The company develops anti-infective medicinal products using physics-informed AI and synthetic biology. Their platform designs precise monoclonal antibodies to reduce manufacturing costs and broaden their application in combating infectious diseases.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional methods of monoclonal antibody (mAb) design for infectious diseases are time-consuming and expensive, often failing to address the rapid evolution and spread of viral and bacterial pathogens. The lengthy development cycles and high costs associated with conventional techniques limit the accessibility and affordability of mAb therapies, particularly in response to emerging pandemics and drug-resistant infections.
Solution
mAIbe is developing a platform for anti-infective medicinal products, leveraging physics-informed generative AI and synthetic biology to accelerate and optimize the design of monoclonal antibodies (mAbs). The platform uses advanced AI methods and computer simulations to predict and optimize how antibodies attach to their targets, enhancing their effectiveness and stability. By embedding key properties such as potency, half-life, and developability early in the discovery process, mAIbe reduces the need for extensive lab testing and animal testing, focusing only on the most promising drug candidates. This approach enables the creation of precise mAbs that target both endemic viruses, such as HIV and RSV, and pathogens with pandemic potential, as well as multi-drug resistant (MDR) bacteria.
Target Audience
The primary target audience includes pharmaceutical companies, research institutions, and healthcare organizations focused on developing novel therapies for infectious diseases, particularly those caused by viruses and multi-drug resistant bacteria.
Features
- Physics-informed generative AI for rapid generation and testing of mAb variations
- AI-driven computer simulations to improve antibody strength, stability, and target binding
- Computational tools to embed key therapeutic properties early in the discovery process
- Focus on mAbs targeting vulnerable regions in viral proteins and MDR bacteria
- Dynamic Developing Network (DDN) model for efficient collaboration with geographically dispersed SMEs
- Sustainable therapeutics development adhering to the Refine, Reduce, Replace (3Rs) principles