Monoclonal antibodies have significantly advanced our ability to treat cancer, yet clinical studies have shown that many patients do not adequately respond to monospecific therapy. This is in part due to the multifactorial nature of the disease, where tumors rely on multiple and often redundant pathways for proliferation. Bi- or multi- specific antibodies capable of blocking multiple growth and survival pathways at once have a potential to better meet the challenge of blocking cancer growth, and indeed many of them are advancing in clinical development. ( 1) However, bispecific antibodies present significant design challenges mostly due to the increased number of variables to consider. In this perspective we describe an innovative integrated approach to the discovery of bispecific antibodies with optimal molecular properties, such as affinity, avidity, molecular format and stability. This approach combines simulations of potential inhibitors using mechanistic models of the disease-relevant biological system to reveal optimal inhibitor characteristics with antibody engineering techniques that yield manufacturable therapeutics with robust pharmaceutical properties. We illustrate how challenges of meeting the optimal design criteria and chemistry, manufacturing and control concerns can be addressed simultaneously in the context of an accelerated therapeutic design cycle. Finally, to demonstrate how this rational approach can be applied, we present a case study where the insights from mechanistic modeling were used to guide the engineering of an IgG-like bispecific antibody.