Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug–Target Affinity

Zeng, Xin; Zhong, Kai-Yang; Jiang, Bei; Li, Yi

doi:10.3390/molecules28248005

Cited by 6 publications

(1 citation statement)

References 40 publications

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“…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%

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%

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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GNN‐DDAS: Drug discovery for identifying anti‐schistosome small molecules based on graph neural network

Zeng,

Feng,

et al. 2024

J Comput Chem

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Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti‐schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti‐schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer‐aided methods. To address this issue, we proposed GNN‐DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti‐schistosome (DDAS) small molecules. Initially, a multi‐layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti‐schistosome small molecules. Experimental results showed that GNN‐DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real‐world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN‐DDAS. Overall, GNN‐DDAS provided a promising solution for discovering new and active anti‐schistosome small molecules.

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Recent advances in computational and experimental protein-ligand affinity determination techniques

Kairys,

Baranauskiene,

Kazlauskiene

et al. 2024

Expert Opinion on Drug Discovery

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Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug–Target Affinity

Cited by 6 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

GNN‐DDAS: Drug discovery for identifying anti‐schistosome small molecules based on graph neural network

Recent advances in computational and experimental protein-ligand affinity determination techniques

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