NaturalAntibody provides bioinformatics solutions that integrate and analyze antibody data from various repositories to enhance the design of antibody-based therapeutics. The platform employs predictive modeling and structural analysis to identify developability issues and optimize therapeutic candidates, thereby accelerating the drug discovery process.
Funding
$4.7M 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
The antibody discovery process is often slow and inefficient due to the difficulty of integrating and analyzing vast amounts of antibody data from disparate sources. Identifying developability issues early and optimizing therapeutic candidates remains a challenge, hindering the development of effective antibody-based therapeutics.
Solution
NaturalAntibody offers a suite of bioinformatics solutions designed to streamline antibody discovery by integrating and analyzing antibody data from various repositories. The platform employs predictive modeling, structural analysis, and machine learning to identify potential developability issues and optimize therapeutic candidates. By providing a comprehensive view of antibody data and advanced analytical tools, NaturalAntibody accelerates research decision-making and facilitates the design of more effective antibody-based therapeutics. The platform's modules include an antibody database, sequence engineering tools, structure modeling capabilities, and antibody-antigen docking predictions.
Target Audience
The primary users are researchers and scientists in the pharmaceutical and biotechnology industries involved in antibody discovery, engineering, and development.
Features
- Comprehensive antibody database integrating data from major sources, updated regularly.
- Prediction of developability issues using sequence and structure-based biophysical descriptors.
- Language model-based mutational suggestions to identify beneficial mutations.
- Hit picking with multiple clustering methods (sequence, structure, paratope, embedding) to increase diversity.
- Batch sequence annotation module for filtering sequences based on liabilities, biophysical descriptors, and immunogenicity.
- Structure modeling module for therapeutic antibody lead optimization in a structural context.
- Antibody-antigen docking analysis and prediction of antibody poses towards the antigen.
- Secure data storage with high security standards to ensure data safety.