Scholarlink

Motivation <p>Predicting drug–target binding affinity (DTA) is essential for identifying potential therapeutic candidates in drug discovery. However, most existing models rely heavily on static protein structures, often overlooking the dynamic nature of proteins, which is crucial for capturing conformational flexibility that will be beneficial for protein binding interactions.</p> Methods <p>We introduce DynamicDTA, an innovative deep learning framework that incorporates static and dynamic protein features to enhance DTA prediction. The proposed DynamicDTA takes three types of inputs, including drug sequence, protein sequence, and dynamic descriptors. A molecular graph representation of the drug sequence is generated and subsequently processed through graph convolutional network, while the protein sequence is encoded using dilated convolutions. Dynamic descriptors, such as root mean square fluctuation, are processed through a multi-layer perceptron. These embedding features are fused with static protein features using cross-attention, and a tensor fusion network integrates all three modalities for DTA prediction.</p> Results <p>Extensive experiments on three datasets demonstrate that DynamicDTA achieves by at least 3.4% improvement in <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(e_{\text{RMSE}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>e</mi> <mtext>RMSE</mtext> </msub> </math></EquationSource> </InlineEquation> score with comparison to seven state-of-the-art baseline methods. Additionally, predicting novel drugs for <i>Human Immunodeficiency Virus Type 1</i> and visualizing the docking complexes further demonstrates the reliability and biological relevance of DynamicDTA.</p> Availability and implementation <p>The source code is publicly available and can be accessed at <a href="https://github.com/shmily-ld/DynamicDTA">https://github.com/shmily-ld/DynamicDTA</a>.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation

  • 期刊论文

  • 发表日期：

    2025年6月6日

查看全文
  • Dan Luo,
  • Jinyu Zhou,
  • Le Xu,
  • Sisi Yuan,
  • Xuan Lin

摘要

Motivation

Predicting drug–target binding affinity (DTA) is essential for identifying potential therapeutic candidates in drug discovery. However, most existing models rely heavily on static protein structures, often overlooking the dynamic nature of proteins, which is crucial for capturing conformational flexibility that will be beneficial for protein binding interactions.

Methods

We introduce DynamicDTA, an innovative deep learning framework that incorporates static and dynamic protein features to enhance DTA prediction. The proposed DynamicDTA takes three types of inputs, including drug sequence, protein sequence, and dynamic descriptors. A molecular graph representation of the drug sequence is generated and subsequently processed through graph convolutional network, while the protein sequence is encoded using dilated convolutions. Dynamic descriptors, such as root mean square fluctuation, are processed through a multi-layer perceptron. These embedding features are fused with static protein features using cross-attention, and a tensor fusion network integrates all three modalities for DTA prediction.

Results

Extensive experiments on three datasets demonstrate that DynamicDTA achieves by at least 3.4% improvement in \(e_{\text{RMSE}}\) e RMSE score with comparison to seven state-of-the-art baseline methods. Additionally, predicting novel drugs for Human Immunodeficiency Virus Type 1 and visualizing the docking complexes further demonstrates the reliability and biological relevance of DynamicDTA.

Availability and implementation

The source code is publicly available and can be accessed at https://github.com/shmily-ld/DynamicDTA.

Graphical Abstract