InterAx Biotech utilizes machine learning and mathematical modeling of cellular processes to generate datasets that predict drug effects on GPCR targets, addressing the challenge of cellular signaling in drug discovery. Their platform enhances hit-to-lead identification by providing tools for the discovery and optimization of high-efficacy drug candidates based on cellular biology mechanisms.
Funding
$2.7M 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
Drug discovery for G protein-coupled receptors (GPCRs) is hampered by an incomplete understanding of cellular signaling pathways, leading to high failure rates in clinical trials. Traditional methods often focus on ligand binding affinity but neglect the complexities of cellular biology and downstream effects, resulting in drug candidates with suboptimal efficacy and safety profiles.
Solution
InterAx Biotech offers a technology platform that integrates experimental biology, mathematical modeling, and machine learning to predict drug effects on GPCR targets. The platform generates datasets that capture the nuances of cellular signaling, enabling the discovery and optimization of drug candidates with high efficacy and improved safety. By simulating cellular processes and training machine learning models on both experimental data and biological knowledge, InterAx facilitates the selection of drug candidates based on their impact on cellular biology mechanisms, moving beyond simple ligand discovery to comprehensive drug discovery. This approach allows for the creation of lead molecules and drug candidates with uniquely high efficacy, an extended therapeutic window, and differentiated paths for IP protection.
Target Audience
InterAx Biotech primarily serves pharmaceutical companies and research institutions seeking to accelerate and optimize hit-to-lead identification and lead optimization for GPCR targets.
Features
- Mechanistic modeling of cellular processes to generate predictive datasets
- Integration of experimental methods, mathematical models, and machine learning
- Identification of cellular responses linked to drug-induced side effects
- High-throughput biochemical screening of compounds
- AI-driven lead optimization for GPCR targets
- Prediction of therapeutic efficacy and safety in patients
- Generation of drug candidates with improved tolerability