Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks

Predicting the outcome of biological
assays based on high-throughput
imaging data is a highly promising task in drug discovery since it
can tremendously increase hit rates and suggest novel chemical scaffolds.
However, end-to-end learning with convolutional neural networks (CNNs)
has not been assessed for the task biological assay prediction despite
the success of these networks at visual recognition. We compared several
CNNs trained directly on high-throughput imaging data to a) CNNs trained
on cell-centric crops and to b) the current state-of-the-art: fully
connected networks trained on precalculated morphological cell features.
The comparison was performed on the Cell Painting data set, the largest
publicly available data set of microscopic images of cells with approximately
30,000 compound treatments. We found that CNNs perform significantly
better at predicting the outcome of assays than fully connected networks
operating on precomputed morphological features of cells. Surprisingly,
the best performing method could predict 32% of the 209 biological
assays at high predictive performance (AUC > 0.9) indicating that
the cell morphology changes contain a large amount of information
about compound activities. Our results suggest that many biological
assays could be replaced by high-throughput imaging together with
convolutional neural networks and that the costly cell segmentation
and feature extraction step can be replaced by convolutional neural
networks.