Deep learning is showing increasing promise for drug discovery . One of its key applications is virtual screening , whereby large commercial libraries (usually consisting of 10 3 -10 9 molecular candidates) are prioritized for prospective wet-lab experiments . A key bottleneck of deep learning, however, is the need for sufficient training data (preferably 10 3 molecules and above ). Unfortunately, available ligand-target interaction data are often limited in size and structural diversity -factors that might hamper the usefulness of deep learning models in practice .