Abstract: To facilitate drug discovery, deep learning models have been developed for the prediction of inhibitors of various targets including kinases, achieving high prediction performances. Nonetheless the ability of deep learning on low-sample targets (<100 known active molecules) has not been adequately tested. Leveraging the good activity prediction capability of a recently emerged deep convolutional neural network MolMapNet method under knowledge-based molecular representations, this study developed multi-task MolMapNet models for inhibitory activity prediction of 19 low-sample kinases and 43 higher-sample kinases of 6 kinase subfamilies. The developed multi-task MolMapNet models for all low-sample and higher-sample kinases significantly enhanced the activity prediction performance over the single-task models. The activity prediction indicators such as R² values were in the good performance ranges of 0.651 3-0.749 8 for most kinases. These suggest the usefulness of the multi-task transfer learning strategy in activity prediction of low-sample targets.