Validate a Foundation Model for Protein-Ligand Docking Acceleration
Overview
What this challenge is about.
Pick 20 publicly available protein-ligand complexes from the PDBbind dataset (or similar public source). Use a published open-source structural foundation model (e.g., a Boltz-style or DiffDock-style model — name your specific choice in the writeup) to predict ligand poses. Compute root-mean-square deviation (RMSD) of predicted vs. crystal poses, success rate at 2 Angstrom RMSD, and runtime per complex. Repeat on 5 'hard' cases the team flags. Write a 4-page validation report with a clear license / defer / build recommendation and a risk register. Cite the model's published numbers and discuss any gap to your reproduction.
The Brief
What you'll do, and what you'll demonstrate.
Validate an open-source structural foundation model on protein-ligand docking and recommend a license/defer/build path.
Earning criteria — what you'll demonstrate
- Evaluate a structural foundation model on a controlled benchmark
- Quantify success with structural-biology metrics (RMSD, success at 2 A)
- Identify the failure modes of a generative structural model
- Make a build/buy/defer recommendation grounded in measurement
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Research Scientist
Designing a leak-free benchmark of a published model and writing an honest validation is the first project a junior research scientist ships in a comp-chem team.
This challenge sharpens
- foundation-model-evaluation
- model-validation
- research-writing
Applied AI Scientist
Translating a model evaluation into a license/defer/build recommendation is the applied-AI bridge into a biotech R-and-D org.
This challenge sharpens
- foundation-model-evaluation
- scientific-ml
- structural-biology
ML Researcher
Reproducing published numbers and naming the gap to them is the literal first-week work of an ML researcher in an AI-for-science group.
This challenge sharpens
- foundation-model-evaluation
- pytorch
- research-writing