality feature fused neural network integrating binding site information for drug target affinity prediction |
| title_fullStr | Dual modality feature fused neural network integrating binding site information for drug target affinity prediction |
| title_full_unstemmed | Dual modality feature fused neural network integrating binding site information for drug target affinity prediction |
| title_short | Dual modality feature fused neural network integrating binding site information for drug target affinity prediction |
| title_sort | dual modality feature fused neural network integrating binding site information for drug target affinity prediction |
| url | https://doi.org/10.1038/s41746-025-01464-x |
| work_keys_str_mv | AT haohuaihe dualmodalityfeaturefusedneuralnetworkintegratingbindingsiteinformationfordrugtargetaffinityprediction AT guanxingchen dualmodalityfeaturefusedneuralnetworkintegratingbindingsiteinformationfordrugtargetaffinityprediction AT zhenchaotang dualmodalityfeaturefusedneuralnetworkintegratingbindingsiteinformationfordrugtargetaffinityprediction AT calvinyuchianchen dualmodalityfeaturefusedneuralnetworkintegratingbindingsiteinformationfordrugtargetaffinityprediction |