Antibody Engine
A next-gen pLLM platform for antibody therapies
We build proprietary pLLMs through a distinct and disciplined approach. By integrating project-specific data directly from in vitro workflows, we can achieve exceptionally accurate results with minimal input.
We can create successful candidates and modify them at the same time. Our protein-LLM only requires amino acid sequences as an input. Using trained AI models on amino acid sequences we can generate more quality antibodies from scratch, without using in vitro nor in vivo methods.
At the intersection of cutting-edge biotechnology and advanced artificial intelligence, our process leverages state-of-the-art technology to revolutionize antibody drug design. By integrating machine learning algorithms with high-throughput data analysis, we accelerate the discovery and optimization of novel therapeutics.
Our Process
Our models process vast quantities of protein data, enabling us to achieve significant insights from only 100 project-specific candidates. The integration of project-specific data directly from in vitro workflows enhances accuracy compared to traditional 3D structure-based methods.
Our Exclusivity
Our Protein Large Language Models (pLLMs) are trained on millions of unique protein-protein interactions and fine-tuned on thousands of antibody-antigen interactions for antibody-specific tasks.
Our Holistic Approach
Silica Corpora stands out in the crowded field of biotechnology by using a holistic, AI-driven approach to design antibodies. We design successful candidates by considering the system in its entirety rather than just focusing on a specific property.
Generator
Our White Paper highlights the exceptional capabilities of Silica Corpora's Generator. Analyzing market-available antibody-antigen pairs, the Complementary-Determining Regions (CDRs) designed by our platform exhibit a striking mean similarity of 85% to 98% compared to established therapeutic counterparts. This remarkable level of similarity underscores the precision and effectiveness of our technology in creating antibodies with strong therapeutic potential.
Discriminator
Our AI solution demonstrates remarkable efficiency and accuracy in antibody screening with 98% success rate. In a test involving Antibody-Antigen pairs together with non-binders, our platform precisely distinguishes binding antibodies from non-binders in just hours. The predicted probabilities for binders are heavily skewed towards 1.0, while non-binders show probabilities near 0.0. This performance underscores the robustness of our AI in identifying effective antibodies.
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Ep-Mapper
The Ep-Mapper accurately predicts epitope amino acids for a given antibody of interest. Validation on a subset of 60 therapeutic antibodies demonstrated striking precision 0.68 and recall 0.71 values, which surpasses the performance of all currently known methods.
Optimizer
The Optimizer tool has demonstrated its effectiveness in enhancing the performance therapeutic antibodies. Our algorithms successfully optimize sequences, producing candidates with stronger predicted binding probability and relative affinity. The optimization process does not rely solely on generating sequences similar to existing therapeutics, but rather explores diverse sequence variations that maintain or improve functionality.
From millions to hundreds of candidates in days.
