A machine learning-based quantitative model (LogBB_Pred) to predict the blood–brain barrier permeability (logBB value) of drug compounds

Author:

Shaker Bilal1,Lee Jingyu1,Lee Yunhyeok1,Yu Myeong-Sang1,Lee Hyang-Mi1,Lee Eunee2,Kang Hoon-Chul3,Oh Kwang-Seok4ORCID,Kim Hyung Wook5,Na Dokyun1

Affiliation:

1. Department of Biomedical Engineering, Chung-Ang University , Seoul 06974, Republic of Korea

2. Division of Pediatric Neurology, Department of Pediatrics, Severance Children’s Hospital, Yonsei University College of Medicine, Epilepsy Research Institute , Seoul 03722, Republic of Korea

3. Department of Anatomy College of Medicine, Yonsei University , Seoul 03722, Republic of Korea

4. Convergence Drug Research Center, Korea Research Institute of Chemical Technology , Daejeon 34114, Republic of Korea

5. Department of Bio-integrated Science and Technology, College of Life Sciences, Sejong University , Seoul 05006, Republic of Korea

Abstract

Abstract Motivation Efficient assessment of the blood–brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate. Results Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29–0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. Availability and implementation Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

Funder

National Research Foundation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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