I Trained Knowledge Graph Embeddings to Find a Cure for My Disease. The Model Found One That Causes It.

I have ulcerative colitis. It's an autoimmune disease where your immune system attacks your colon. I manage it with medication, but the medication options aren't great; immunosuppressants with side effects you wouldn't wish on anyone. Some days are fine, while others aren't so great.

I'm not a biologist. I'm a software engineer. But there's this whole field of computational drug repurposing that promises something remarkable and frankly delusional: take a massive database of known biological relationships, train a machine learning model on the patterns, and it can predict which existing drugs might treat new diseases. The drugs are already approved for other conditions, so you skip years of safety testing. Papers get published. Startups get funded. Impressive-looking ranked lists of "novel drug candidates" get produced.

I decided to build one for UC. I realise that the decision of going through this whole exercise was ambitious but it wasn't to personally discover a treatment, but to find out whether the approach actually works. Whether "45% of known treatments recovered" is a meaningful statement about drug discovery or a meaningless statement about graph retrieval. What I found was more interesting than any drug prediction could have been.