  Drug repurposing is an emerging approach for drug discovery involving the
reassignment of existing drugs for novel purposes. An alternative to the
traditional de novo process of drug development, repurposed drugs are faster,
cheaper, and less failure prone than drugs developed from traditional methods.
Recently, drug repurposing has been performed in silico, in which databases of
drugs and chemical information are used to determine interactions between
target proteins and drug molecules to identify potential drug candidates. A
proposed algorithm is NeuroCADR, a novel system for drug repurposing via a
multi-pronged approach consisting of k-nearest neighbor algorithms (KNN),
random forest classification, and decision trees. Data was sourced from several
databases consisting of interactions between diseases, symptoms, genes, and
affiliated drug molecules, which were then compiled into datasets expressed in
binary. The proposed method displayed a high level of accuracy, outperforming
nearly all in silico approaches. NeuroCADR was performed on epilepsy, a
condition characterized by seizures, periods of time with bursts of
uncontrolled electrical activity in brain cells. Existing drugs for epilepsy
can be ineffective and expensive, revealing a need for new antiepileptic drugs.
NeuroCADR identified novel drug candidates for epilepsy that can be further
approved through clinical trials. The algorithm has the potential to determine
possible drug combinations to prescribe a patient based on a patient's prior
medical history. This project examines NeuroCADR, a novel approach to
computational drug repurposing capable of revealing potential drug candidates
in neurological diseases such as epilepsy.

Biomolecules;Machine Learning;Quantitative Methods