The startup operates a digital drug discovery platform that utilizes a proprietary computational analytics system and artificial intelligence to identify and validate candidate drugs through biomedical testing. This approach enables researchers to efficiently match clinical indications with drug effects, targeting complex and rare diseases that lack effective treatments.
Funding
$4.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional drug discovery is a time-consuming and expensive process, often failing to identify new uses for existing drugs with known safety profiles. This inefficiency hinders the development of treatments for complex and rare diseases, leaving many patients without effective options.
Solution
Delta4 provides an AI-powered drug discovery platform, Hyper-C, that identifies and validates new indications for existing drugs. The platform leverages a vast collection of algorithmic intelligence, feeding on big data to build molecular models and match compounds to diseases. This approach allows for the detection of previously undetected relationships between drugs and diseases with unprecedented speed and accuracy. Delta4's process combines AI with the expertise of computational biologists to efficiently develop assets, de-risking the drug discovery process and accelerating the delivery of new therapeutic options to patients.
Target Audience
Delta4 serves pharmaceutical companies, research organizations, and medical communities seeking to accelerate drug discovery, expand drug indications, and develop treatments for complex and rare diseases.
Features
- AI-powered Hyper-C platform that analyzes scientific literature, clinical trials, patents, and omics datasets to identify drug-disease relationships
- Network-based approach leveraging genomic, proteomic, and metabolomic data to evaluate the biological effects of molecules
- Target-based approach focusing on specific proteins and receptors involved in diseases
- Signature-based approach using gene expression signatures to identify drug targets
- Genetic association-based approach leveraging genetic data to identify novel therapies
- Text-mining based approach using natural language processing to identify potential drugs from biomedical literature
- Identification of prognostic and predictive protein biomarkers to streamline preclinical studies