Andreas Evers, Associate Scientific Director, Antibody Discovery & Protein Engineering, Global Research & Development Discovery Technology, Merck Healthcare KGaA
Significant advancements have been achieved in predicting the developability properties of antibodies through AI/ML methods, enabling the in silico design of extensive sequence sets. Although these techniques are effective for standard monospecific antibodies, they frequently fall short when applied to more complex next-generation antibodies, such as multispecifics and antibody-drug conjugates (ADCs). This presentation will highlight key lessons learned and specific applications of physico-chemical property prediction strategies for evaluating and optimizing bispecifics and ADCs.