Silica Corpora
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About
Silica Corpora is a De Novo antibody therapy platform that designs and optimizes therapeutic antibodies targeting priority diseases using a proprietary AI-based platform. Their modular system leverages AI-machine learning and data analytics to work exclusively with amino acid sequences. The platform includes modules such as Generator, Discriminator, Optimizer, and Ep-Mapper, which facilitate the design, optimization, and characterization of antibody candidates. It takes amino acid sequences of therapeutic targets as input and outputs amino acid sequences of novel antibodies or antibody mutants with improved biochemical properties.
Platform
Features
• data analytics
• ep-mapper accurately predicts epitopes
• optimizer module adapts candidates
• discriminator module predicts antibody properties
• generator module delivers antibody candidates
• precision antibody design
• ai-machine learning
• de novo antibody therapy platform
FAQs
What is the current status of the company’s compliance with key regulatory bodies like the FDA or EMA?
Currently, Silica Corpora is not a subject for any regulatory regulations, as there are no regulations for AI solutions for drug discovery. Being a software company allow us to be extent of strict regulatory and safety measures concerning the handling of hazardous substances or animal trials, which will always rely on our collaborators or external parties.
What safety, security, and governance layers does SC’s AI technology include to meet biotech and pharma standards?
The therapeutic candidates designed by Silica Corpora, are extensively tested in the same way as any other candidates, therefore, ensuring safety and efficacy. All the molecules that we design with our AI algorithms are then synthesized and tested extensively in a wet-lab for a wide variety of properties like toxicity, immunogenicity, therapeutic effect, possible side-effects, etc.
How does the company mitigate risks like algorithmic hallucinations, and what proprietary guardrails are in place?
Since molecules are extensively validated in the laboratory, algorithmic hallucinations will be always found. Thus, even if some parts of an antibody (some amino acid sequence regions) were generated due to hallucinations, it does not necessarily mean worse quality, it can be the opposite: some new important properties are brought by those “hallucinations”.
How does the system work in a high level perspective?
We give as an input for the platform amino acid sequences of therapeutic targets and, depending on the task, amino acid sequences of existing and characterized antibody candidates. The platform will deliver back amino acid sequences of novel antibodies in any kind of antibody format (e.g. single domain antibodies, scFVs, etc.) against the therapeutic target of interest, or antibody mutants with improved biochemical properties.
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