In Silico prediction of Blood-Brain Barrier permeability of chemical compounds using molecular feature modeling

The introduction of computational techniques to analyze chemical data has given rise to the analytical study of biological systems, known as “bioinformatics”. One facet of bioinformatics is using machine learning (ML) technology to detect multivariable trends in various cases. Among the most pressing cases is predicting blood-brain barrier (BBB) permeability. The development of new drugs to treat central nervous system disorders presents unique challenges due to poor penetration efficacy across the blood-brain barrier. In this research, we aim to mitigate this problem through an ML model that analyzes chemical features. To do so: (i) An overview into the relevant biological systems and processes as well as the use case is presented. (ii) second, an in-depth literature review of existing computational techniques for detecting BBB permeability was undertaken. From there, an aspect unexplored across current techniques was identified and a solution is proposed. (iii) Lastly, a two-part in silico model to quantify the likelihood of permeability of drugs with defined features across the BBB through passive diffusion is developed, tested, and discussed. Testing and validation with the dataset determined the predictive logBB model’s mean squared error to be ~ 0.112 units. The currently used neuro-inflammation model’s mean squared error was approximately 0.3 units. The developed model hence outperforms the currently used model to predict permeability into the BBB.