HyperNetworks have established themselves as an effective technique to rapidly adapt parameters in neural networks. Recently, HyperNetworks conditioned on descriptors of tasks have improved multi-task generalization in various domains, such as personalized federated learning and neural architecture search. Compelling results were achieved in few- and zero-shot settings, attributed to the increased information sharing by the HyperNetwork. With the rise of new diseases fast discovery of drugs is needed which requires proteochemometric models that can generalize drug-target interaction predictions in low-data scenarios. State-of-the-art methods apply a few fully-connected layers to concatenated learned embeddings of the protein target and drug molecule. In this work, we develop a task-conditioned HyperNetwork approach for the proteochemometrics problem in drug discovery. We show, that predictive performance can be improved or competitive over previous methods when model parameters are predicted based on the protein embedding for the fully-connected layers processing the molecule embedding. Furthermore, we extend our approach to also learn all parameters of a graph neural network as the molecular encoder using a particular weight initialization scheme. Our experiments with this extended architecture contribute new insights to the machine learning field, as HyperNetworks have rarely been applied to learn graph neural networks.