PhD thesis work regarding development of generative, probabilistic models for protein sequences with application in protein and biologics design. More specifically, to devise a machine learned, computational framework for optimization and design of antibodies. The framework will leverage ideas from natural language processing, data from next-generation sequencing technologies and molecular simulations, as well as inductive biases from chemistry and physics.