Revolutionizing Cancer Drug Discovery with DrugGen: Identifying a Novel Drug for DNA polymerase θ

Overview

Homologous Recombination (HR) deficient cancers cause around 140,000 deaths yearly due to genetic changes, such as in BRCA1 and BRCA2 genes, which encode DNA repair enzymes. Mutations in these enzymes increase genomic instability, resulting in cancer growth. Chemotherapy is the standard treatment for HR-deficient cancers, but it causes severe side effects due to healthy cell death. Precision medicine offers a promising solution to the issue of specificity. Still, a significant percentage of patients fail to benefit from it due to the lack of FDA-approved drugs for targets overexpressed in their cancer. One such target is DNA Polymerase Theta (pol θ), a backup repair mechanism used by BRCA-deficient tumors. Pol θ is over- expressed in cancer cells and is an ideal drug target. Conventional drug development for this target would take significant resources and time. Computational approaches for drug development are promising to help in this chase of small-molecule inhibitors for targets. However, there is still no unified computational platform for identification of small -molecule inhibitors for precision medicine. Therefore, I developed DrugGen, an innovative approach that combines computational drug discovery utilities with a Graph -based Neural Network to identify novel inhibitors for use in precision medicine drug discovery. Using DrugGen, I identified a novel pol θ inhibitor with high potency, named SK -2. DrugGen is extensible for novel drug targets in precision medicine, and it can significantly reduce the cost and time required for drug development. Several targets in cancer still need to be explored, and DrugGen can help develop drugs for these targets.

Competition history

• JSHS 2024 Category not listed

Resources

• National JSHS abstract book ↗

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