Lightning.AI Platform Technologies

Architecture Technologies Applications
Visualizing and decoding disease biology

A Decade of Anima's Platform Evolution

1.0
TranslationLight
TranscriptLight
Literature
and Omics
2.0
PathwayLight
AI technologies
Lightning.AI
Co-pilot
Pathway Hypothesis
3.0
Lightning.AI
Computational AI
Chemistry
Automated Visual
Biology Lab
AI-driven MOA
elucidation
PathwayLight
Neural Network
mRNA Biology
Neural Network
Experimental AI

Target.Discovery
Drug.Discovery
2 Billion Visual
Biology Images
Literature
and Omics
Lightning.AI
Co-Pilot
Pathway
Hypotheses
 
Thinking
Biology
 
Computational.AI
Experimental.AI
 
Seeing
Biology
 
PathwayLight
Automated Visual
Biology Lab
 
Learning
Biology
 
PathwayLight
Neural Network
mRNA Biology
Neural Network

Computational AI - Hypotheses at Scale

Thinking biology

Our Computational AI integrates multi-omics, literature, and proprietary data to generate thousands of mechanistic hypotheses for a given disease. It prioritizes pathways likely to drive disease and frames the key questions for exploration. Thousands of hypotheses get to be explored in parallel by Experimental AI in over a billion experiments in a single run - instantly.

2 billion Visual Biology Images

The world’s largest dataset of visual biology images generated by our PathwayLight technologies in multiple disease models and cell types, used for training the Computational AI.

Computational AI - Hypotheses at Scale

Thinking biology

Our Computational AI integrates multi-omics, literature, and proprietary data to generate thousands of mechanistic hypotheses for a given disease. It prioritizes pathways likely to drive disease and frames the key questions for exploration. Thousands of hypotheses get to be explored in parallel by Experimental AI in over a billion experiments in a single run - instantly.

Literature and Omics

Literature and multi-omics spark ideas and give the initial input to our Computational AI to generate thousands of hypotheses, but our disease-specific proprietary data and self-learning neural networks is what turns them into breakthroughs.


Literature
Omics
2 billion Visual Biology Images

Computational AI - Hypotheses at Scale

Thinking biology

Our Computational AI integrates multi-omics, literature, and proprietary data to generate thousands of mechanistic hypotheses for a given disease. It prioritizes pathways likely to drive disease and frames the key questions for exploration. Thousands of hypotheses get to be explored in parallel by Experimental AI in over a billion experiments in a single run - instantly.

Lightning.AI Co-Pilot

The Lightning.AI Co-Pilot provides a ChatGPT-like interface across the Lightning.AI, enabling biologists, chemists, and data analysts to interact with the system in a seamless user experience.

Using the LLM, the Co-Pilot translates questions in natural language into queries that are saved along with their results, with each step further elucidating disease biology. It also acts as a way to ask the system to plan, execute, and monitor discovery activities.


Computational AI - Hypotheses at Scale

Thinking biology

Our Computational AI integrates multi-omics, literature, and proprietary data to generate thousands of mechanistic hypotheses for a given disease. It prioritizes pathways likely to drive disease and frames the key questions for exploration. Thousands of hypotheses get to be explored in parallel by Experimental AI in over a billion experiments in a single run - instantly.

Pathway Hypotheses

Leveraging knowledge built over a decade, the Computational AI integrates literature and omics with multiple proprietary sources such as curated publications, analyses from Lightning.AI dataset of over 2 billion visual biology images and thousands of experimental results from our projects, across diseases, targets, and cell types.

Thousands of pathway hypotheses are generated by our Computational AI and then seen by Experimental AI in the next iteration.


2 Billion Visual Biology images
Lightning.AI dataset
Omics
Literature
Thousands of
experimental results
Curated publications

Experimental AI - Visualizing, Learning, Refining

Seeing biology

Experimental AI doesn’t test - it sees biology in action. Using PathwayLight technologies in our fully automated visual biology lab, it sees pathway behaviour, functional phenotypes, and biological signals across millions of healthy and diseased cells.

PathwayLight

PathwayLight technologies visualize how thousands of pathways behave inside real cells. These technologies including TranslationLight, TranscriptLight and others, see regulatory events and pathways such as splicing, localization, modification, or decay/stability, creating visualizers. Each visualizer is built as a high-content screening assay with the images acting as visual readouts of pathway biology.

  • Transcription
  • Splicing
  • Alternative splicing
  • Capping
  • Polyadenylation
  • Modification
  • Nuclear Export
  • Transport
  • Localization
  • Stability
  • Decay
  • Translation

Experimental AI - Visualizing, Learning, Refining

Seeing biology

Experimental AI doesn’t test - it sees biology in action. Using PathwayLight technologies in our fully automated visual biology lab, it sees pathway behaviour, functional phenotypes, and biological signals across millions of healthy and diseased cells.

Automated Visual Biology Lab

Our 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 assays under a single framework.


Experimental AI - Visualizing, Learning, Refining

Learning biology

Our neural networks, PathwayLight and mRNA biology learn about targets and drugs from cell images they see - and feed Computational AI with these insights back into the loop.

PathwayLight Neural Network

Trained on billions of images, this AI inside the biology compares healthy and diseased cells millions of times for every hypothesis. It identifies dysregulation, recognizes phenotypes, and drives the discovery loop forward. It feeds that insight back to Computational AI - iteratively converging on novel, druggable targets. The final outcome is the discovery of the targets directly linked to disease biology with biological proof at each step.


Diseased
Phenotype
Healthy
Phenotype

Experimental AI - Visualizing, Learning, Refining

Learning biology

Our neural networks, PathwayLight and mRNA biology learn about targets and drugs from cell images they see - and feed Computational AI with these insights back into the loop.

mRNA Biology Neural Network

Trained on mRNA biology regulation across thousands of pathways, the second neural network explores each target’s mRNA biology. It identifies modulation strategy through splicing, RBPs, localization, degradation or translation. As compounds are screened, the neural network visualizes their effect on disease pathways in real cells - confirming impact and optimizing intervention. The final outcome is the discovery of the drugs using mRNA modulation with mechanisms of actions as a strategy to drug even the undruggable targets.