Student research portfolio
Alina Ren
I’m a high school student who wants to be a doctor. In the meantime, I’m teaching myself molecular docking to look for new uses of drugs that are already FDA approved.
My project screens approved drugs against proteins from Leishmania, the parasite behind visceral leishmaniasis, a disease that gets very little research attention. Every step is written up here, including the parts that didn’t go to plan.
The project at a glance
Docking predicts how tightly a drug molecule might fit into a protein. It generates hypotheses, not answers, and I treat it that way.
- parasite protein targets screened
- 3
- FDA-approved drugs in my screening library
- 1,859
- drugs docked in each full screen (of 1,808 attempted)
- 1,795
- cross-check comparing all three screens
- 1
Featured research
The three most recent findings. Each write-up shows the method, the result, and what I checked before trusting it.
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Cross-screen analysis
Sticky vs. Specific: Cross-Checking Three Screens
The same drugs kept showing up near the top of more than one target’s results. Is that a real signal, or is Vina’s scoring just enthusiastic about certain molecules?
Read the write-up -
Screen 3 of 3
Screening CYP51: A Pipeline Sanity Check
A third Leishmania target, picked partly to test the pipeline against a class of drug I already know the answer for, and it produced the strongest literature-backed hit of the project so far.
Read the write-up -
Screen 2 of 3
Screening Trypanothione Reductase: The Pivot Away from Specialty Drugs
PTR1’s top hits were all very expensive cancer drugs, so I switched to a completely different Leishmania target, trypanothione reductase, instead of giving up on cheap repurposing.
Read the write-up
About me and how I work
I plan to become a doctor, and this project is how I’m learning what real computational research feels like. I run the docking calculations on my own GPU workstation, read the published literature to check what the results mean, and write up each step so other students can follow along.
Questions, feedback and collaboration ideas are welcome at alinaren@biomedbound.com.
Using AI, openly
I build this project with AI tools, mainly Claude Code, and I say so on every page. The way I think about it: if I can run 3 miles on my own, a bike lets me ride 30. The bike is worth writing about, not hiding. The decisions about what to run and how to read the results are mine to understand and defend, and I’ve included the corrections along the way.