Antiverse utilizes machine learning and advanced cell line engineering to design target-specific antibody libraries for challenging drug targets, including G-protein coupled receptors and ion channels. The platform accelerates the antibody discovery process, enabling the development of functional therapeutics within six months, addressing the lengthy timelines typically associated with traditional drug development methods.
Funding
$8.4M 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.
IIFounders
Product
Problem
Traditional antibody discovery methods for challenging drug targets like G-protein coupled receptors (GPCRs) and ion channels are often slow and inefficient. These targets have complex structures, and generating antibodies that bind with high affinity and specificity can be difficult and time-consuming.
Solution
Antiverse is developing a computational antibody design platform that uses machine learning and advanced cell line engineering to accelerate the discovery of functional antibodies for challenging drug targets. The platform designs target-specific antibody libraries and screens them against cell lines expressing the desired receptor. Machine-learning models predict sequences with a high binding probability without the need for pre-existing data. The analytical arm of the platform clusters sequences across multiple antibody properties, and predicted lead candidates are sent for further functional validation. This approach enables the development of functional therapeutics within six months.
Target Audience
The primary customers are pharmaceutical and biotechnology companies seeking to develop antibody therapeutics for challenging drug targets, including G-protein coupled receptors and ion channels.
Features
- Machine learning design engine that designs target-specific antibody libraries
- Models predict sequences with a high binding probability without pre-existing data
- State-of-the-art laboratory facilities to prepare unique cell lines expressing millions of the desired receptor
- Screening of target-specific libraries against cells with high receptor counts to improve the likelihood of discovering binders with high efficacy and affinity
- Analytical arm clusters sequences across 20 different antibody properties
- _In silico_ approach augments the efficiency of the antibody discovery process
- Multiparameter clustering capabilities to select for antibodies suiting desired properties
- Capability to design epitope-specific antibodies, optimize physiochemical properties, and ensure high humanness simultaneously