Revolutionizing Multiomics Research with Generative AI.

Cognit rapidly accelerates research in drug discovery and pre-clinical trials in identifying multi-omics biomarkers and fine-mapping diseases.

The current state of drug discovery is inefficient and expensive. It takes an average of 12 years and $2.6 billion to bring a new drug to market.

This is due in part to the fact that drug discovery is largely a trial-and-error process. Researchers must first identify a target gene, then develop a drug that can modulate that gene's expression. This process is often time-consuming and expensive, and it can be difficult to predict which drugs will be effective.

Combinatorial Genomics.

Cognit is focused on Functional, Regulatory, and Epigenomics to enable gene expression prediction and gene expression engineering.
The Gene expression prediction platform takes any DNA sequence (DNASE) across any cell/tissue type and eukaryotes and can predict the gene expression and transcriptomic profile of the gene.
Cognit's AI can predict gene expressions given mutations in distal regulatory elements that can be 1MB from the loci.
Cognit's perturbative oracle allows rapid CRISPR-like experiments in-silico, enabling gene expression engineering.
In-silico saturation mutagenesis experiments with gene knock-out and engineered mutations across introns, exons, and regulatory elements to predict gene expression.

Cognit's Gene Expression Oracle ~ Applications

Gene expression is useful for identifying a disease's molecular signature and correlating a pharmacodynamic marker with the dose-dependent cellular responses to the exposure to a drug.
Cognit is revolutionizing drug discovery, clinical development, and commercialization of new drugs through cheap, in-silico experiments.
Directly predicting gene expression from DNA has significant advantages in rapidly accelerating the fine-mapping of diseases.
Enables significant leap in saturation mutagenesis that can be conducted in-silico.
Significant improvements in Directed Evolution.
Accelerated development of de-novo Biomolecules & Protein Engineering.

Cognit Edge

The core premise of Cognit is to run self-learning models in a distributed environment to accommodate very large architectures.

Decentralized Intelligence to run the workload across several nodes. (approach Genomics as a Large Model Architecture).
Learn from Epigenetic influences which require large model architectures.
Ethical, Responsible AI with explainability built from ground up.
Self-governing AI and Math models for Autonomous modeling using distributed learning.
New generation Deep Learning models that work in noisy/low data settings.
Focused on the discovery phase (read, write, deliver) with minimal or missing data.
Platform focus on whole-genome and single-cell computational investigations.
Multimodal, multi-agent learning systems that learn from across scenarios (motifs, functions, expressions, alleles).
Privacy of data, transparency of decisions, and pre-emptive controls with causal inference frameworks.