Global pharmaceutical leader uses custom AI to unlock predictive insights from complex cell painting data.
High-Content Screening (HCS) technologies such as Cell Painting offer tremendous potential for drug discovery, but they also generate huge, complex data sets that are difficult to analyze and interpret at scale. In this case study, we explore how Ardigen partnered with a major pharmaceutical company to transform multidimensional imaging data into predictive biological insights using advanced artificial intelligence techniques.
The Challenge
The client faced several significant challenges in extracting actionable insights from Cell Painting data:
- Sparse activity labels: Many screened compounds yielded minimal biological activity, making it difficult to train reliable models.
- Data heterogeneity: Datasets were generated from multiple libraries, concentrations, and cell lines, introducing batch effects and inconsistencies.
- Storage and processing inefficiencies: Large-scale image datasets presented challenges in data handling and optimization.
Our Approach
Ardigen deployed a tailored AI strategy that brought together domain expertise and scalable computing:
- Custom AI models trained on both raw images and human-engineered features, maximizing signal extraction from complex HCS data.
- Prediction reliability assessment built directly into the models, helping scientists interpret outcomes with confidence.
- Batch effect reduction techniques to harmonize data from various sources.
- Construction of a robust, integrated dataset across multiple compound libraries using the Cell Painting protocol.
Results
The project delivered strong, measurable impact:
- 2x improvement in accurate predictions of biological activity.
- 20% increase in the retrospective ROC AUC of AI models, confirming better classification performance.
- 50% boost in loading optimization and storage efficiency, improving data handling across the pipeline.
By combining advanced artificial intelligence techniques with deep biological understanding, Ardigen enabled the client to transform complex imaging data into valuable insights that propel drug discovery. This case highlights out-of-the-box AI solutions to increase the value of HCS data, even when labels are rare and sources are diverse.
