MINI REVIEW article
Front. Drug Discov.
Sec. In silico Methods and Artificial Intelligence for Drug Discovery
Volume 5 - 2025 | doi: 10.3389/fddsv.2025.1628789
AI-Driven Innovation in Antibody-Drug Conjugate Design
Provisionally accepted
- 1Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, D.C., United States
- 2Artificial Intelligence and Drug Discovery (AIDD) Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Washington DC, United States
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Antibody-drug conjugates (ADCs) represent a mechanistically defined class of targeted therapeutics that combine monoclonal antibodies with cytotoxic payloads to achieve selective delivery to antigen-expressing carcinoma cells. Conventional ADC development has primarily relied on empirical screening and structure-based design, often limited by incomplete structural information, non-systematic linker-payload selection, and constraints in experimental throughput. Computational methods, including artificial intelligence and machine learning (AI/ML) are increasingly being integrated into ADC discovery and optimization workflows (i.e. AI-driven ADC Design) to address these limitations. This review is organized into six sections: (1) the progression from traditional modeling approaches to AI-driven design of individual ADC components; (2) the application of deep learning (DL) to antibody structure prediction and identification of optimal conjugation sites; (3) the use of AI/ML models for forecasting pharmacokinetic properties and toxicity profiles; (4) emerging generative algorithms for antibody sequence diversification and affinity optimization;(5) case studies demonstrating the integration of computational tools with experimental pipelines, including systems that link in silico predictions to high-throughput validation; and (6) persistent challenges, including data sparsity, model interpretability, validation complexity, and regulatory considerations. The review concludes with a discussion of future directions, emphasizing the role of multimodal data integration, reinforcement learning (RL), and closedloop design frameworks to support iterative ADC development.
Keywords: AI/ML (Artificial Intelligence/Machine Learning), antibody-drug conjugate (ADC), AlphaFold 3, Neural ODEs, Generative & algorithmic design
Received: 15 May 2025; Accepted: 02 Jun 2025.
Copyright: © 2025 Noriega and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Heather A Noriega, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, D.C., United States
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