Abstract

Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process. In this study, a total of 1588 drug molecules with HOB data were collected from the literature for the development of a classifying model that uses the consensus predictions of five random forest models. The consensus model shows excellent prediction accuracies on two independent test sets with two cutoffs of 20% and 50% for classification of molecules. The analysis of the importance of the input variables allowed the identification of the main molecular descriptors that affect the HOB class value. The model is available as a web server at www.icdrug.com/ICDrug/ADMET for quick assessment of oral bioavailability for small molecules. The results from this study provide an accurate and easy-to-use tool for screening of drug candidates based on HOB, which may be used to reduce the risk of failure in late stage of drug development.

Details

Title
HobPre: accurate prediction of human oral bioavailability for small molecules
Author
Wei, Min 1 ; Zhang, Xudong 1 ; Pan Xiaolin 1 ; Wang, Bo 1 ; Ji Changge 2 ; Qi Yifei 3   VIAFID ORCID Logo  ; Zhang John Z H 4 

1  East China Normal University, Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, Shanghai, China (GRID:grid.22069.3f) (ISNI:0000 0004 0369 6365) 
2  East China Normal University, Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, Shanghai, China (GRID:grid.22069.3f) (ISNI:0000 0004 0369 6365); NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, China (GRID:grid.449457.f) (ISNI:0000 0004 5376 0118) 
3  Fudan University, Department of Medicinal Chemistry, School of Pharmacy, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443) 
4  East China Normal University, Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, Shanghai, China (GRID:grid.22069.3f) (ISNI:0000 0004 0369 6365); NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, China (GRID:grid.449457.f) (ISNI:0000 0004 5376 0118); New York University, Department of Chemistry, New York, USA (GRID:grid.137628.9) (ISNI:0000 0004 1936 8753); Chinese Academy of Sciences, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, China (GRID:grid.9227.e) (ISNI:0000000119573309); Shanxi University, Collaborative Innovation Center of Extreme Optics, Taiyuan, China (GRID:grid.163032.5) (ISNI:0000 0004 1760 2008) 
Publication year
2022
Publication date
Dec 2022
Publisher
Springer Nature B.V.
e-ISSN
1758-2946
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1186/s13321-021-00580-6
ProQuest document ID
2617107635
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.