Innovation in drug development workflows that rely on machine learning is largely dependent on the quality of your training data. A leading contract research organization in Europe, Selvita, faced the familiar challenge of inconsistent and disparate open-access data that slowed down Machine Learning model development.

In this exclusive case study, discover how Selvita partnered with CAS to overcome this hurdle by leveraging a custom-curated training dataset, built in just a few days using the CAS Content Collection™.

The result? A faster, more streamlined organic synthesis workflow, faster and more efficient drug discovery solutions — and months of manual effort saved.

This case study provides a clear understanding of how expert-curated data can transform your machine learning strategy and accelerate time-to-discovery, regardless of whether you’re leading R&D, driving innovation, or navigating data problems in the chemical or pharmaceutical industries.

👉 Download the case study to learn:

• Why poor data quality slows down Machine Learning efforts in synthesis
• How CAS built a tailored dataset in record time
• The measurable impact on Selvita’s efficiency and drug discovery pipeline