Utilizing machine learning techniques to predict the blood-brain barrier permeability of compounds detected using LCQTOF-MS in Malaysian Kelulut honey

ABSTRACT

Current in silico modelling techniques, such as molecular dynamics, typically focus on compounds with the highest concentration from chromatographic analyses for bioactivity screening. Consequently, they reduce the need for labour-intensive in vitro studies but limit the utilization of extensive chromatographic data and molecular diversity for compound classification. Compound permeability across the blood–brain barrier (BBB) is a key concern in central nervous system (CNS) drug development, and this limitation can be addressed by applying cheminformatics with codeless machine learning (ML). Among the four models developed in this study, the Random Forest (RF) algorithm with the most robust performance in both internal and external validation was selected for model construction, with an accuracy (ACC) of 87.5% and 86.9% and area under the curve (AUC) of 0.907 and 0.726, respectively. The RF model was deployed to classify 285 compounds detected using liquid chromatography quadrupole time-of-flight mass spectrometry (LCQTOF-MS) in Kelulut honey; of which, 140 compounds were screened with 94 descriptors. Seventeen compounds were predicted to permeate the BBB, revealing their potential as drugs for treating neurodegenerative diseases. Our results highlight the importance of employing ML pattern recognition to identify compounds with neuroprotective potential from the entire pool of chromatographic data.

KEYWORDS:

• Machine learning
• Kelulut honey
• blood–brain barrier
• chemometrics
• random forest

Acknowledgements

The authors would like to express gratitude to the Faculty of Chemical and Process Engineering Technology (FTKKP) and Centre for Advanced Research in Fluid Flow (CARIFF) for the equipment and training provided to accomplish this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in Blood-Brain Barrier Database (B3DB) (https://github.com/theochem/B3DB) at 10.6084/m9.figshare.15634230.v3 [27].

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2023.2230868

Additional information

Funding

The authors would like to thank Universiti Malaysia Pahang for the research funding under the grant number RDU190357 and Ministry of Education Malaysia in providing the fund for this project under the Fundamental Research Grant Scheme [FRGS; Grant Number: RDU210118; FRGS/1/2021/SKK06/UMP/02/1].