Drug discovery has made enormous progress over the past decades, yet many diseases remain difficult to treat because their underlying biological targets are considered too complex or inaccessible for traditional therapies. UK based biotech company Antiverse is aiming to overcome this challenge by combining artificial intelligence with experimental biology to design new therapeutic antibodies for these hard to reach targets. The company has now secured $9.3 million in Series A funding to accelerate the development of its technology and expand its drug discovery programmes.
New funding to advance AI driven antibody discovery
The Series A round was led by Soulmates Ventures, with participation from Innovation Investment Capital, DOMiNO Ventures and existing investors DBW, Kadmos Capital and i&i Biotech Fund. With this latest financing, Antiverse’s total capital raised has surpassed $20 million.
The new funding will support the scaling of the company’s AI powered antibody discovery platform, enabling it to work more closely with pharmaceutical partners and research organisations while also advancing its own internal therapeutic pipeline.
The challenge of undruggable targets
A large proportion of diseases are linked to molecular structures that have historically been difficult to target with conventional drugs. These so called undruggable targets include certain protein families that are essential to disease development but remain challenging for traditional therapeutic approaches.
Among these are G protein coupled receptors and ion channels, which play critical roles in cell communication and are associated with a wide range of medical conditions. These targets are involved in diseases such as cancer, neurological disorders and rare genetic conditions, including cystic fibrosis.
Despite their importance, developing therapies against such targets is extremely difficult. The overall success rate for drug candidates entering clinical trials remains low, with attrition rates estimated to reach around 90 percent. This means that most drug discovery programmes fail before reaching patients.
AI meets experimental biology
Antiverse’s approach seeks to improve the efficiency of antibody discovery by integrating artificial intelligence with laboratory validation. The company’s computational platform uses machine learning models to design antibodies that can bind to complex disease targets with high specificity.
Once candidate antibodies are generated by the AI models, they are produced and tested in Antiverse’s in house laboratory systems. These proprietary cell based models are designed to replicate how target proteins appear in the human body, allowing researchers to assess whether the antibodies interact with their targets in realistic biological conditions.
Promising candidates identified through this process can then move forward into further preclinical development, creating a rapid feedback loop between computational design and experimental validation.
Collaboration with the Cystic Fibrosis Foundation
Antiverse has also established a research collaboration with the Cystic Fibrosis Foundation to explore new antibody based therapies for cystic fibrosis. The partnership focuses on targeting the extracellular region of the CFTR protein, a critical component involved in the disease.
The CFTR protein has historically been difficult to target therapeutically, making it an ideal candidate for Antiverse’s AI driven discovery approach. Through the collaboration, the company will apply its platform to design and evaluate new antibody candidates that could potentially open up novel treatment pathways for patients.
Expanding partnerships and internal programmes
With the new funding, Antiverse plans to further develop its antibody discovery platform and strengthen collaborations with pharmaceutical companies, foundations and research institutions. These partnerships will allow the company to apply its technology to a broader range of disease targets.
At the same time, the company intends to expand its internal drug pipeline, advancing lead antibody candidates toward in vivo efficacy studies. By combining advanced computational models with targeted laboratory experimentation, Antiverse hopes to accelerate the development of therapies for diseases that have remained difficult to treat using traditional drug discovery approaches.
As the pharmaceutical industry increasingly adopts AI technologies, platforms capable of bridging computational design with experimental biology may play a crucial role in unlocking new therapeutic possibilities. Antiverse is positioning itself at the forefront of this shift by focusing on some of the most challenging targets in modern medicine.