Antibody-Drug Conjugation Design

Antibody-drug conjugates (ADCs) have emerged as a promising class of biopharmaceuticals for the treatment of various diseases, including cancer. These conjugates combine the specificity of monoclonal antibodies with the cytotoxic potency of small-molecule drugs, allowing for precise targeting of diseased cells while minimizing off-target effects. However, the design of ADCs presents significant challenges due to the complexity of the conjugation process, the need for optimal payload delivery, and the requirement for stability and specificity.

At CD ComputaBio, we are revolutionizing the field of enzyme inhibitor design through our cutting-edge AI-aided antibody-drug conjugation design services. With our state-of-the-art computational tools and deep expertise in molecular modeling, we offer a comprehensive suite of services aimed at accelerating the development of highly effective, targeted therapeutics.

Our Services

Fig 1: New Technologies Bloom Together for Bettering Cancer Drug Conjugates

Conjugation Site Prediction

Our proprietary algorithms take into account structural, electrostatic, and steric considerations to predict conjugation sites that maximize target binding while minimizing potential interference with antibody functionality.

Fig 2: Advances and limitations of antibody drug conjugates for cancer

Linker Design and Evaluation

We utilize AI algorithms to rationally design and evaluate linkers for ADCs, ensuring optimal stability, controlled drug release, and minimal off-target effects.

Fig 3: Our services of antibody-drug conjugation design

Pharmacokinetic Profiling

Our computational approaches enable comprehensive pharmacokinetic profiling of ADC candidates, predicting key parameters such as clearance, distribution, and half-life.

Fig 4: Our services of antibody-drug conjugation design

Off-Target Assessment

Our AI-aided simulations enable the proactive identification of off-target liabilities, guiding design modifications to improve specificity and minimize toxicity.

Our Analysis Methods

We rely on a diverse array of computational techniques and software platforms to power our AI-aided antibody-drug conjugation design services:

Methods Description
Molecular Docking Our advanced docking algorithms facilitate the exploration of binding interactions between antibodies and drug payloads, guiding the rational design of conjugation sites.
Molecular Dynamics Simulations By simulating the behavior of ADC components at the atomic level, we gain insights into linker stability, conformational dynamics, and interactions within physiological environments.
Pharmacokinetic Modeling Leveraging AI-enabled pharmacokinetic models, we accurately predict the absorption, distribution, metabolism, and excretion of ADCs, informing critical decisions in drug development.
Machine Learning-Based Prediction Through the integration of machine learning approaches, we extract meaningful patterns from complex biological data, enhancing the predictive accuracy of our design strategies.

Our Capabilities

CD ComputaBio's AI-aided antibody-drug conjugation design services leverage cutting-edge computational platforms to address these challenges, enabling rapid and accurate prediction of conjugation sites, assessment of linker stability, and evaluation of pharmacokinetic properties. By harnessing the power of artificial intelligence and molecular modeling, we streamline the ADC design process, significantly reducing the time and resources required for preclinical development. If you are interested in our services or have any questions, please feel free to contact us.


  • Jin Y, Zakeri S E, Bahal R, et al. New technologies bloom together for bettering cancer drug conjugates[J]. Pharmacological Reviews, 2022, 74(3): 680-711.
  • Mckertish C M, Kayser V. Advances and limitations of antibody drug conjugates for cancer[J]. Biomedicines, 2021, 9(8): 872.


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