Stemming from these observations, this minireview delves into existing deep learning approaches for drug discovery with limited data availability, with a focus on bioactivity prediction (i.e., how to predict if and how a ligand will interact with one or more macromolecular targets) and de novo design (i.e., how to design novel bioactive molecules from scratch, Fig. ). While extensive reviews on how machine learning is employed for drug discovery exist , , this minireview provides a structured overview of deep learning for lowdata drug discovery, with special emphasis on recent approaches, their advantages and limitations, and the opportunities ahead.