  Machine learning shows great potential in virtual screening for drug
discovery. Current efforts on accelerating docking-based virtual screening do
not consider using existing data of other previously developed targets. To make
use of the knowledge of the other targets and take advantage of the existing
data, in this work, we apply multi-task learning to the problem of
docking-based virtual screening. With two large docking datasets, the results
of extensive experiments show that multi-task learning can achieve better
performances on docking score prediction. By learning knowledge across multiple
targets, the model trained by multi-task learning shows a better ability to
adapt to a new target. Additional empirical study shows that other problems in
drug discovery, such as the experimental drug-target affinity prediction, may
also benefit from multi-task learning. Our results demonstrate that multi-task
learning is a promising machine learning approach for docking-based virtual
screening and accelerating the process of drug discovery.
