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.