Graph Transformer Research Probe for a Drug-Target Predictor
Overview
What this challenge is about.
Train a GraphGPS transformer on 50K drug-target pairs, compare to a GIN baseline, run 3 ablations, and write a research memo. Earn a verifiable certificate.
The scenario
The pharma-AI lab (around 25 researchers, part of a bigger consortium) competes with academic labs and a few well-funded biotech startups on novel-target prediction; a 2-point ROC-AUC lift on this task is publishable and commercially meaningful.
The Brief
What you'll do, and what you'll demonstrate.
Quantify whether graph-transformers beat strong message-passing GNNs on drug-target interaction prediction and recommend a research investment.
Earning criteria — what you'll demonstrate
- Implement and compare a Graph Transformer with a message-passing GNN
- Run controlled ablations on transformer hyperparameters
- Evaluate molecular learning models with per-family stratification
- Write publication-style research memos for leadership
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Graph Transformers
Apply graph transformers to solve real industry problems and demonstrate production-level capability.
- Graph Neural Networks
Apply graph neural networks to solve real industry problems and demonstrate production-level capability.
- Message Passing
Apply message passing to solve real industry problems and demonstrate production-level capability.
- Drug Target Prediction
Apply drug target prediction to solve real industry problems and demonstrate production-level capability.
- Pytorch Geometric
Apply pytorch geometric to solve real industry problems and demonstrate production-level capability.
- Experiment Design
Apply experiment design to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Research Scientist
Comparing graph-transformers against strong GNN baselines with rigorous ablations is exactly the day-one work of a research scientist on any graph-ML or chem-informatics team.
This challenge sharpens
- graph-transformers
- experiment-design
- drug-target-prediction
ML Researcher
Running compute-matched benchmarks and writing publication-style memos transfers directly to ML-researcher roles in industry research labs.
This challenge sharpens
- graph-neural-networks
- message-passing
- experiment-design
Applied AI Scientist
Translating a methodology result into a research-investment recommendation is core applied-AI-scientist work in pharma AI.
This challenge sharpens
- graph-transformers
- pytorch-geometric
- drug-target-prediction