Biological 3D Structural Databases

Gaber, Yasser; Rashad, Boshra; Fathy, Eman

doi:10.1007/978-3-030-02634-9_4

Cited by 2 publications

(4 citation statements)

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“…To be consistent with DeepDTA ( Öztürk et al , 2018 ), we evaluate our model on two benchmarks, Davis ( Davis et al , 2011 ) and KIBA ( Tang et al , 2014 ). Additionally, we convert the data of a new 3D dataset, sc-PDB ( Gaber et al , 2019 ), into the sequence format to evaluate the performance of the proposed method for BR prediction. The statistics for the three datasets are shown in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

“…However, MTR-DTA still performs better than the others. Besides, we supply a new dataset, sc-PDB ( Gaber et al , 2019 ), that includes actual BR information, to evaluate these models in predicting unseen drug–target BRs with the scale S =15. The experimental results also confirm that the performance of a BR prediction model with supervised learning is more reliable.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, we take the probability of protein binding sites falling in the prediction region as the measurement to evaluate the prediction accuracy. Moreover, we use a new dataset, sc-PDB ( Gaber et al , 2019 ), to evaluate the performance of the proposed method in BR prediction of unseen samples. As compared with the existing methods ( Abbasi et al , 2020 ; Chen et al , 2020 ; Hua et al , 2022 ; Tsubaki et al , 2019 ), the proposed method performs better in accuracy.…”

Section: Introductionmentioning

confidence: 99%

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MFR-DTA: a multi-functional and robust model for predicting drug–target binding affinity and region

et al. 2023

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Motivation Recently, deep learning has become the mainstream methodology for Drug-Target binding Affinity (DTA) prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ignore the individual information of sequence elements, resulting in poor sequence feature representations. On the other hand, without prior biological knowledge, the prediction of drug-target binding regions based on attention weights of a deep neural network could be difficult to verify, which may bring adverse interference to biological researchers. Results We propose a novel Multi-Functional and Robust Drug-Target binding Affinity prediction (MFR-DTA) method to address the above issues. Specifically, we design a new biological sequence feature extraction block, namely BioMLP, that assists the model in extracting individual features of sequence elements. Then, we propose a new Elem-feature fusion block to refine the extracted features. After that, we construct a Mix-Decoder block that extracts drug-target interaction information and predicts their binding regions simultaneously. Last, we evaluate MFR-DTA on two benchmarks consistently with the existing methods and propose a new dataset, sc-PDB, to better measure the accuracy of binding region prediction. We also visualise some samples to demonstrate the locations of their binding sites and the predicted multi-scale interaction regions. The proposed method achieves excellent performance on these datasets, demonstrating its merits and superiority over the state-of-the-art methods. Availability and implementation https://github.com/JU-HuaY/MFR Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MFR-DTA: a multi-functional and robust model for predicting drug–target binding affinity and region

et al. 2023

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“…To verify the effectiveness of our model and to facilitate comparative analysis with other models, we use two famous benchmark datasets, Davis [34] and KIBA [6]. Furthermore, to evaluate the performance of our model in predicting drug-target BR, we use the sc-PDB dataset [35]. The Davis dataset contains 30056 interactions between 68 drugs and 442 targets.…”

Section: Methodsmentioning

confidence: 99%

MMD-DTA: A multi-modal deep learning framework for drug-target binding affinity and binding region prediction

Liu,

Zhang,

Wei

et al. 2023

Preprint

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The prediction of drug-target affinity (DTA) plays an important role in the development of drugs and the discovery of potential drug targets. In recent years, computer-assisted DTA prediction has become an important method in this field. In this work, we propose a multi-modal deep learning framework for drug-target binding affinity and binding region prediction, namely MMD-DTA. The model can predict DTA while unsupervised learning of drug-target binding regions. The experimental results show that MMD-DTA performs better than the existing models on the main evaluation metrics. In addition, external validation results show that MMD-DTA improves the generalization ability of the model by integrating sequence information and structural information of drugs and targets, and the model trained on the benchmark dataset can be well generalized to independent virtual screening tasks. Visualization of drug-target binding region prediction shows the powerful interpretability of MMD-DTA, which has important implications for exploring the functional regions of drug molecules acting on proteins.

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Biological 3D Structural Databases

Cited by 2 publications

References 50 publications

MFR-DTA: a multi-functional and robust model for predicting drug–target binding affinity and region

MFR-DTA: a multi-functional and robust model for predicting drug–target binding affinity and region

MMD-DTA: A multi-modal deep learning framework for drug-target binding affinity and binding region prediction

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