An optimized deep learning approach for blood-brain barrier permeability prediction with ODE integration

Abstract

Blood-brain barrier (BBB) permeability prediction plays a pivotal role in drug discovery for neurological disorders which is essential for the development of central nervous system (CNS) drugs. Compounds having high permeability must be found to synthesize brain drugs for the treatment of different brain disorders such as Alzheimer's, Parkinson's, and brain tumors. Developing an accurate mathematical computational model to determine the exact brain permeability value for a possible drug is essential for advancing and improving the success rate of the development of drugs for neurological treatment. We developed a combined method capable of forecasting the logBB value of the compound in question for lightBBB and B3DB datasets by using the Convolutional neural network (CNNs), Recurrent Neural Networks (RNNs) and Ordinary Differential Equations (ODEs). The results demonstrate the overall assessment of the prediction ability of BBB permeability. CNN-LSTM model tests the performance for the prediction of logBB values in two different datasets LightBBB and B3DB. In both datasets, CNN-LSTM achieves lower RMSE values as compared to other models, showing that it has better predictive performance. Specifically, in the LightBBB dataset, the CNN-LSTM model achieves an RMSE of 0.59, while in the B3DB datasets, it achieves an even lower RMSE of 0.85. CNN-LSTM model shows highly effective in accurately predicting logBB values in both datasets.

Keywords

• Blood-brain barrier
• Convolutional neural networks
• Long short-term memory
• Ordinary differential equation
• Alzheimer's disease