Drug-target binding affinity prediction using message passing neural network and self supervised learning

Xia, Leiming; Xu, Lei; Pan, Shourun; Niu, Dongjiang; Zhang, Beiyi; Li, Zhen

doi:10.1186/s12864-023-09664-z

Cited by 14 publications

(6 citation statements)

References 28 publications

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“…Flow forecasting is significant for planning transportation and allocating basic transportation resources [21]. In recent years, deep learning has played an essential role in computer science and other fields [22]. With the extensive application of new generational information technologies such as artificial intelligence and big data, intelligent transportation systems will become more intelligent, providing more personalized and convenient travel services for urban residents.…”

Section: Resultsmentioning

confidence: 99%

A survey of sustainable development of intelligent transportation system based on urban travel demand

Yan,

2024

Sustain. Social. Dev.

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<p>This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development.<strong></strong></p>

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Section: Resultsmentioning

confidence: 99%

A survey of sustainable development of intelligent transportation system based on urban travel demand

Yan,

2024

Sustain. Social. Dev.

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“…In the future, it is imperative to delve into DTA prediction methods based on deep learning from three key perspectives: Performance analysis of multiple state-of-the-art methods based on KIBA dataset. The evaluation metric values of these methods in the figure are sourced from References (Bi et al, 2023;Xia et al, 2023;Tian et al, 2024;Wu et al, 2024;Zhou et al, 2024). Performance analysis of multiple state-of-the-art methods based on Davis dataset.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 1 highlights PDBbind, KIBA, and Davis datasets as commonly used datasets for predicting DTA using deep learning. We summarized the performance evaluation metrics values of several state-of-the-art methods on PDBbind, KIBA, and Davis datasets, as reported in recently published literatures ( Wang et al, 2023a ; Zhu et al, 2023a ; Bi et al, 2023 ; Xia et al, 2023 ; Tian et al, 2024 ; Wu et al, 2024 ; Zhou et al, 2024 ), without considering the specific partitioning of the corresponding datasets by these methods. Although the statistical results ( Tables 4 , 5 ; Figures 5 – 7 ) showed that these methods have achieved good prediction performance for DTA on commonly used benchmark datasets, the further improvement in DTA prediction still faces challenges.…”

Section: Performance Analysis Of Multiple State-of-the-art Methods Ba...mentioning

confidence: 99%

“… Performance analysis of multiple state-of-the-art methods based on Davis dataset. The evaluation metric values of these methods in the figure are sourced from References ( Bi et al, 2023 ; Xia et al, 2023 ; Tian et al, 2024 ; Wu et al, 2024 ; Zhou et al, 2024 ). …”

Section: Performance Analysis Of Multiple State-of-the-art Methods Ba...mentioning

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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CGPDTA: An Explainable Transfer Learning‐Based Predictor With Molecule Substructure Graph for Drug‐Target Binding Affinity

Fan,

Liu,

Zhang

et al. 2024

J Comput Chem

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Identifying interactions between drugs and targets is crucial for drug discovery and development. Nevertheless, the determination of drug‐target binding affinities (DTAs) through traditional experimental methods is a time‐consuming process. Conventional approaches to predicting drug‐target interactions (DTIs) frequently prove inadequate due to an insufficient representation of drugs and targets, resulting in ineffective feature capture and questionable interpretability of results. To address these challenges, we introduce CGPDTA, a novel deep learning framework empowered by transfer learning, designed explicitly for the accurate prediction of DTAs. CGPDTA leverages the complementarity of drug–drug and protein–protein interaction knowledge through advanced drug and protein language models. It further enhances predictive capability and interpretability by incorporating molecular substructure graphs and protein pocket sequences to represent local features of drugs and targets effectively. Our findings demonstrate that CGPDTA not only outperforms existing methods in accuracy but also provides meaningful insights into the predictive process, marking a significant advancement in the field of drug discovery.

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Drug-target binding affinity prediction using message passing neural network and self supervised learning

Cited by 14 publications

References 28 publications

A survey of sustainable development of intelligent transportation system based on urban travel demand

A survey of sustainable development of intelligent transportation system based on urban travel demand

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

CGPDTA: An Explainable Transfer Learning‐Based Predictor With Molecule Substructure Graph for Drug‐Target Binding Affinity

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