HITS develops AI-driven digital solutions for drug discovery, utilizing advanced molecular design and predictive modeling to enhance the efficiency of early-stage drug development. The platform addresses the challenges of traditional drug discovery by providing accurate predictions for drug-protein interactions and optimizing molecular properties, significantly reducing time and costs associated with the development process.
Funding
$5.5M 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 methods are time-consuming and expensive, often failing to identify promising drug candidates early in the development process. Accurately predicting drug-protein interactions and optimizing molecular properties remains a significant challenge, hindering the efficient development of new therapeutics.
Solution
HITS leverages AI-driven digital solutions to accelerate and enhance early-stage drug discovery. The Hyper Lab platform offers a suite of AI-powered tools, including Hyper ADME/T for predicting drug absorption, distribution, metabolism, excretion, and toxicity; Hyper Design for AI-assisted molecular design; Hyper Binding for accurate drug-protein interaction prediction; and Hyper Screening for efficient virtual screening. These tools enable researchers to explore a vast chemical space, optimize molecular properties, and prioritize promising candidates for synthesis and experimental validation. By providing accurate predictions and streamlining the discovery process, HITS aims to reduce the time and costs associated with bringing new drugs to market.
Target Audience
The primary target audience includes pharmaceutical companies, biotech firms, and academic research institutions involved in early-stage drug discovery.
Features
- **Hyper ADME/T:** Predicts key ADME/T properties such as CYP inhibition, solubility, LogP, metabolic stability, and hERG inhibition.
- **Hyper Design:** Generates novel molecular structures using AI, exploring a broad chemical space beyond human intuition.
- **Hyper Binding:** Predicts drug-protein interactions, enabling prioritization of compounds with high binding affinity.
- **Hyper Screening:** Facilitates virtual screening of large compound libraries to identify potential drug candidates.
- Virtual screening using physics-informed deep learning models.
- AI drug design based on molecular fragment generative models.
- ADME/T prediction using physics-based AI models.