• 中国精品科技期刊
  • 中国高校百佳科技期刊
  • 中国中文核心期刊
  • 中国科学引文数据库核心期刊
Advanced Search
PU Chengtao, GU Lingqian, CHEN Xingye, ZHANG Yanmin. Prediction of human intestinal absorption properties based on artificial intelligence[J]. Journal of China Pharmaceutical University, 2023, 54(3): 355-362. DOI: 10.11665/j.issn.1000-5048.2023032102
Citation: PU Chengtao, GU Lingqian, CHEN Xingye, ZHANG Yanmin. Prediction of human intestinal absorption properties based on artificial intelligence[J]. Journal of China Pharmaceutical University, 2023, 54(3): 355-362. DOI: 10.11665/j.issn.1000-5048.2023032102
shu

Prediction of human intestinal absorption properties based on artificial intelligence

  • Institute of Medical Big Data and Artificial Intelligence, School of Science, China Pharmaceutical University, Nanjing 211198, China

More Information
  • Received Date: March 20, 2023
  • Revised Date: June 12, 2023
    Graphical Abstract
    • Abstract
  • Human intestinal absorption (HIA) is a crucial indicator for measuring the oral bioavailability of drugs.This study aims to use artificial intelligence methods to predict and evaluate the HIA of drugs in the early stages of drug discovery, thus accelerating the drug discovery process and reducing costs.This study used MOE''s 2D, 3D descriptors, and ECFP4 (extended connectivity fingerprints) to characterize the molecules and established eight models, including support vector machine (SVM), random forest (RF), and deep neural network (DNN).The results showed that the SVM model constructed using a combination of 2D, 3D descriptors and ECFP4 fingerprints was the optimal model according to comprehensive evaluation of various evaluation indicators.The area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient, and Kappa coefficient of the optimal model were 0.94, 0.75, and 0.74, respectively.In conclusion, this study established a robust and generalizable machine learning model for predicting HIA properties, which can provide guidance for early molecular screening and the study of pharmacokinetic properties of drugs.
    • FullText(HTML)
    • References (21)
  • [1]
    Pillai N, Dasgupta A, Sudsakorn S, et al. Machine Learning guided early drug discovery of small molecules[J]. Drug Discov Today, 2022, 27(8): 2209-2215.
    [2]
    Basant N, Gupta S, Singh KP. Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches[J]. Comput Biol Chem, 2016, 61: 178-196.
    [3]
    Hou TJ, Wang JM, Li YY. ADME evaluation in drug discovery. 8. The prediction of human intestinal absorption by a support vector machine[J]. J Chem Inf Model, 2007, 47(6): 2408-2415.
    [4]
    Kumar R, Sharma A, Siddiqui MH, et al. Prediction of human intestinal absorption of compounds using artificial intelligence techniques[J]. Curr Drug Discov Technol, 2017, 14(4): 244-254.
    [5]
    Fu MY, Zhu YY, Wu CY, et al. Prediction of plasma protein binding rate based on machine learning[J]. J China Pharm Univ (中国药科大学学报), 2021, 52(6): 699-706.
    [6]
    Wang YC, Liu HC, Fan YR, et al. In silico prediction of human intravenous pharmacokinetic parameters with improved accuracy[J]. J Chem Inf Model, 2019, 59(9): 3968-3980.
    [7]
    Yang M, Chen JL, Xu LW, et al. A novel adaptive ensemble classification framework for ADME prediction[J]. RSC Adv, 2018, 8(21): 11661-11683.
    [8]
    Xing GM, Liang L, Deng CL, et al. Activity prediction of small molecule inhibitors for antirheumatoid arthritis targets based on artificial intelligence[J]. ACS Comb Sci, 2020, 22(12): 873-886.
    [9]
    Jadhav SD, Channe HP.. Comparative study of K-NN, naive Bayes and decision tree classification techniques[J]. Int J Sci Res, 2016, 5(1): 1842-1845.
    [10]
    Wang MWH, Goodman JM, Allen TEH. Machine learning in predictive toxicology: recent applications and future directions for classification models[J]. Chem Res Toxicol, 2021, 34(2): 217-239.
    [11]
    Geurts P, Ernst D, Wehenkel L. Extremely randomized trees[J]. Mach Learn, 2006, 63(1): 3-42.
    [12]
    Ke GL, Meng Q, Finley T, et al. LightGBM: a highly efficient gradient boosting decision tree[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York: ACM, 2017: 3149-3157.
    [13]
    Chen TQ, Guestrin C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 785-794.
    [14]
    Dou LJ, Li XL, Zhang LC, et al. iGlu_AdaBoost: identification of lysine glutarylation using the AdaBoost classifier[J]. J Proteome Res, 2021, 20(1): 191-201.
    [15]
    Ciregan D, Meier U, Schmidhuber J. Multi-column deep neural networks for image classification[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence. IEEE, 2012: 3642-3649.
    [16]
    Tharwat A. Classification assessment methods[J].Appl Comput Inform, 2018, 12(1): 1-13.
    [17]
    Warr WA. Scientific workflow systems: pipeline pilot and KNIME[J]. J Comput Aided Mol Des, 2012, 26(7): 801-804.
    [18]
    Tipping ME, Bishop CM. Probabilistic principal component analysis[J]. J R Stat Soc, 1999, 61(3): 611-622.
    [19]
    Maaten L, Hinton GE. Visualizing data using t-SNE[J]. J Mach Learn Res, 2008, 9: 2579-2605.
    [20]
    Rogers D, Hahn M. Extended-connectivity fingerprints[J].J Chem Inf Model, 2010, 50(5): 742-754.
    [21]
    Carracedo-Reboredo P, Li?ares-Blanco J, Rodríguez-Fernández N, et al. A review on machine learning approaches and trends in drug discovery[J].Comput Struct Biotechnol J, 2021, 19: 4538-4558.
    • Related Articles

  • Related Articles

    [1]ZHANG Zhixing, DENG Hua, TANG Yun. Applications and challenges of artificial intelligence in the development of anticancer peptides[J]. Journal of China Pharmaceutical University, 2024, 55(3): 347-356. DOI: 10.11665/j.issn.1000-5048.2024040201
    [2]HU Zi’ang, GAO Liming, YU Wenying. Advances in the application of artificial intelligence in nucleic acid drug development[J]. Journal of China Pharmaceutical University, 2024, 55(3): 335-346. DOI: 10.11665/j.issn.1000-5048.2024033101
    [3]ZENG Hao, WU Guozhen, ZOU Wuxin, WANG Zhe, SONG Jianfei, SHI Hui, WANG Xiaojian, HOU Tingjun, DENG Yafeng. Optimization of Menin inhibitors based on artificial intelligence-driven molecular factory technology[J]. Journal of China Pharmaceutical University, 2024, 55(3): 326-334. DOI: 10.11665/j.issn.1000-5048.2024040904
    [4]TANG qian, CHEN Roufen, SHEN Zheyuan, CHI Xinglong, CHE Jinxin, DONG Xiaowu. Research progress of artificial intelligence-based small molecule generation models in drug discovery[J]. Journal of China Pharmaceutical University, 2024, 55(3): 295-305. DOI: 10.11665/j.issn.1000-5048.2024031501
    [5]XUE Feng, FENG Shuo, LI Jing. Application and prospect of artificial intelligence in antimicrobial peptides screening[J]. Journal of China Pharmaceutical University, 2023, 54(3): 314-322. DOI: 10.11665/j.issn.1000-5048.2023030901
    [6]GU Zhihao, GUO Wenhao, YAO Hequan, LI Xuanyi, LIN Kejiang. Research progress of the screening and generation of lead compounds based on artificial intelligence model[J]. Journal of China Pharmaceutical University, 2023, 54(3): 294-304. DOI: 10.11665/j.issn.1000-5048.2023042201
    [7]YU Zehao, ZHANG Leiming, ZHANG Mengna, DAI Zhiqi, PENG Chengbin, ZHENG Siming. Artificial intelligence-based drug development: current progress and future challenges[J]. Journal of China Pharmaceutical University, 2023, 54(3): 282-293. DOI: 10.11665/j.issn.1000-5048.2023041003
    [8]WANG Chao, XIAO Fu, LI Miaozhu, PAN Ying, DING Xiao, REN Feng, ZHAVORONKOV Alex, WANG Yazhou. Application progress of artificial intelligence in the screening and identification of drug targets[J]. Journal of China Pharmaceutical University, 2023, 54(3): 269-281. DOI: 10.11665/j.issn.1000-5048.2023041102
    [9]YAN Fangrong. Application and advance of artificial intelligence in biomedical field[J]. Journal of China Pharmaceutical University, 2023, 54(3): 263-268. DOI: 10.11665/j.issn.1000-5048.2023030304
    [10]Zhang Zunjian, Yu Shuqin, Xiang Bingren, An Dengkui. A New Artificial Neural Network Model:Combined Counter-Back Propagation and its Application[J]. Journal of China Pharmaceutical University, 1996, (11).

  • Cited by

    Periodical cited type(1)

    1. 赵倩，魏宇，郭凯敏，王文佳，周水平，孙鹤，方坚松，胡蕴慧. 人工智能结合生物网络对“养血清脑制剂”和“逍遥丸”组方的优化及验证. 中药药理与临床. 2024(04): 27-33 .

    Other cited types(3)

Catalog

    Get Citation
    PDF
    XML
    Article views (495) PDF downloads (434) Cited by(4)
    Turn off MathJax
    Article Contents
    Abstract
    References
    Related Articles

    Copyright © Journal of China Pharmaceutical University苏ICP备12050101号-2

    Address：24 Tongjia Lane, Nanjing City, Jiangsu Province （210009）Tel: 025-83271566E-mail: xuebao@cpu.edu.cn

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    Total visits：295209

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return