Hedera-22 develops a natural product discovery platform that utilizes bioinformatics and machine learning algorithms to identify and synthesize novel biopesticides from a vast library of genomes and environmental DNA. This approach addresses the agricultural industry's need for sustainable plant protection solutions by providing effective alternatives to synthetic chemicals.
Funding
$2.2M 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 agricultural industry faces increasing pressure to adopt sustainable plant protection solutions due to the environmental and health concerns associated with synthetic chemical pesticides. Identifying and developing effective, natural alternatives to these chemicals is a time-consuming and resource-intensive process. Existing natural product discovery methods often struggle to unlock the full potential of microbial biodiversity for novel biopesticide development.
Solution
Hedera-22 offers an AI-driven natural product discovery platform that accelerates the identification and development of novel biopesticides. The platform leverages a vast library of genomes and environmental DNA, combined with proprietary machine learning algorithms, to predict and prioritize biomolecules with pesticidal activity. Hedera-22 employs in-vitro synthesis to produce identified compounds, bypassing the limitations of traditional strain collection and culture methods. The platform integrates genome mining, metabolomics, and structure prediction to streamline the discovery process and unlock new possibilities in biomolecule exploration. This approach enables the creation of a diverse and patentable molecule library for sustainable crop protection.
Target Audience
Hedera-22 primarily targets agricultural companies, biopesticide manufacturers, and research institutions seeking to develop sustainable plant protection solutions.
Features
- Proprietary machine learning algorithms for predictive biomolecule discovery
- Integration of genome mining, metabolomics, and structure prediction techniques
- In-vitro synthesis capabilities for efficient production of identified compounds
- High-throughput screening assays for antifungal, herbicidal, and ecotoxicological activity
- Extensive library of genomes and environmental DNA for novel biopesticide identification
- Pre-characterized biomolecule catalog available for screening and licensing
- Collaborative research programs for identifying new metabolites with agricultural applications