mRNA Lightning™ Platform Technologies

The world’s largest dataset of mRNA biology visualizations generated by our mRNA Biology technologies in multiple disease models and cell types, used for training the mRNA Image Network.

Standing out as the world’s first mRNA biology knowledge graph and leveraging knowledge built over a decade of our deep diving into mRNA biology, the knowledge graph integrates multiple proprietary sources such as curated publications, analyses from mRNA Lightning’s dataset of over 2 billion mRNA biology images and thousands of experimental results from our projects, across diseases, targets, and cell types.

mRNA Biology pathway visualizers

Our mRNA PathwayLight technology enables the visualization of dozens of mRNA regulatory events and pathways such as splicing, localization, modification, or decay/stability. Each of these “visualizers” is built as a high-content screening assay with the images acting as visual readouts of pathway biology.

Visualize mRNA transcripts

A large number of imaging assays, enabling visualization of individual or multiple mRNA transcripts across their life cycle.

Massively parallel architecture

Our mRNA biology lab runs high-content at high-throughput screening in a massively parallel architecture. With a screen-to-cloud architecture, billions of images are uploaded to our cloud servers for analysis. The fully automated lab uses robotics and high-resolution microscopy, incorporating our imaging and mRNA biology assays under a single framework.

Standing out as the first language model specifically targeting mRNA biology, the LLM is augmented with Anima’s proprietary knowledge and data. It enables biologists to ask questions about pathways, targets, and mechanisms of action in mRNA biology. In an iterative process of suggesting hypotheses, prioritizing wet lab experiments, and analyzing their results, all with AI working alongside biologists.

Our MOAi technology elucidates the mechanism of action and molecular targets of compounds. Molecules that were found to be active as disease signature modulators are iteratively studied using the knowledge graph, the LLM, and the mRNA image neural network. Unlike purely computational AI approaches, MOAi technology integrates the data coming back from all wet lab biology experiments. It starts with the knowledge graph, using the LLM to query the identified pathway. Taking into account the compound’s chemistry, it then devises an MOA elucidation strategy by optimally sequencing the execution of our dozens of proprietary MOA assays. Experimental biological results and their associated MOA visualizations are uploaded to the system. This enables MOAi to reconsider all new data for an adjusted ranking of MOAs and targets, driving the next iteration. Throughout this process, biologists interact with the system in a natural language through the Lightning co-pilot. This AI-driven, biologist-in-the-loop approach integrates computational and wet lab biology, rapidly converging to elucidate a compound’s MOAs and molecular target.

MOA elucidation strategy