A top 5 pharmaceutical company was on a mission to use AI to revolutionize drug discovery. They wanted in-house control of the technology that would define their future.
Molecular data is inherently three-dimensional — traditional machine learning approaches built for flat data don't apply.
Training data is scarce by nature — successful drug compounds are extremely rare, making model training a unique challenge.
The system needed to earn the trust of PhD researchers who are deeply skeptical of black-box AI making safety-critical predictions.
How we addressed each goal
Trained multiple neural network types on decades of drug safety data to predict toxicity from molecular structure alone, reducing reliance on animal testing in early screening stages.
Assigned a dedicated staff of machine learning engineers to build big data infrastructure capable of analyzing thousands of molecules per day, compressing timelines and reducing cost per compound.
Combined the AI back-end with a beautifully designed interface and intuitive workflow, empowering researchers to explore vast chemical spaces and surface novel molecular candidates.
Early results have been extremely encouraging, spurring the company to significantly expand exploration into AI across multiple phases of drug discovery.
Get a quote within 1 day guaranteed to cover your project from start to finish.
Get Your Quote