Antibody-drug conjugate engineering: from design to efficacy and safety

Abstract

Antibody–drug conjugates (ADCs) represent a rapidly expanding class of targeted cancer therapeutics that combine the high selectivity of monoclonal antibodies with the potent cytotoxic activity of small-molecule drugs. Their clinical success relies on the simultaneous optimization of multiple interdependent parameters, including antigen selection, antibody engineering, linker chemistry, and payload pharmacology, which limits the effectiveness of traditional empirical approaches. In this context, recent advances in artificial intelligence (AI) and computational biophysics are transforming the rational design of ADCs. AI enables large-scale integration of genomic, transcriptomic, and proteomic data to identify tumor-selective, surface-accessible antigens and to support patient stratification strategies. Deep learning models enhance antibody engineering by predicting structure, affinity, stability, and developability, while generative algorithms accelerate affinity maturation and specificity optimization. Computational prediction of linker design and conjugation sites improves plasma stability, controlled payload release, and drug-to-antibody ratio, whereas graph-based neural networks facilitate the selection and optimization of cytotoxic payloads with favorable potency, membrane permeability, and bystander effects. Complementary molecular dynamics simulations provide atomistic insight into antibody conformation, linker flexibility, and payload interactions, enabling a deeper mechanistic understanding of ADC stability and function. At the translational level, hybrid physiologically based pharmacokinetic–AI models and digital twin simulations enable virtual evaluation of tumor penetration, systemic exposure and safety, ultimately supporting dose optimization and more efficient clinical development. Although this review places particular emphasis on the application of these approaches in oncology, emerging ADC strategies beyond cancer—including autoimmune, neurodegenerative, cardiovascular, and metabolic diseases—are also discussed. Together, these computational strategies represent a convergence of machine intelligence and molecular biology that is poised to fundamentally transform ADC development, enabling safer, more precise, and effective therapies across oncology and beyond.