-- With the rapid pace of biopharmaceutical R&D, the industry's demand for efficient and predictable antibody engineering technologies continues to grow.
The upgraded platform integrates four major capabilities—antibody humanization, affinity optimization, developability enhancement, and bispecific antibody design—providing pharmaceutical companies with a faster and more reliable technical route in early-stage development.
Experts from Creative Biolabs pointed out that traditional antibody optimization often relies on extensive experimental screening, which is characterized by a long development cycle, high costs, and a relatively high risk of failure. However, AI platforms based on deep learning and structure prediction can identify potential risks at the early stage of sequence design, significantly reducing the cost of trial and error. "AI not only speeds up the process but also makes early-stage decisions more reliable," said the company's antibody engineering director. "Especially in the prediction of immunogenicity of candidate antibodies, the assessment of developability, and the judgment of affinity change trends, the advantages of AI are very obvious."
In terms of antibody humanization, Creative Biolabs' AI antibody humanization platform achieves intelligent integration of framework regions and CDRs through structural modeling, sequence analysis, and immunogenicity algorithms. This reduces the common affinity loss in traditional CDR transplantation and enhances the manufacturability of the sequence.
To address the issue of insufficient affinity, the AI antibody affinity optimization platform utilizes generative models and molecular dynamics predictions to screen through a vast number of mutation combinations, enabling customers to quickly obtain sequences with high affinity and specificity. Project data shows that for some complex targets, AI has helped reduce the optimization cycle to one-third of the original.
In terms of the development risk management of candidate molecules, such as stability, viscosity, and aggregation tendency, the AI antibody developability optimization platform can predict key process parameters before experiments, helping pharmaceutical companies eliminate sequences that are difficult to develop at an early stage and reducing the possibility of subsequent process rework.
With the rapid growth of bispecific drugs, the problems of chain mismatch and stability caused by structural complexity have also attracted much attention. Creative Biolabs has launched an AI bispecific antibody design platform that can evaluate chain pairing methods, interface stability and structural rationality among millions of combinations, and provide sequences that can be directly used for experimental verification, significantly improving the success rate of bispecific antibody design.
Experts from the technical team of Creative Biolabs stated, "The significance of AI is not merely to replace experiments, but to transform research and development from passive trial and error to active design. We hope to help our clients achieve a higher success rate from the very first version of the sequence."
Industry insiders generally believe that with the continuous integration of AI models and validation experiments, antibody engineering will enter a more efficient and controllable stage. Creative Biolabs stated that it will continue to expand the scale of the data set, iterate the performance of the algorithm, and enhance the ability of experimental validation, providing more intelligent R&D support for global biopharmaceutical enterprises.
Contact Info:
Name: Candy Swift
Email: Send Email
Organization: Creative Biolabs
Website: https://ai.creative-biolabs.com/
Release ID: 89178328
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