Drug resistance threatens the effective prevention and treatment of an ever-increasing range of
human infections. This highlights an urgent need for new and improved drugs with novel
mechanisms of action to avoid cross-resistance. Current cell-based drug screens are,
however, restricted to binary live/dead readouts with no provision for mechanism of action
prediction. Machine learning methods are increasingly being used to improve information
extraction from imaging data. Such methods, however, work poorly with heterogeneous
cellular phenotypes and generally require time-consuming human-led training. We have
developed a semi-supervised machine learning approach, combining human- and machinelabelled training data from mixed human malaria parasite cultures. Designed for highthroughput and high-resolution screening, our semi-supervised approach is robust to natural
parasite morphological heterogeneity and correctly orders parasite developmental stages. Our
approach also reproducibly detects and clusters drug-induced morphological outliers by
mechanism of action, demonstrating the potential power of machine learning for accelerating
cell-based drug discovery.