Arctoris
Accelerating drug discovery through automated, robotics-driven data generation and AI-integrated laboratories.
Decoding the human genome to design life-changing RNA-based therapies.
Deep Genomics is a pioneer in the AI-driven drug discovery space, specifically focusing on the intersection of deep learning and genomic biology. Their proprietary 'BigBear' platform represents one of the most advanced biological foundation models in existence, capable of predicting how genetic variations influence cellular processes such as RNA splicing, protein binding, and translation. Unlike traditional drug discovery which relies on high-throughput screening of existing libraries, Deep Genomics utilizes a 'predictive' approach to design antisense oligonucleotides (ASOs) that can correct the root causes of genetic diseases. By 2026, the company has solidified its position as a primary partner for global pharmaceutical firms, moving beyond rare monogenic diseases into more complex polygenic conditions. Their technical architecture leverages massive datasets of genomic sequences and cellular phenotypes to simulate millions of genetic mutations, identifying therapeutic targets that would be invisible to human researchers. The platform's ability to model the 'non-coding' genome—once considered junk DNA—has unlocked entirely new classes of druggable targets, making them a cornerstone of the programmable medicine era.
A deep neural network trained on billions of data points across the entire human genome to predict cell-level consequences of any genetic change.
Accelerating drug discovery through automated, robotics-driven data generation and AI-integrated laboratories.
Accelerating drug discovery through deep physics and generative AI without experimental data training.
Augmenting human intelligence to discover and develop life-changing medicines via end-to-end AI drug discovery.
Pioneering hypothesis-free drug discovery through the Interrogative Biology® platform and Causal AI.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Generative algorithms that design oligonucleotide sequences to maximize binding affinity and minimize immunogenicity.
Models the complex interaction between DNA sequence and the cellular machinery that creates RNA transcripts.
Predicts how results in animal models (mice, NHPs) will translate to human biological outcomes.
Uses multi-omics data to rank genetic targets based on druggability and safety profiles.
Simulates potential unintended interactions of a drug candidate across the entire human transcriptome.
Simulates cellular response to therapeutic intervention at the molecular level.
Identifying the genetic driver for ultra-rare conditions where the patient population is too small for standard GWAS.
Registry Updated:2/7/2026
Designing a drug that binds perfectly to a mutated RNA strand without affecting healthy versions.
Understanding why a drug failed in a specific subgroup and identifying the correct responder population.