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  1. AU="Qiao, Haoran"
  2. AU=Ning Li
  3. AU="Djillali, Salih"

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  1. Article ; Online: AI-based Virtual Screening of Traditional Chinese Medicine and the Discovery of Novel Inhibitors of TCTP.

    Bai, Juxia / Ni, Yangyang / Zhang, Yuqi / Wan, Junfeng / Liang, Liqun / Qiao, Haoran / Zhu, Yanyan / Zhao, Qingjie / Li, Huiyu

    Current computer-aided drug design

    2024  

    Abstract: Background: Translationally controlled tumour protein (TCTP) is associated with tumor diseases, such as breast cancer, and its inhibitor can reduce the growth of tumor cells. Unfortunately, there is currently no effective medication available for ... ...

    Abstract Background: Translationally controlled tumour protein (TCTP) is associated with tumor diseases, such as breast cancer, and its inhibitor can reduce the growth of tumor cells. Unfortunately, there is currently no effective medication available for treating TCTP-related breast cancer.
    Objective: The objective of this study was to explore the inhibitor candidates among natural compounds for the treatment of breast cancer related to TCTP protein.
    Methods: To explore the potential inhibitors of TCTP, we first screened out four potential inhibitors in the Traditional Chinese Medicine (TCM) for cancer based on AI virtual screening using the docking method, and then revealed the interaction mechanism of TCTP and four candidate inhibitors from TCM with molecular docking and molecular dynamics (MD) methods.
    Results: Based on the conformational characteristics and the MD properties of the four leading compounds, we designed the new skeleton molecules with the AI method using MolAICal software. Our MD simulations have revealed that different small molecules bind to different sites of TCTP, but the flexible regions and the signaling pathways are almost the same, and the VDW and hydrophobic interactions are crucial in the interactions between TCTP and ligands.
    Conclusion: We have proposed the candidate inhibitor of TCTP. Our study has provided a potential new method for exploring inhibitors from Traditional Chinese Medicine (TCM).
    Language English
    Publishing date 2024-01-15
    Publishing country United Arab Emirates
    Document type Journal Article
    ISSN 1875-6697
    ISSN (online) 1875-6697
    DOI 10.2174/0115734099277605231218071503
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: From theory to experiment: transformer-based generation enables rapid discovery of novel reactions.

    Wang, Xinqiao / Yao, Chuansheng / Zhang, Yun / Yu, Jiahui / Qiao, Haoran / Zhang, Chengyun / Wu, Yejian / Bai, Renren / Duan, Hongliang

    Journal of cheminformatics

    2022  Volume 14, Issue 1, Page(s) 60

    Abstract: Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further ... ...

    Abstract Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further exploration. Inspired by molecular generation, we proposed a novel task of reaction generation. Herein, Heck reactions were applied to train the transformer model, a state-of-art natural language process model, to generate 4717 reactions after sampling and processing. Then, 2253 novel Heck reactions were confirmed by organizing chemists to judge the generated reactions. More importantly, further organic synthesis experiments were performed to verify the accuracy and feasibility of representative reactions. The total process, from Heck reaction generation to experimental verification, required only 15 days, demonstrating that our model has well-learned reaction rules in-depth and can contribute to novel reaction discovery and chemical space exploration.
    Language English
    Publishing date 2022-09-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 2486539-4
    ISSN 1758-2946
    ISSN 1758-2946
    DOI 10.1186/s13321-022-00638-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Self-Supervised Molecular Pretraining Strategy for Low-Resource Reaction Prediction Scenarios.

    Wu, Zhipeng / Cai, Xiang / Zhang, Chengyun / Qiao, Haoran / Wu, Yejian / Zhang, Yun / Wang, Xinqiao / Xie, Haiying / Luo, Feng / Duan, Hongliang

    Journal of chemical information and modeling

    2022  Volume 62, Issue 19, Page(s) 4579–4590

    Abstract: In the face of low-resource reaction training samples, we construct a chemical platform for addressing small-scale reaction prediction problems. Using a self-supervised pretraining strategy called MAsked Sequence to Sequence (MASS), the Transformer model ...

    Abstract In the face of low-resource reaction training samples, we construct a chemical platform for addressing small-scale reaction prediction problems. Using a self-supervised pretraining strategy called MAsked Sequence to Sequence (MASS), the Transformer model can absorb the chemical information of about 1 billion molecules and then fine-tune on a small-scale reaction prediction. To further strengthen the predictive performance of our model, we combine MASS with the reaction transfer learning strategy. Here, we show that the average improved accuracies of the Transformer model can reach 14.07, 24.26, 40.31, and 57.69% in predicting the Baeyer-Villiger, Heck, C-C bond formation, and functional group interconversion reaction data sets, respectively, marking an important step to low-resource reaction prediction.
    Language English
    Publishing date 2022-09-21
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.2c00588
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Transformer-based multitask learning for reaction prediction under low-resource circumstances.

    Qiao, Haoran / Wu, Yejian / Zhang, Yun / Zhang, Chengyun / Wu, Xinyi / Wu, Zhipeng / Zhao, Qingjie / Wang, Xinqiao / Li, Huiyu / Duan, Hongliang

    RSC advances

    2022  Volume 12, Issue 49, Page(s) 32020–32026

    Abstract: Recently, effective and rapid deep-learning methods for predicting chemical reactions have significantly aided the research and development of organic chemistry and drug discovery. Owing to the insufficiency of related chemical reaction data, computer- ... ...

    Abstract Recently, effective and rapid deep-learning methods for predicting chemical reactions have significantly aided the research and development of organic chemistry and drug discovery. Owing to the insufficiency of related chemical reaction data, computer-assisted predictions based on low-resource chemical datasets generally have low accuracy despite the exceptional ability of deep learning in retrosynthesis and synthesis. To address this issue, we introduce two types of multitask models: retro-forward reaction prediction transformer (RFRPT) and multiforward reaction prediction transformer (MFRPT). These models integrate multitask learning with the transformer model to predict low-resource reactions in forward reaction prediction and retrosynthesis. Our results demonstrate that introducing multitask learning significantly improves the average top-1 accuracy, and the RFRPT (76.9%) and MFRPT (79.8%) outperform the transformer baseline model (69.9%). These results also demonstrate that a multitask framework can capture sufficient chemical knowledge and effectively mitigate the impact of the deficiency of low-resource data in processing reaction prediction tasks. Both RFRPT and MFRPT methods significantly improve the predictive performance of transformer models, which are powerful methods for eliminating the restriction of limited training data.
    Language English
    Publishing date 2022-11-08
    Publishing country England
    Document type Journal Article
    ISSN 2046-2069
    ISSN (online) 2046-2069
    DOI 10.1039/d2ra05349g
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Order via subito

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    Inter-library loan at ZB MED

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  5. Article ; Online: Virtual data augmentation method for reaction prediction.

    Wu, Xinyi / Zhang, Yun / Yu, Jiahui / Zhang, Chengyun / Qiao, Haoran / Wu, Yejian / Wang, Xinqiao / Wu, Zhipeng / Duan, Hongliang

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 17098

    Abstract: To improve the performance of data-driven reaction prediction models, we propose an intelligent strategy for predicting reaction products using available data and increasing the sample size using fake data augmentation. In this research, fake data sets ... ...

    Abstract To improve the performance of data-driven reaction prediction models, we propose an intelligent strategy for predicting reaction products using available data and increasing the sample size using fake data augmentation. In this research, fake data sets were created and augmented with raw data for constructing virtual training models. Fake reaction datasets were created by replacing some functional groups, i.e., in the data analysis strategy, the fake data as compounds with modified functional groups to increase the amount of data for reaction prediction. This approach was tested on five different reactions, and the results show improvements over other relevant techniques with increased model predictivity. Furthermore, we evaluated this method in different models, confirming the generality of virtual data augmentation. In summary, virtual data augmentation can be used as an effective measure to solve the problem of insufficient data and significantly improve the performance of reaction prediction.
    MeSH term(s) Research Design
    Language English
    Publishing date 2022-10-12
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-21524-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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