Causal AI
Causal AI moves beyond correlations to discover the why behind relationships. It answers not just "what is happening?" but "what is causing it?"
In drug development, understanding causality is critical to de-risk decisions, minimize failures, and focus resources on what works.
Our Causal AI technology is designed to do just that—help you uncover and leverage true cause-effect relationships across your drug development pipeline.
Causality is your secret weapon
In drug development, the key question isn’t just "What predicts Y?", but rather "What would happen to Y if we change X?"
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Instead of simply predicting which patients might have a disease (Y), we want to know what would happen to their health outcomes (Y) if we administered a particular treatment (X).
"Causal Prediction"
Causal prediction requires understanding the underlying cause-effect relationships between variables.
This requires more than data; it needs external knowledge—like scientific insights, trial results, and expert understanding of biological pathways—to inform AI models.
Incorporate Causal Inference
Combine the power of cauasl knowledge and AI to:
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Fail or Succeed faster at evaluating drug candidates saving time and resources.
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Identify successful drug candidates earlier in the process.
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Target the right patient groups for better trial outcomes, reducing the risk of missing key endpoints.
AI excels at simple prediction
Prediction identifies patterns and correlations based on historical data, but it doesn’t tell us whether changing X will actually cause a different outcome.
AI might predict that certain biomarkers are associated with patient survival, but it can't tell us if modifying those biomarkers will cause the patient to live longer.
De-Risk your Pipeline
Without causal knowledge, AI is limited to spotting correlations, which may lead to misleading conclusions, trial failures and costly setbacks.
For drug development, knowing why something happens (causality) is essential to “derisking” the biology, helping make decisions that can truly improve patient outcomes.
Causal Graphical Models
Causal graphical models are your tool for reasoning under uncertainty.
By incorporating causality into AI-driven decision-making, you can biologically de-risk your pipeline and increase the probability of success at every step, from discovery to post-market.
SynoGraph
We are building the SynoGraph platform to leverage the power of Large Language Models (LLMs) to maximize the value of your clinical and real-world data while de-risking your drug development pipeline.
SynoGraph will rapidly extract causal knowledge from vast amounts of external data—literature and expert insights. It then updates our pre-built causal knowledge graphs to help you reason under uncertainty, guiding you to make decisions backed by cause-effect insights, not just correlations.