NeuroCADR: Drug Repositioning to Reveal Novel Anti-Epileptic Drug Candidates Through an Integrated Computational Approach
JSHS · 2022
Overview
Drug repositioning 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, repositioned drugs are faster, cheaper, and less failure prone than drugs developed from traditional methods. Recently, drug repositioning has been performed in silico - 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 approach for drug repositioning via spherical k-means and k-nearest neighbor algorithms (KNN). Data sourced from several databases consisting of interactions between genes, proteins, and drug molecules were compiled into separate binarified datasets. These were inputted into an KNN machine learning algorithm that learned associations between these. The proposed method displayed a high level of accuracy, outperforming nearly all in silico approaches. NeuroCADR was performed on epilepsy, a condition that is 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 was incorporated into a user-friendly website for medical professionals 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 repositioning capable of revealing potential drug candidates in neurological diseases such as epilepsy. PUERTO RICO
Competition history
- JSHS 2022
Resources
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