Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.
Technologies to develop and evolve the function of proteins and, in particular, antibodies have developed rapidly since the introduction of phage display. Importantly, it has become possible to identify molecules with binding properties that cannot be found by other means. A range of different approaches to create general libraries that are useful for the selection of such molecules specific for essentially any kind of target have emerged. We herein review some of the most prominent approaches in the field and in particular discuss specific features related to the development of antibody libraries based on single antibody framework scaffolds. This approach not only permits identification of a range of specific binders, but also facilitates further evolution of initially derived molecules into molecules with optimised functions.
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