Our AI discovery platform harnesses nature’s rich diversity to drive the next generation of therapeutics.
We work with our global ecosystem of partners to generate data and characterize the unexplored chemical space found in nature's biodiversity, building the most advanced screening library ever made.
We optimize research workflows by accelerating the analysis of spectral data to identify novel metabolites from nature. Our technology identifies bioactive fragments, automatically dereplicates known strucutres, and accelerates the structural annotation of novel chemistry.
We integrate multiple data streams across species, compounds, targets & indications to map and predict relationships between natural compounds and their use cases, uncovering the connection between plants and people at scale.
We integrate AI-driven compound modifications to design enhanced natural product derivatives. We perform multi-parameter optimization across factors such as target affinity, synthetic tractability, patentability and other pharmacologic properties - optimizing leads to advance through preclinical development.
1
We work with our ecosystem of partners to explore and generate data on the chemical composition of nature’s biodiversity. We use our knowledge graph to prioritize plants for exploration, and partners perform mass spectrometry analysis to generate data on the metabolic composition of key plants.
2
We use machine learning to analyze & process the spectral data. Our platform automatically dereplicates previously known compounds, followed by accelerated structural elucidation of unknown chemical scaffolds - using deep learning to translate spectral data into chemical structures.
3
We use AI to predict the bioactivity of novel chemical structures against hundreds of targets at a time. We prioritize high potential compounds for further testing & validation.
4
We use our computational drug design tools to optimize bioactive natural compound structures for synthesis, efficacy, and pharmacological properties - resulting in natural compound derivatives with enhanced performance, scalable production, and patentability.
5
We perform biological experimentation & preclinical validation to evaluate and further develop compounds.
6
We continuously generate vast, complex datasets to improve performance of our AI platform across multiple dimensions: making more accurate predictions on chemical structure elucidation, predicting bioactivity of chemical scaffolds, and optimizing the pharmacologic properties of lead compounds.
By the numbers
10x
100x
500x
Differentiators
What sets us apart
Address Undruggable Targets
Unlock novel natural chemical space to address challenging targets such as intracellular, epigenetic and protein-protein interactions.
Active Learning Strategy
We continuously generate large & complex datasets to improve performance of our AI models.
Multi-target screening
We computationally screen thousands of compounds against hundreds of targets at time.
Proprietary NP fingerprinting method
We are building a proprietary descriptor framework to encode the diverse & complex chemistry found in natural product chemical space
Multi-parameter optimization of all drug properties
Computational approaches power enhanced drug design for key pharmacologic properties, such as efficacy and ADMET - improving failure rates and reducing time to market.
Design for synthesis
Optimization of compounds for synthesis, driving enhanced scale production and sourcing.
Work with us to identify novel compounds, powering your early-stage discovery pipeline. We screen our rich & diverse library of natural compound scaffolds to provide high-value lead candidates for co-development.
We are building a portfolio of validated lead candidates in key therapeutic areas. Contact us to learn more about our proprietary pipeline of leads.