Deep generative models are at the core of research in artificial intelligence, especially for unlabelled data. They have achieved remarkable performance in domains including computer vision, natural language processing, speech recognition, and audio synthesis. Very recently, deep generative models have been applied to broader domains, e.g. fields of science including the natural sciences, physics, chemistry and molecular biology, and medicine. However, deep generative models still face challenges when applied to these domains from which arise highly structured data. This workshop aims to bring experts from different backgrounds and perspectives to discuss the applications of deep generative models to these data modalities. The workshop will put an emphasis on challenges in encoding domain knowledge when learning representations, performing synthesis, or for prediction purposes. Since evaluation is essential for benchmarking, the workshop will also be a platform for discussing rigorous ways to evaluate representations and synthesis.