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Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta

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submitted on 2024-02-12, 10:43 and posted on 2024-02-12, 10:44 authored by Vaisali Chandrasekar, Mohammed Yusuf Ansari, Ajay Vikram Singh, Shahab Uddin, Kirthi S. Prabhu, Sagnika Dash, Souhaila Al Khodor, Annalisa Terranegra, Matteo Avella, Sarada Prasad Dakua

Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. The ability of a molecule to effectively cross the placental barrier and reach the fetus determines the drug’s toxicological effects on the fetus. In this regard, our study aims to predict the permeability of molecules across the placental barrier. Based on publicly available datasets, several machine learning models are comprehensively analysed across different fingerprints and toolkits to find the best suitable models. Several dataset analysis models are utilised to study the data diversity. Further, this study demonstrates the application of neural network-based models to effectively predict the permeability. K-nearest neighbour (KNN), standard vector classifier (SVC) and Multi-layer perceptron (MLP) are found to be the best-performing models with a prediction percentage of 82%, 86.4% and 90.8%, respectively. Different models are compared to predict the chosen set of drugs, drugs like Aliskiren, some insulin secretagogues and glucocorticoids are found to be negative while predicting the permeability.

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Published in: IEEE Access
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Open Access funding provided by the Qatar National Library.



  • English



Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Hamad Medical Corporation
  • Interim Translational Research Institute - HMC
  • Sidra Medicine