More than eight million people die from cancer worldwide each year. Current treat-ment such as chemotherapy and radiotherapy are still limited in terms of benefit/risk ratio. Nevertheless, engineered nanoparticles have opened new interesting perspectives in can-cerology, as emphasized by Brigger et al. since 2002. One of these promising solutions is based on the development of nanoparticles able to enhance the cytotoxic effect of radiother-apy. Nevertheless, the preclinical development in nano-medicine is slow, risky and expen-sive. Recently, Etheridge et al. (2013) highlighted the fact that many of the revolutionary nano-medicine technologies anticipated in the literature may be 20 or more years from clini-cal use. To speed up the preclinical development of medical engineered nanomaterial’s, we have designed an integrated computing platform dedicated to the virtual screening of nanostructured materials activated by X-ray making it possible to select nano-objects pre-senting interesting medical properties faster. That innovation gathers stochastic simulations and statistical modeling to estimate the impact of each design parameter describing the nano-object. That allows us to optimize composition factors in order to suggest one or few promising architectures regarding the medical purpose. The main advantage of this in silico design approach is to virtually screen a lot of possible formulations and to rapidly select the most promising ones. The platform can currently handle the accelerated design of radiation therapy enhanc-ing nanoparticles and medical imaging nano-sized contrast agents as well as the comparison between nano-objects and the optimization of existing materials. Other applications related to nano-medicines will be subject to further developments (e.g. photodynamic therapy).