Despite these limitations, neural networks shine for their 'adaptable' nature; e.g., to different types of inputs and modeling tasks , and to manipulation of internal model representations -which unlocks novel avenues compared to traditional methods. Hence, deep learning for drug discovery bears incredible potential to extract relevant information from complex molecular systems and perform tasks that are not accessible via traditional computational approaches. Its application in low-data regimes faces unique challenges that demand innovative approaches.