Abstract A main challenge to drug development in immuno-oncology (IO) is the ability to translate biological intent into molecular designs that enable clear antitumor efficacy in patients. This constraint is especially evident for multispecific and immune-engaging biologics, where clinical activity is highly sensitive to molecular architecture, affinity balance, and pharmacologic exposure. Molecular engineering has therefore emerged as a central enabler of next-generation IO medicines. Modern immune modulators function as integrated molecular systems, in which coordinated control of binding geometry, signaling strength, and persistence determines immune activation, tolerability, and therapeutic index. As multispecific formats expand the design space combinatorially, empirical screening alone becomes insufficient to navigate the trade-offs required for translational success. Artificial intelligence (AI) is accelerating the shift from empirical discovery toward design-driven IO development. AI-enabled approaches support rapid exploration of molecular architectures, prediction of structure–function relationships from early sequence data, and early identification of developability risks, enabling faster and more systematic learning cycles. When integrated with experimental validation, these tools allow immune logic to be encoded intentionally into molecular design. This presentation will introduce a conceptual framework for molecular engineering in IO, positioning AI as a design accelerator that expands what is biologically and therapeutically achievable. Together, molecular engineering and AI are reshaping how immune modulators are conceived, designed, and advanced. Citation Format: David M. Reese. Molecular engineering in the era of artificial intelligence abstract. In: Proceedings of the AACR Immuno-Oncology Conference (AACR IO): Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2026 Feb 18-21; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2026;14(2 Suppl):Abstract nr IA03.
David Reese (Wed,) studied this question.