2024

Cancer Research: Experimental Validation and Bioinformatics in Targeted NCE Development

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During my internship in the Structural Bioinformatics and Network Biology Group at the Institute for Research in Biomedicine (IRB), the main project I contributed to was the high-level signature-based generation of new chemical entities (NCEs). This project utilized artificial intelligence to identify molecules capable of selectively killing specific cancer cell types while leaving others unharmed.

 

My main responsibility was the experimental validation of computational results by performing cell viability assays to confirm the inhibitory effects of the predicted molecules on various cancer cell lines. I was also responsible for cell culture maintenance, which included performing cell passages, freezing cell samples, and ensuring the overall care of our cell lines. The data generated from these experiments were analyzed using GraphPad Prism to assess the efficacy of each molecule.

 

In addition to this, I contributed to a second project focused on the systematic identification of RIPTAC targets. My role in this project involved refining our western blot protocol for the target proteins EED and EZH2, and I used ImageJ to quantify the protein yields.

 

Toward the end of my internship, I had the opportunity to deepen my understanding of bioinformatics. I learned to create and refine models that can identify the protein targets of molecules based on their chemical data. I worked with several machine learning models, including logistic regressions, neural networks, and random forests, gaining practical experience in applying these techniques to real-world biological data.

 

To conclude my internship, I gave a presentation where I communicated the details of my work and the results I obtained to the rest of the team. This was an invaluable opportunity for me to hone my science communication skills and reflect on the key contributions I made during my internship.