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Harnessing computational technologies to facilitate antibody–drug conjugate development

Nature Chemical Biology volume 21, pages 1138–1147 (2025)Cite this article

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Abstract

Antibody–drug conjugates (ADCs) represent a powerful therapeutic approach for the treatment of a range of cancers. They merge the toxicity of known chemical agents with the specificity of monoclonal antibodies, thereby maximizing efficacy while minimizing adverse side effects. Although multiple ADCs have made it to the marketplace, their development remains a challenge in part owing to the lack of three-dimensional (3D) structural information that must account for the inherent flexibility of monoclonal antibodies as well as that of the drug payloads. This Perspective discusses computational methods, including machine learning and physics-based approaches, that could facilitate the interpretation of experimental data, make predictions on optimal solutions concerning drug conjugate linker type, conjugation sites and drug/antibody ratios and minimize the number of design iterations during ADC development. We explore examples of how the information content from physics-based 3D molecular modeling and simulations on model ADCs may facilitate ADC design.

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Fig. 1: Schematic summary of monoclonal antibody properties obtained through computational approaches.
Fig. 2: Schematic representation of aspects of ADC development.
Fig. 3: Monoclonal antibodies may be linked with payloads at multiple sites, thereby impacting the dynamical and structural properties of the ADC.
Fig. 4: SILCS identifies all possible binding sites of a warhead, avoiding the need for multiple, computationally demanding MD simulations.

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Acknowledgements

Financial support was from NIH R35 GM131710, and computational support was from the University of Maryland Computer-Aided Drug Design Center.

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Authors and Affiliations

  1. Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, USA

    Anastasia Croitoru & Alexander D. MacKerell Jr.

  2. SilcsBio LLC, Baltimore, MD, USA

    Asuka A. Orr & Alexander D. MacKerell Jr.

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  1. Anastasia Croitoru
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  2. Asuka A. Orr
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  3. Alexander D. MacKerell Jr.
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Contributions

A.A.O. modeled the monoclonal antibody and ADCs and performed and analyzed SILCS simulations. A.C. created ADC mutants and performed and analyzed canonical MD simulations. A.D.M. Jr conceptualized and supervised the entire project and wrote the first draft with all authors involved in revising the manuscript.

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Correspondence to Alexander D. MacKerell Jr..

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Competing interests

A.D.M. Jr is a cofounder and the CSO of SilcsBio LLC. A.A.O. was employed by SilcsBio LLC. A.C. has no competing interests.

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Nature Chemical Biology thanks Yutaka Matsuda and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Croitoru, A., Orr, A.A. & MacKerell, A.D. Harnessing computational technologies to facilitate antibody–drug conjugate development. Nat Chem Biol 21, 1138–1147 (2025). https://doi.org/10.1038/s41589-025-01950-z

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