- Role
- Finalist
- Region
- Lakeland
- Pronouns
- he/him/his
Ali Usman
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Project
Optimizing Drug Discovery Using Wet Lab Data to Predict Cancer Cell Line Inhibitors
Project #5308
Chemotherapy, immunotherapy, or radiation are traditional cancer treatments rendered useless once cancer cells develop acquired resistance due to uncontrolled mutations. This necessitates the need to find new therapeutics through drug discovery. However, the process of drug discovery costs $1-2 billion and takes 12-15 years, leading to cancer patients paying ridiculous out-of-pocket costs for lifesaving treatments. I've developed machine learning models to speed up drug discovery by utilizing drug sensitivity data to predict new cancer cell line inhibitors across various cancer types. Compounds are screened for thousands of molecular features using Morgan fingerprints and then ranked by their z-scores to indicate inhibition. Next, top inhibitors for each cancer cell line are analyzed to determine how they bind to target proteins that drive cancer growth and their pharmacological properties like absorption or solubility. Ultimately, my approach optimizes drug discovery by identifying top inhibitors to discover more affordable cancer drugs for patients.
- Challenge
- Disease and Illness
- Category
- Senior
- Type
- Innovation