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DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy
Rajnish Kumar1, Anju Sharma2, Athanasios Alexiou3,4
1Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India.
Frontiers in Neuroscience
|May 20, 2022
Summary
This study developed DeePred-BBB, a deep learning model that accurately predicts blood-brain barrier permeability for CNS drug development. The model achieves high accuracy, aiding early-stage compound screening.
Area of Science:
- Computational chemistry
- Neuroscience
- Pharmacology
Background:
- The blood-brain barrier (BBB) is crucial for central nervous system (CNS) homeostasis.
- Predicting BBB permeability is vital for CNS-acting drug development but challenging.
- Current clinical methods are accurate but time-consuming and labor-intensive.
Purpose of the Study:
- To improve the accuracy of blood-brain barrier (BBB) permeability prediction models.
- To develop a computational tool for early-stage screening of CNS-acting drug candidates.
- To leverage deep learning and machine learning for enhanced BBB permeability prediction.
Main Methods:
- Applied deep learning and machine learning algorithms to a dataset of 3,605 compounds.
- Utilized 1,917 features per compound, including physicochemical properties and molecular fingerprints.
- Developed and compared prediction models such as DNN, 1D-CNN, and transfer learning CNN.
Main Results:
- Achieved high prediction accuracies: DNN (98.07%), 1D-CNN (97.44%), and transfer learning CNN (97.61%).
- Selected the best-performing DNN model for the DeePred-BBB tool.
- DeePred-BBB predicts BBB permeability using simplified molecular input line entry system (SMILES) notations.
Conclusions:
- The developed DNN-based model, DeePred-BBB, offers accurate BBB permeability prediction.
- This tool can significantly aid in the preliminary screening of compounds for CNS drug development.
- DeePred-BBB is publicly available, facilitating broader research and application.
