In recent years, deep learning models have resulted in outstanding breakthrough performances. However, many models behave as black boxes that can hide data biases, incorrect hypotheses or even software errors. In this talk, I will illustrate how interpretable deep learning models can achieve both high prediction accuracy and transparency. First, I will introduce multi-modal deep learning models that predict drug response while highlighting the genetic and chemical patterns that were more informative to make a prediction. I will also discuss how reinforcement learning approaches can facilitate the early phases of drug discovery and support the personalized design of new candidate compounds. Focusing next on T cell-based immunotherapies, I will present a model to predict the binding of T cell receptors and epitopes. This model can be coupled with an easy-to-use interpretable pipeline to extract the binding rules governing the T cell binding. These approaches are a first step towards the design and engineering of receptors of improved affinity. Finally, I will discuss how the integration of AI and mechanistic models is necessary to tackle many current computational challenges and enable the personalized design of new therapeutic interventions.