The first-generation models for drug discovery and clinical applications were mostly direct modifications of algorithms developed for NLP, computer vision, and other well-established application areas. However, deployment of these models revealed the significant mismatch between their basic assumptions and the needs of these new life sciences applications. Examples include challenging generalization scenarios, unknown biases in the collected data, and the inability of domain experts to validate model predictions. In my talk, I will illustrate some of these problems, and introduce our initial solutions to them.