Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China.
Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China.
Semin Cancer Biol. 2023 Oct;95:13-24. doi: 10.1016/j.semcancer.2023.06.005. Epub 2023 Jun 22.
Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.
治疗性抗体是最大的生物治疗药物类别，已成功用于治疗人类疾病。然而，抗体药物的设计和发现仍然具有挑战性和耗时。最近，人工智能技术对抗体设计和发现产生了巨大影响，导致抗体发现、优化和可开发性方面取得了重大进展。本综述总结了主要的机器学习 (ML) 方法及其在抗体结构和抗原界面/相互作用的计算预测因子，以及抗体可开发性评估方面的应用。此外，本综述还介绍了基于 ML 的治疗性抗体在临床前和临床阶段的现状。虽然仍然存在许多挑战，但 ML 可能为完全基于计算的抗体设计的未来方向提供新的治疗选择。
