Silica Corpora's AI platform designs and optimizes therapeutic antibodies. It uses a modular system leveraging AI, machine learning, and data analytics to work exclusively with amino acid sequences. The platform includes a Generator module to create antibody candidates, a Discriminator module to predict antibody properties, an Optimizer module to adapt candidates to specific profiles, and an Ep-Mapper module for epitope prediction. The platform aims to increase efficiency, precision, and control in antibody design, reducing the need for experimental candidates and accelerating drug discovery.
• ep-mapper
• optimizer
• discriminator
• generator
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. However, we are aware of the possibility of new regulations and we are following its development ([from FDA](https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development) and [EMA](https://www.ema.europa.eu/en/use-artificial-intelligence-ai-medicinal-product-lifecycle)) with the purpose of being prepared for their approval.
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. If some of the molecular properties are not satisfactory, in the following iterations, we optimize the molecule with our AI tooling as well, and then the wet-lab validation cycle is repeated.
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”. In the end, the most important results are wet-lab results, therefore, we care mostly about the pre-clinical performance of the designed molecules proved by wet-lab experiments.
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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