The startup develops antibody design platforms that utilize computational methods and large datasets to enhance the discovery and optimization of therapeutic antibodies. By enabling researchers and pharmaceutical companies to identify new candidates and refine existing ones, the platform accelerates the development of antibody-based treatments.
Funding
$60K 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.
BGEMSASFFounders
Product
Problem
Traditional antibody discovery and optimization methods are often slow, expensive, and failure-prone, requiring extensive iterative cycles of lab work and guesswork. Identifying antibodies against challenging therapeutic targets can be particularly difficult, consuming significant resources without guaranteeing success.
Solution
EVQLV provides a computational antibody design platform that leverages machine learning, computational biology, and large datasets to accelerate and enhance the discovery and optimization of therapeutic antibodies. The platform uses in silico design and a Prescreened Human-Antibody Synthetic Intelligent Compendium (PHASIC) to generate diverse, fully-human antibodies without needing an antigen’s crystal structure. By calculating biophysical and machine-learned features, EVQLV creates digital signatures of antibodies, optimizing them for properties like affinity, immunogenicity, and stability. This approach reduces the need for extensive lab iterations, saving time and resources while increasing the likelihood of identifying viable therapeutic candidates.
Target Audience
The primary customers are researchers and pharmaceutical companies involved in antibody-based drug discovery and development, including those working on challenging therapeutic targets or seeking to optimize existing antibody candidates.
Features
- In silico antibody design and de novo antibody discovery
- Prescreened Human-Antibody Synthetic Intelligent Compendium (PHASIC) for generating target-specific display antibodies
- Antibody optimization for developability, including affinity, immunogenicity, and aggregation
- Antibody structure prediction algorithm
- Ability to work with any antibody format (e.g. scFv, nanobodies, bispecific antibodies, ADCs, AACs, AOCs, etc.)
- Epitope prediction capabilities