  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. Amongst 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 given. (ii) Second,
an in-depth literature review of existing computational techniques for
detecting BBB permeability is undertaken. From there, an aspect unexplored
across current techniques is identified and a solution is proposed. (iii)
Lastly, a two-part in silico model to quantify likelihood of permeability of
drugs with defined features across the BBB through passive diffusion is
developed, tested, and reflected on. Testing and validation with the dataset
determined the predictive logBB model's mean squared error to be around 0.112
units and the neuroinflammation model's mean squared error to be approximately
0.3 units, outperforming all relevant studies found.

Quantitative Methods;Machine Learning