# Aimie Garces

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Aimie Garces](https://cerebralvalley.ai/u/Aimie_Garces)
- **GitHub:** https://github.com/Eleftheria14/predicting-cns-target-validation
- **Demo video:** https://youtu.be/2Urf21q4VdY
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/147

Most new drugs fail, and the most expensive mistake is made years before a trial begins: choosing the wrong target (the specific protein a drug is designed to act on) for a disease. This is worst in the brain and nervous system, where success rates are the lowest in medicine. Two decades of Alzheimer's programs aimed at amyloid plaques failed one after another, and a Huntington's program to lower the very protein that causes the disease was halted in late-stage trials despite the target being genetically certain. The science was plausible and the money was huge; the target choice still did not pay off. This project asked whether the clinical success of a target-disease pairing can be predicted from the basic biology of the target protein alone, before anyone spends a dollar on a trial.

A set of models that score how likely a target-disease pairing is to succeed, trained on a purpose-built dataset of 6,843 brain-disease pairings that captures not just the success stories but the far larger set of quiet failures most databases leave out. Each protein is described using bioinformatics: its amino-acid sequence, predicted 3D structure, likely biological functions, and how intolerant its gene is to mutation. These descriptions deliberately leave out the after-the-fact clues many other models rely on, such as how much a target has already been studied, since those clues do not exist for a brand-new target and flatter a model's apparent performance. The dataset and feature pipeline were built with an AI-agent workflow on Claude Science.

A protein's own biology carries a real, if partial, signal of clinical success. The model does not give a yes-or-no verdict; it ranks candidates from more to less promising, and ranks a true success above a failure about 85% of the time, against 50% for a coin toss: useful for sorting a shortlist, not a guarantee for any one target. The signal is genuine, not luck, since scrambling the true answers dropped performance to chance.

The result that matters most is the comparison to existing tools. They report scores above 90%, but on an easier test that lets the model lean on stand-ins for its answers, such as a new target for a disease it has already studied. Those tools depend on a target's "association score," a summary of how strongly databases already link a gene to a disease. Tested the honest way, training on the past and predicting which targets entered trials next, that score barely beats a coin toss (about 56%). This model avoids the shortcut and still reaches about 70% on that same fair footing. It also delivers what the high-scoring black boxes cannot: a prediction whose reasoning a person can read.

 drug-development team commits hundreds of millions of dollars per program, and no committee can responsibly kill or greenlight one on a number it cannot examine. This project shows a transparent model, whose reasoning a person can read, matched the black box with no measurable loss. An end user can see why a target scored well, sanity-check it against known biology, catch the model leaning on something spurious, and be told when a target falls outside what it can reliably judge. The result is a target-ranking tool that is accurate, inspectable, and honest about its limits, aimed at one of the costliest decisions in medicine.

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