Modeling Industrial ADMET Data with Multitask Networks
This incremental study helps drug discovery researchers optimize virtual screening models by clarifying when multitask learning is effective for ADMET prediction.
The researchers investigated multitask neural networks for industrial ADMET data modeling, finding they provide modest benefits over single-task models (with smaller datasets benefiting more) and that performance improvements are highly dataset-dependent.
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.