AI Antibody Developability Optimization Platform

Are prolonged drug development timelines, difficulties in antibody optimization, or elevated attrition rates from poor developability issues you are encountering? Our Creative Biolabs AI Platform for Antibody Developability Optimization addresses these challenges by delivering highly specific and stable antibody candidates. This platform expedites the drug discovery process using advanced machine learning and cutting-edge protein engineering technologies.

Ways Creative Biolabs' Platform Can Support Your Endeavor

Creative Biolabs' AI-powered platform provides a comprehensive solution for accelerating antibody development, delivering a higher probability of success and a more efficient path to the clinic. Our platform's core capability is the intelligent co-optimization of multiple developability properties simultaneously, such as affinity, solubility, stability, and immunogenicity, which are often difficult to balance using traditional methods. We provide you with refined antibody sequences that have been rigorously vetted in silico and validated in our wet labs, dramatically reducing the risk of late-stage failures and shortening your development timeline.

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Workflow

Why Choose Creative Biolabs?

Choosing Creative Biolabs means partnering with a team that delivers unparalleled accuracy, efficiency, and expertise. Our AI-driven platform offers distinct advantages that set us apart from traditional approaches and other computational solutions.

• Intelligent Closed Loop of AI and Wet Labs: Our core strength is the seamless integration of AI predictions with in-house wet lab validation. This intelligent feedback loop ensures our models are continuously improved by real-world data, making predictions highly accurate, reliable, and significantly de-risking your project.
• End-to-End Continuous Process: We provide a fully integrated, end-to-end solution from discovery to optimization. This approach eliminates the need for managing multiple vendors, streamlines the R&D process, and allows you to focus on your science.
• Predictive Accuracy and Efficiency: Our machine learning models, trained on proprietary and published data, virtually screen billions of candidates. This predictive power increases the hit rate and success rate, making the process faster and more efficient. Published Data confirms the high accuracy of our in silico predictions.
• Data-Driven Rational Design: Moving beyond empirical screening, we use big data and AI to inform every design decision. This rational design approach leads to superior molecules with predictable properties, based on deep insights into sequence-structure-function relationships.
• Expertise and Experience: Our interdisciplinary team of experienced biologists, computational scientists, and drug discovery experts ensures the highest quality service by tackling complex challenges from both theoretical and practical perspectives.
• Customization and Flexibility: Our highly customized services are tailored to your specific project needs, whether for a single antibody or a large library, avoiding a one-size-fits-all approach.

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Introduction of the AI Antibody Developability Optimization Platform

Antibodies are pivotal in contemporary medicine, serving as the most extensive class of biotherapeutics with applications spanning from oncology to immunology. Yet, the transition from an antibody candidate to a therapeutic drug is riddled with challenges. Conventional antibody discovery methods are often slow, expensive, and inefficient, with high failure rates at advanced stages due to inadequate developability, encompassing crucial attributes like stability, solubility, and immunogenicity. The advent of artificial intelligence profoundly alters this scenario. AI now addresses significant obstacles in antibody development, utilizing sophisticated computational models to explore vast sequences and predict essential properties. This approach supports the rational design of promising molecules from the outset, and when integrated with wet lab data, forms a robust feedback loop that accelerates and enhances biologics discovery.

Fig.1 Information on antibody sequences and structures related to developability attributes.¹

Platform Technology & Infrastructure

The Creative Biolabs platform is built on a robust technological foundation designed for speed, scalability, and precision. Our infrastructure integrates multiple cutting-edge technologies to deliver a seamless and powerful service.

• Advanced Machine Learning Models: We utilize a suite of deep learning and machine learning models, including generative AI and Transformer-based models, for de novo antibody design and sequence optimization.
• Proprietary Biologics Database: Our models are trained on an extensive and proprietary database of antibody sequences and their associated experimental data, which significantly enhances our predictive accuracy.
• High-Throughput In Silico Screening: The platform leverages cloud-based computing to screen millions of antibody candidates in a fraction of the time required by traditional methods.
• Predictive Analytics for Developability: We employ sophisticated predictive analytics to evaluate critical developability metrics, providing a quantitative score for properties like aggregation, viscosity, and immunogenicity risk.

Creative Biolabs' AI Antibody Developability Optimization Platform is a robust and cohesive solution aimed at transforming and accelerating the discovery of top-tier biotherapeutics. By integrating an intelligent closed-loop system of AI with wet lab validation, we offer a data-driven, streamlined, and precise process. This platform yields superior antibody candidates with enhanced developability, substantially minimizing development risks and timelines. To explore how our platform can cater to your specific needs, connect with our team of experts ready to assist and discuss your project.

Reference

1. Santuari, Luca, et al. "AI-accelerated therapeutic antibody development: practical insights." Frontiers in Drug Discovery 4 (2024): 1447867. DOI: 10.3389/fddsv.2024.1447867 Distributed under Open Access license CC BY 4.0, without modification.