Identifying active compounds for a target is time- and resource-intensive. Here, the authors show that deep learning models trained on Cell Painting and single-point activity data, can reliably predict compound activity across diverse targets while maintaining high hit rates and scaffold diversity.
- Johan Fredin Haslum
- Charles-Hugues Lardeau
- Erik Müllers
