AI is changing everything.

The Proprietary AI platforms at Deep Genomics consist of datasets, data processing pipelines, machine learning systems, including foundation models and large language models, and software engineering systems, plus the processes and protocols followed by team members.

Our flagship platform, BigRNA, is the world’s first RNA foundation model for RNA therapeutics.

In the AI community, a foundation model is a very large machine learning model that can be used for a wide range of tasks. In drug discovery, a traditional AI model may be good at one task, such as predicting molecule-target interactions, whereas a foundation model will have learned fundamental aspects of biology and chemistry that benefit many tasks.

BigRNA Figure 1a

This means that BigRNA can uniquely discover a wide range of new biological mechanisms and RNA therapeutic candidates that would not be found using traditional approaches. We have built, and continue to improve upon, our proprietary BigRNA platform, which is fueled by diverse proprietary datasets, new ML engineering advances, and the ongoing work of our scientists.

Currently, most efforts have focused on predicting data that measures overall gene expression levels, which are not suited to predicting regulatory interventions; for example, specific transcriptional perturbations on splicing or polyadenylation. In contrast, BigRNA is trained to predict RNA expression at sub-gene resolution.

Our BigRNA model is good at target identification, discovering novel biological mechanisms that can be drugged, predicting molecule-target interactions, designing therapeutic candidates, designing surrogate molecules for in vivo testing, and much more. Further, it can do all of this across a wide range of species, tissues, cell models, and RNA therapeutic modalities, including oligonucleotides, DNA editing, RNA editing, and mRNA. For some of these tasks, there are individual tools, such as Enformer, Saluki, or SpliceAI, but we found that BigRNA exceeds the state of the art across a wide range of tasks.


Our scientists regularly publish in their fields' most influential peer-reviewed journals. Here is a selection of our contributions.

An RNA foundation model enables discovery of disease mechanisms and candidate therapeutics

Celaj et al., BioRxiv, September 2023.

A curated census of pathogenic and likely pathogenic UTR variants and evaluation of deep learning models for variant effect prediction

Bohn et al., Front. Mol. Biosci., September 2023.

Transcriptome-wide off-target effects of steric-blocking oligonucleotides

Holgersen et al., Nucleic Acid Ther., December 2021.

ATP7B Met645Arg causes Wilson disease by promoting exon 6 skipping

Merico et al., npj Genomic Medicine, April 2020.

Deep learning in biomedicine

Wainberg et al. Nature Biotechnology, September 2018.

Inference of the human polyadenylation code

Leung et al. RECOMB, April 2017.

Genome-wide characteristics of de novo mutations in autism

Yuen et al. NPJ Genome Medicine, August 2016.

Automated analysis of high‐content microscopy data with deep learning

Kraus et al. Molecular Systems Biology, April 2017.

Deep learning of the tissue-regulated splicing code

Leung et al. Bioinformatics, June 2014.