BathGenBio utilizes artificial intelligence to analyze extensive biobank data, including genomic and clinical information, to identify novel biomarkers and optimize drug discovery processes. The platform addresses the challenges of precision medicine by enhancing target discovery, drug response prediction, and drug repurposing for medical institutions and biotechnology companies.
Funding
$7.6M 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
The process of identifying novel drug targets and predicting drug responses is often slow and inefficient, hampered by the complexity of biological data and the limitations of traditional research methods. Analyzing large-scale biobank data, which includes genomic and clinical information, presents significant challenges in extracting meaningful insights for precision medicine and drug discovery.
Solution
BathGenBio offers an AI-driven platform that analyzes extensive biobank data to accelerate and improve drug discovery and precision medicine. The platform integrates clinico-omics cohort databases (COCD) with deep-learning algorithms to simulate drug effects, identify potential drug targets, and predict patient responses. By applying Mendelian randomization and time-labeled biomarker analysis, BathGenBio helps researchers understand disease causality, optimize drug combinations, and repurpose existing drugs for new indications. The platform's DEEPCT module simulates clinical trials to identify responders and non-responders, while the TLBM module discovers time-dependent biomarkers to predict disease risk across different age groups.
Target Audience
BathGenBio primarily serves medical institutions, biotechnology companies, and pharmaceutical firms seeking to enhance their drug discovery pipelines and advance precision medicine initiatives.
Features
- COCD Platform: Integrates global biobank data, including the K-Biobank with over 156,000 Korean clinical and genomic samples.
- DEEPCT: A deep learning-based clinical trial simulation platform for predicting drug efficacy and identifying optimal drug combinations.
- TLBM: A solution for discovering time-labeled biomarkers and predicting disease risk by age.
- Target Discovery: Identifies potential drug targets by analyzing the relationship between diseases and genetic factors.
- Drug Repurposing: Discovers new indications for existing drugs through simulations and biomarker analysis.
- Drug Response Prediction: Identifies markers for drug responders and non-responders using Mendelian randomization.
- Multi-omics Data Integration: Incorporates genomics, metabolomics, proteomics, transcriptomics, and microbiome data for comprehensive analysis.