Drug discovery has been missing decision-quality evidence. Causaly is working with Microsoft to close the gap.

Biopharma R&D organizations are generating more signals than they can act on, and the bottleneck is not in the technology but in everything that happens after: the manual synthesis, the buried program history, the evidence that never makes it into the reasoning loop. That gap is where drug discovery programs lose weeks they cannot afford.

That is the gap Causaly and Microsoft are working together to address.  

Our collaboration is built on the principle that scientific computation and scientific interpretation should work together in managed workflow. Microsoft, with the Microsoft Discovery platform, provides large-scale compute, specialized agents, and simulation capabilities. Causaly brings prior-knowledge reasoning across internal and external scientific sources, with provenance, mechanistic context, that inform high-stakes decisions and judgements.  

"Microsoft Discovery is designed to accelerate science and unlock insights hidden in an organization's internal scientific data. But insights are only as valuable as the decisions they support. Causaly provides the prior-knowledge intelligence to determine whether these insights are biologically meaningful and consistent with an organization’s existing use cases and institutional knowledge. Microsoft Discovery can then support validation through advanced computing and integrations with laboratory automation systems. Together, we give biopharma R&D teams an end-to-end scientific workflow, from raw data to cited decision."

Aseem Datar, Corporate Vice President, Product Innovation, Microsoft Discovery & Quantum

Causaly is a governed, agentic research platform designed for scientific work, combining knowledge-graph-grounded reasoning, provenance-first outputs, and access to both external and internal scientific knowledge.  Causaly's data fabric spans the breadth of external biomedical evidence, and through private data integration, organizations can bring their own scientific memory (internal reports, historical program decisions, negative results, and proprietary findings) into the same reasoning environment, so scientists can assess a signal with the complete evidence landscape.

That distinction matters in life sciences. In this domain, decision quality depends not only on generating answers quickly, but on being able to explain why the answer is credible, what supports it, what contradicts it, and what to do next. Every claim is traceable to its underlying source in a single click, so outputs stand up to internal governance committees, regulators, payers, and Key Opinion Leaders who will ultimately scrutinize them.

Microsoft Discovery is an R&D platform with capabilities spanning chemistry, biology and life sciences, silicon, and physics use cases. It operates over governed internal data estates and enables scientific teams to run workloads, predictions, and simulations. That includes workflows such as omics analysis, pathway enrichment, in silico modeling, and other enterprise-scale scientific computation.  

Causaly is a leading agentic scientific decision platform that compounds the world's biomedical evidence with an organization's proprietary knowledge to accelerate time to market by powering confident decisions with evidence traceability and defensibility from discovery through launch.  

"Drug discovery does not suffer from a lack of data. It suffers from a lack of trustworthy interpretation. Microsoft Discovery brings scientific computation over enterprise data, and Causaly brings the prior knowledge, mechanistic reasoning, and provenance needed to turn those signals into decisions. Together, we can help researchers move from raw data to evidence-backed judgment much faster and with greater confidence."

Yiannis Kiachopoulos, Co-founder & CEO, Causaly

The joint solution can be applied across R&D Workflows where teams lose the most time, including:  

Target identification and prioritization:

• Microsoft Discovery generates quantitatively supported candidate targets from internal datasets  
• Causaly evaluates each for mechanistic plausibility, translational credibility, competitive precedent, and safety liabilities, drawing on both external evidence and internal program history
• The result is a cited target prioritization brief with evidence-for and evidence-against for every entry, safety flags, and recommended de-risking experiments in hours, not weeks.

Resulting in R&D teams that iterate faster, make higher-confidence decisions, and produce outputs built to withstand scientific and regulatory scrutiny — at every stage.

What this means for Enterprise R&D

For biopharma organizations, the combined result is faster iteration with fewer blind spots, higher-confidence go/no-go decisions, and outputs that carry provenance which have been used to support regulatory review and cross-team alignment. The joint solution is available early biopharma customer engagement.  

To see the joint workflow in action, request a demo here.  

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