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Deep Genomics

AI platform for RNA biology and genetic medicine drug discovery
Biotech
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Deep Genomics

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Deep Genomics
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Description

Deep Genomics is an AI-first TechBio organization that uses proprietary artificial intelligence platforms to decode the complexity of RNA biology and develop therapeutic candidates for genetic diseases. The company's mission is to program therapies for any gene and any genetic condition, leveraging the power of AI where traditional biological approaches are too slow and costly.

At the core of the company's capabilities is BigRNA, the world's first AI foundation model for RNA biology and therapeutics. Unlike task-specific AI models in drug discovery, BigRNA has learned fundamental aspects of biology and chemistry that enable it to be applied across a wide range of tasks — from target identification and novel mechanism discovery to designing therapeutic candidates and predicting molecule-target interactions. The model is trained to predict RNA expression at sub-gene resolution, covering splicing, polyadenylation, and other transcriptional regulatory events not captured by standard gene expression approaches.

BigRNA operates across a wide range of species, tissues, cell models, and RNA therapeutic modalities, including oligonucleotides, DNA editing, RNA editing, and mRNA. The platform is fueled by diverse proprietary datasets, continuous machine learning engineering advances, and the ongoing scientific work of the Deep Genomics team. The company is currently developing BigRNA+, an expansion intended to address more complex genetic diseases and discover new biology.

Deep Genomics also conducts Project Saturn, the world's first drug design system built around a molecular biology AI. In this initiative, the platform evaluates over 69 billion oligonucleotide molecules against one million targets in silico, generating a library of experimentally verified compounds designed to manipulate cell biology. The platform improves continuously through closed-loop AI learning from both experimental successes and failures.

Use cases
  • Identifying novel RNA biology targets for genetic disease drug discovery programs
  • Predicting tissue-specific regulatory mechanisms of RNA expression across human genomes
  • Designing steric-blocking oligonucleotide therapeutic candidates for rare genetic diseases
  • Evaluating molecule-target interactions across oligonucleotide and RNA therapeutic modalities
  • Assessing effects of genetic variants on RNA splicing, polyadenylation, and expression
  • Screening billions of antisense oligonucleotide compounds against disease targets in silico
  • Predicting RNA-binding protein and microRNA binding sites for therapeutic design
  • Designing surrogate molecules for in vivo testing of therapeutic candidates
  • Discovering new biological mechanisms in complex genetic diseases using foundation model AI
  • Generating experimentally verified compound libraries through AI-guided drug design systems
Features
BigRNA foundation model, RNA target identification, Therapeutic candidate design, Molecule-target interaction prediction, Splicing mechanism prediction, Oligonucleotide screening, Closed-loop AI learning, Multi-modality RNA support, Proprietary genomic datasets, Project Saturn drug design

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