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The introduction of computational techniques to analyze chemical data hasgiven 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 mostpressing cases is predicting blood-brain barrier (BBB) permeability. Thedevelopment of new drugs to treat central nervous system disorders presentsunique challenges due to poor penetration efficacy across the blood-brainbarrier. In this research, we aim to mitigate this problem through an ML modelthat analyzes chemical features. To do so: (i) An overview into the relevantbiological systems and processes as well as the use case is given. (ii) Second,an in-depth literature review of existing computational techniques fordetecting BBB permeability is undertaken. From there, an aspect unexploredacross current techniques is identified and a solution is proposed. (iii)Lastly, a two-part in silico model to quantify likelihood of permeability ofdrugs with defined features across the BBB through passive diffusion isdeveloped, tested, and reflected on. Testing and validation with the datasetdetermined the predictive logBB model's mean squared error to be around 0.112units and the neuroinflammation model's mean squared error to be approximately0.3 units, outperforming all relevant studies found.

In Silico Prediction of Blood-Brain Barrier Permeability of Chemical Compounds through Molecular Feature Modeling