CEO
Converge Bio
Integrates generative AI with biological data using large language models (LLMs) specifically trained on biological languages to accelerate drug discovery and development. The platform enables biotech and pharmaceutical companies to optimize small molecules, generate antibodies, identify biomarkers, design mRNA vaccines, and engineer novel proteins, improving efficiency and effectiveness in creating therapeutics.
- Artificial Intelligence
- Biotechnology
- Software Only
Updated 2 days ago
Funding
Raised to date
$5.5MRaised 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.
across 1 round
$5.5M · Seed
Announced Nov 2024
Disclosed cumulative: $5.5M
RoundDateAmountValuationInvestors
Founders
Product
Problem
Drug discovery and development is a time-consuming and expensive process, often hindered by the difficulty of predicting molecular interactions, identifying viable drug targets, and optimizing therapeutic candidates. Traditional methods struggle to efficiently process and leverage the vast amounts of biological data needed to create effective therapeutics.
Solution
Converge Bio offers a generative AI platform that leverages large language models (LLMs) trained on biological data to accelerate drug discovery and development. The platform enables users to predict molecule functionality, optimize small molecules, generate antibodies with enhanced therapeutic potential, discover new drug targets, design mRNA vaccines, identify new biomarkers, and engineer novel proteins. By integrating AI with biological data, Converge Bio aims to improve the efficiency and effectiveness of creating new therapeutics. The platform offers a library of bio-LLMs with foundational models across biological languages, enhanced with curated data and fine-tuned on task-specific datasets.
Target Audience
The primary customers are biotech and pharmaceutical companies seeking to accelerate drug discovery and development through the use of generative AI.
Features
- Bio-LLM library with foundational models across small molecules, proteins, single cells, human genes, antibodies, and RNA
- Models enhanced with curated data and fine-tuned on task-specific datasets
- Application layer focused on prediction, explainability, and generation
- Small molecule optimization to improve binding affinity, stability, and pharmacokinetic profiles
- Antibody generation to increase specificity and reduce immunogenicity
- Target discovery to accelerate the identification of druggable targets
- Vaccine design to enable rapid development and high efficacy of mRNA vaccines
- Biomarker identification to connect phenotypes with underlying molecular causes
- Protein engineering to create tailored proteins with enhanced efficacy and stability