Prediction of human intestinal absorption properties based on artificial intelligence

PU Chengtao; GU Lingqian; CHEN Xingye; ZHANG Yanmin

doi:10.11665/j.issn.1000-5048.2023032102

Journal of China Pharmaceutical University > 2023 > 54(3): 355-362. > DOI: 10.11665/j.issn.1000-5048.2023032102

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

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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

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Received Date: March 20, 2023
Revised Date: June 12, 2023

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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.
- human intestinal absorption,
- pharmacokinetic properties,
- machine learning,
- deep neural networks,
- classification model,
- artificial intelligence

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[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.

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