Decoding crop genetics with AI

We’re on a mission to accelerate the development of more productive, sustainable, nutritious & climate-resilient food sources.

We use cutting-edge machine learning to identify and prioritise high value targets for crop gene-editing.

Unlocking the future of food

The world is facing a food security crisis. We must produce more food in the next 50 years than in the previous 10,000 combined - and at a time when climate change is reducing crop productivity.

Gene-editing provides a solution to this challenge. But, identifying which genes to edit and how remains a major bottleneck for gene-edited trait developers.

We find the right targets to edit, prioritising novel leads and working with you to identify effective edits.

Next-generation crops demand next-generation targets

Our ability to introduce edits has outstripped our understanding of what edits to introduce. Predictive models for trait discovery developed for traditional breeding don't consider the full biochemical context required for accurate edit design.

Building on a decade of machine learning innovation in drug discovery, we’re harnessing high-throughput sequencing and multi-modal machine learning to rapidly identify novel, high quality genetic targets for any trait, in any crop.

We analyse each target in it's full biological context to go beyond ‘low-hanging fruit’, identifying targets with maximal efficacy and minimal undesired pleiotropic effects.

This is what we’re building at Biographica!

“What agbiotech needs to help guide CRISPR use is an efficient discovery platform that tests things in silico with models that recognize genes, metabolic processes, or signaling pathways, and that get strengthened by in vivo testing so they can predict the two or three genomic changes that will provide the trait outcome. Such models could help companies have a massive impact on developing and breeding the produce of tomorrow.”

— Head of Crop Trait & Technology Discovery at Syngenta

Reshaping the crop development funnel

Our discovery cycle drives increased success in gene discovery through:

Deep multi-modal in silico screening of potential targets in specific genetic backgrounds, prioritised for efficacy and pleiotropy
Modal-informed, context-aware edit design
In planta validaron
Iterative re-integration of validation data to improve future screens

We exist to streamline your gene-editing pipeline

Increase success rate

Save time

Cut costs

Decoding the genetics of food

We're hiring!