This work showcases how ADC biotransformation can be routinely characterized using automated commercial software tools. Interest in ADC biotransformation in the analytical community has been increasing as the number of ADC candidates continues to grow. However, lack of streamlined software and analytical processes for high resolution-accurate mass data has been impeding wide application of this approach to impact drug development. In this study, we used two ADC examples with structurally different linker-payloads to demonstrate a novel, automated, data processing workflow. Combining linker-payload structural cleavage prediction and additional common biotransformation, we can apply automated peak matching and rapidly propose new biotransformation structures. This high-throughput workflow is particularly beneficial for early drug discovery where biotransformation insights can guide lead selection through iterative design.