"Next-Generation Antibody Design: Computational Approaches for De Novo Engineering, Affinity Maturation, and Personalized Therapeutics"

Novel and developed computational approaches in the field of antibody engineering have revolutionized and leveraged advanced algorithms through machine learning and detailed structural modeling in order to deeply facilitate the de novo design of new, effective therapeutic agents based on antibodies, affinity maturation, and stability optimization, significantly enhancing and accelerating the drug development processes. The main purpose of this paper is to clearly illustrate how advanced computational antibody design techniques, such as de novo engineering, affinity maturation, and personalized modeling, can deeply affect modern and precision therapeutics by enabling the rapid development of highly specific and potent monoclonal antibodies tailored to specific critical diseases. We observed the rapid development of highly specific and potent monoclonal antibodies tailored for some specific critical diseases—particularly cancers; for example, Human Epidermal Growth Factor Receptor 2 (HER2) or Receptor Tyrosine-Protein Kinase erbB-2- positive breast cancer or colorectal cancer. For autoimmune disorders such as Rheumatoid Arthritis (RA), Antikeratin, anticitrullinated peptides, anti-RA33, anti-Sa, and anti-p68 autoantibodies have been shown to have >90% specificity for RA. Regarding infectious diseases, the immunoglobulins lg M, lg A, and lg G are the key players in the response and the fight against COVID-19. The ultimate essential goal consists is to improve therapeutic efficacy, reduce off-target effects, and facilitate specific personalized treatment strategies that effectively address individual patients' molecular profiles. Machine learning algorithms like DeepMind’s AlphaFold have dramatically improved the accuracy of antibody–antigen structure prediction, facilitating the rapid identification of high-affinity binders. In one instance, a computational redesign of an anti-PD-1 (Immune checkpoint inhibitor) antibody enhanced its binding affinity and stability, leading to a more potent immune checkpoint inhibitor for cancer therapy. Moreover, the de novo design of bispecific antibodies has enabled simultaneous targeting of multiple tumor antigens, such as Cluster of Differentiation 3 (CD3) and Epidermal Growth Factor Receptor (EGFR), boosting immune activation in resistant cancers.