Discovering new physical products and processes often demands enormous experimentation and expensive simulation. To design a new product with certain target characteristics, an extensive search is performed in the design space by trying out a large number of design combinations before reaching to the target characteristics. However, forward searching for the target design becomes prohibitive when the target is itself moving or only partially understood. To address this bottleneck, we propose to use backward prediction by leveraging the rich data generated during earlier exploration and construct a machine learning framework to predict the design parameters for any target in a single step. This poses two technical challenges: the first caused due to one-to-many mapping when learning the inverse problem and the second caused due to an user specifying the target specifications only partially. To overcome the challenges, we formulate this problem as conditional density estimation under high-dimensional setting with incomplete input and multimodal output. We solve the problem through a deep hybrid generative-discriminative model, which is trained end-to-end to predict the optimum design.