GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information
Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affi...
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MDPI AG
2025-03-01
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| Series: | Biomolecules |
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| author | Kusal Debnath Pratip Rana Preetam Ghosh |
| author_facet | Kusal Debnath Pratip Rana Preetam Ghosh |
| author_sort | Kusal Debnath |
| collection | DOAJ |
| description | Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance the prediction capability of the models. To fill this gap, we propose GramSeq-DTA, which integrates chemical perturbation information with the structural information of drugs and targets. We applied a Grammar Variational Autoencoder (GVAE) for drug feature extraction and utilized two different approaches for protein feature extraction as follows: a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). The chemical perturbation data are obtained from the L1000 project, which provides information on the up-regulation and down-regulation of genes caused by selected drugs. This chemical perturbation information is processed, and a compact dataset is prepared, serving as the functional feature set of the drugs. By integrating the drug, gene, and target features in the model, our approach outperforms the current state-of-the-art DTA prediction models when validated on widely used DTA datasets (BindingDB, Davis, and KIBA). This work provides a novel and practical approach to DTA prediction by merging the structural and functional aspects of biological entities, and it encourages further research in multi-modal DTA prediction. |
| format | Article |
| id | doaj-art-69be715cf3a341f6a294b71bb07d161e |
| institution | OA Journals |
| issn | 2218-273X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Biomolecules |
| spelling | doaj-art-69be715cf3a341f6a294b71bb07d161e2025-08-20T02:11:22ZengMDPI AGBiomolecules2218-273X2025-03-0115340510.3390/biom15030405GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression InformationKusal Debnath0Pratip Rana1Preetam Ghosh2Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USADepartment of Computer Science, Old Dominion University, Norfolk, VA 23529, USADepartment of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USADrug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance the prediction capability of the models. To fill this gap, we propose GramSeq-DTA, which integrates chemical perturbation information with the structural information of drugs and targets. We applied a Grammar Variational Autoencoder (GVAE) for drug feature extraction and utilized two different approaches for protein feature extraction as follows: a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). The chemical perturbation data are obtained from the L1000 project, which provides information on the up-regulation and down-regulation of genes caused by selected drugs. This chemical perturbation information is processed, and a compact dataset is prepared, serving as the functional feature set of the drugs. By integrating the drug, gene, and target features in the model, our approach outperforms the current state-of-the-art DTA prediction models when validated on widely used DTA datasets (BindingDB, Davis, and KIBA). This work provides a novel and practical approach to DTA prediction by merging the structural and functional aspects of biological entities, and it encourages further research in multi-modal DTA prediction.https://www.mdpi.com/2218-273X/15/3/405drug–target affinitydeep learninggrammar-based encodingchemical perturbationmulti-modal |
| spellingShingle | Kusal Debnath Pratip Rana Preetam Ghosh GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information Biomolecules drug–target affinity deep learning grammar-based encoding chemical perturbation multi-modal |
| title | GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information |
| title_full | GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information |
| title_fullStr | GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information |
| title_full_unstemmed | GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information |
| title_short | GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information |
| title_sort | gramseq dta a grammar based drug target affinity prediction approach fusing gene expression information |
| topic | drug–target affinity deep learning grammar-based encoding chemical perturbation multi-modal |
| url | https://www.mdpi.com/2218-273X/15/3/405 |
| work_keys_str_mv | AT kusaldebnath gramseqdtaagrammarbaseddrugtargetaffinitypredictionapproachfusinggeneexpressioninformation AT pratiprana gramseqdtaagrammarbaseddrugtargetaffinitypredictionapproachfusinggeneexpressioninformation AT preetamghosh gramseqdtaagrammarbaseddrugtargetaffinitypredictionapproachfusinggeneexpressioninformation |