Generative models such as diffusion models have recently gained a lot of attention. Especially conditional generative models show promising approaches to solve complex inverse problems where observations are only indirectly available. This talk will highlight recent advances in generative models and their application to solve inverse problems in drug discovery. In particular, the possible applications and future potential in the field of structure-based drug design will be discussed.