Developability

Clinical drug development remains associated with a high failure rate, primarily to lack of efficacy, unmanageable toxicity, and suboptimal drug-like properties. These data indicate that, despite extensive optimization workflows, current strategies do not fully capture all determinants of clinical success.

Advances in antibody discovery technologies, engineering strategies, and manufacturing platforms have enabled the rapid expansion of antibody-based therapeutics across oncology, immunology, infectious diseases, and rare disorders. As discovery pipelines grow larger and more complex, however, it has become increasingly clear that strong binding affinity and biological activity alone are not sufficient predictors of downstream success.

Explore how computational predictors like SCM, TAP, and AlphaFold support therapeutic antibody development through AI-driven workflows and in silico tools.