Antibody Drug Design

Antibody-drug design is a critical area in modern biomedicine, particularly for treating cancers, autoimmune diseases, and infectious diseases. Monoclonal antibodies have shown significant therapeutic success. Traditionally, antibody design involved extensive lab work and animal testing, which is time-consuming and costly.

Creative Biogene offers comprehensive AI-driven antibody drug design services. Our advanced deep-learning models accurately predict antibody structures, optimize affinity and target specificity, and forecast biological efficacy. This enables our clients to swiftly develop highly effective antibody drugs. Each stage is complemented by wet experiment validation, ensuring thoroughness and reducing risk, thereby enhancing success rates.

We support antibody drug design through two methods:

Library Screening Based on Embedding Large Models

Our sequence library screening service integrates a variety of cutting-edge models, including LSTM (Long Short-Term Memory networks), Transformers, BERT (Bidirectional Encoder Representations), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs). By using these models for deep optimization and self-supervised learning, we achieve significant improvements in prediction accuracy through precise sequence generation and prioritization.

You will get an end-to-end antibody development solution that includes sequence design, library construction, screening, and experimental validation if you choose our service. Your efforts to develop antibody drugs will be fully supported by this all-encompassing approach, which dramatically increases research and development efficiency and success rates while drastically lowering development costs and risks.

Process Details

Library Construction: From specific sources, such as human immune libraries, B cells or antibody samples are collected. These samples are transcribed into cDNA and inserted into expression vectors to form an antibody display library.
Screening: The constructed antibody display library is exposed to immobilized target antigens. Through multiple rounds of panning, antibodies binding to the target antigen are enriched.
Selection and Characterization: Antibody clones binding to the target antigen are selected. These clones undergo sequence analysis and characterization using techniques such as SPR or ELISA to identify candidates with the desired affinity and specificity.
Wet Lab Validation: Beyond sequence generation, we provide experimental validation to ensure that the generated antibodies function as intended and exhibit the required stability.

Target-Based de novo Design

Lead optimization and de novo design are the two main focuses of our AI-driven antibody drug development service. We enable the "zero-shot" design of antibody CDRs for particular targets by utilizing generative AI models, which screen large libraries of variants for the best binding affinity. We have demonstrated that our models are highly effective in designing every CDR in the antibody-heavy chain through SPR characterization. Furthermore, our high-throughput screening assay, which is validated with SPR data, produces quantitative binding affinity scores for multiple antibody variants. Large language models are trained with this data to predict binding affinities for novel variants, paving the way for the development of antibodies with improved characteristics. Our service offers a comprehensive approach to developing novel drugs by expediting and improving antibody drug development through co-optimizing multiple characteristics.

Figure 1 illustrates how deep learning models, trained on antibody-antigen interactions and supported by high-throughput experiments, can design antibodies that effectively bind to previously unseen antigens without additional affinity optimization. (doi: 10.1101/2023.01.08.523187) Figure 2. These deep learning models, trained on antibody-antigen interactions and complemented by high-throughput experiments, can design antibodies that bind to antigens unseen by the models without requiring further affinity optimization. (Shanehsazzadeh, A., et al., 2024)

Process Details

Database Integration: Compile and consolidate relevant data from various biological databases to ensure comprehensive and accurate input for AI models, incorporating resources.
Target Analysis: Conduct a thorough analysis of target proteins to understand their structural and functional properties, enabling precise predictions and optimizations.
Algorithm Selection: Choose the most appropriate machine learning and AI algorithms based on the specific requirements of the protein analysis, considering options.
Sequence Generation: Generate and refine protein sequences using advanced AI techniques, ensuring they meet desired specifications and exhibit optimal performance characteristics.

Advantages of Our Antibody Drug Design Service

Advanced Deep Learning Algorithms: Achieve precise affinity and specificity enhancement.
High-Throughput Screening: Quickly identify the best candidates.
Robust Wet Lab Validation: Ensure functional stability and effectiveness.

Opting for Creative Biogene for your antibody drug development will lead to reduced costs, increased efficiency, and improved success rates. Reach out to us today to elevate your research and development initiatives with our comprehensive, cutting-edge solutions.

* For research use only. Not intended for any clinical use.

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