Computational lithography is nowadays playing an indispensible role in improving the imaging performance of optical lithography systems. This paper develops a new and powerful approach to computational lithography by introducing an information theoretical channel modeling in partially coherent lithography systems. A statistical model is built up based on the lithography imaging model to characterize the information transfer between the mask and print images. Then, this paper calculates the optimal information transfer (OIT) in partially coherent lithography systems, and derives the theoretical limit of image fidelity for optical proximity correction (OPC), which is used extensively in computational lithography. Finally, the proposed information theoretical approaches are applied to improve the OPC solutions obtained by the gradient-based algorithm. A set of simulations are provided to verify the proposed information theoretical model and approaches.