Drug Discovery Prediction Platform
Top-10 Pharmaceutical Company
The Challenge
The client's research team was manually screening thousands of molecular compounds for potential drug candidates — a process that took months per batch and had a hit rate below 5%. They needed a way to prioritize the most promising candidates before committing to expensive wet-lab experiments.
Our Solution
We built a custom prediction model trained on the client's proprietary molecular data combined with public datasets. The system uses graph neural networks to analyze molecular structures and predict binding affinities against target proteins. We deployed the model on AWS SageMaker with a React dashboard that lets researchers interactively explore predictions, filter by confidence scores, and export candidates for lab validation.
Key Insight
The biggest performance gain came not from model architecture, but from our data augmentation strategy — generating synthetic molecular variants that expanded the training set by 8x while maintaining chemical validity.
Results
"Servesys didn't just build a model — they fundamentally changed how our research team approaches candidate selection. The platform has become indispensable."
VP of Computational Biology
Fortune 100 Pharmaceutical