  Deep learning methods such as multitask neural networks have recently been
applied to ligand-based virtual screening and other drug discovery
applications. Using a set of industrial ADMET datasets, we compare neural
networks to standard baseline models and analyze multitask learning effects
with both random cross-validation and a more relevant temporal validation
scheme. We confirm that multitask learning can provide modest benefits over
single-task models and show that smaller datasets tend to benefit more than
larger datasets from multitask learning. Additionally, we find that adding
massive amounts of side information is not guaranteed to improve performance
relative to simpler multitask learning. Our results emphasize that multitask
effects are highly dataset-dependent, suggesting the use of dataset-specific
models to maximize overall performance.
