Drug–target affinity prediction method based on multi-scale information interaction and graph optimization

Zhu, Zhiqin; Yao, Zheng; Zheng, Xin; Qi, Guanqiu; Li, Yuanyuan; Mazur, Neal; Gao, Xinbo; Gong, Yifei; Cong, Baisen

doi:10.1016/j.compbiomed.2023.107621

Cited by 17 publications

(3 citation statements)

References 40 publications

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“…CNN was directly applied to the target sequences for learning high-level features. TDGraphDTA ( Zhu et al, 2023c ) introduced the transformer and diffusion to predict drug-target interactions using multi-scale information interaction and graph optimization. Hybrid-based methods combine the structural features of drugs with sequence-based approaches, enriching the features of drugs.…”

Section: Introductionmentioning

confidence: 99%

“…Hybrid-based methods (Karimi et al, 2021;Wang et al, 2021b;Zhang et al, 2021;Cheng et al, 2022;Li et al, 2022a;Lin et al, 2022a;Tian et al, 2022;Yang et al, 2022;Jiang et al, 2023;Pan et al, 2023;Wang et al, 2023a;Wang and Li, 2023;Xia et al, 2023;Yang et al, 2023;Zeng et al, 2023;Zhang et al, 2023b;Zhang et al, 2023a;Zhu et al, 2023a;Zhu et al, 2023c;Nguyen et al, 2022.) leverage deep learning models to extract sequence features from drug SMILES and target sequences, as well as the structural features from twodimensional molecular topology graphs and three-dimensional structures of drug small molecules. These methods focus on integrating the structural features of drugs into sequence-based approaches.…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning

Zeng,

Li,

et al. 2024

Front. Pharmacol.

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Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning

Zeng,

Li,

et al. 2024

Front. Pharmacol.

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“…Additionally, hybrid-based methods were also introduced to capture the integration of the drug structures into sequence-based techniques. Recently, Zhu et al, proposed a transformer-based diffusion technique to predict the binding affinity score through the use of multiscale feature interaction and graph optimization methodology to improve the model’s performance and interpretability . A similar study was proposed where transformers were used for target featurization and autoencoders for SMILES featurization, employing adaptive attention pooling to enhance the performance of DTI …”

Section: Introductionmentioning

confidence: 99%

MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction

Ranjan,

Bess,

Alvin

et al. 2024

J. Chem. Inf. Model.

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Drug-target affinity (DTA) prediction is an important task in the early stages of drug discovery. Traditional biological approaches are time-consuming, effort-consuming, and resource-consuming due to the large size of genomic and chemical spaces. Computational approaches using machine learning have emerged to narrow down the drug candidate search space. However, most of these prediction models focus on single feature encoding of drugs and targets, ignoring the importance of integrating different dimensions of these features. We propose a deep learning-based approach called Multi-Dimensional Fusion for Drug Target Affinity Prediction (MDF-DTA) incorporating different dimensional features. Our model fuses 1D, 2D, and 3D representations obtained from different pretrained models for both drugs and targets. We evaluated MDF-DTA on two standard benchmark data sets: DAVIS and KIBA. Experimental results show that MDF-DTA outperforms many state-of-the-art techniques in the DTA task across both data sets. Through ablation studies and performance evaluation metrics, we evaluate the importance of individual representations and the impact of each representation on MDF-DTA.

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SSR-DTA: Substructure-aware multi-layer graph neural networks for drug–target binding affinity prediction

Liu,

Xia,

Gong

et al. 2024

Artificial Intelligence in Medicine

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Drug–target affinity prediction method based on multi-scale information interaction and graph optimization

Cited by 17 publications

References 40 publications

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning

MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction

SSR-DTA: Substructure-aware multi-layer graph neural networks for drug–target binding affinity prediction

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