Moving faster with more certainty: How AI can revolutionize life sciences R&D

According to Deloitte, the average cost of drug development rose to $2.2 billion in 2024. With the stakes so high, life sciences R&D teams face mounting pressure to identify promising drug targets while minimizing the risk of failure.

There also remains a productivity challenge across the industry. A report by McKinsey noted that biopharma “R&D productivity remains stubbornly low” — exacerbated by factors including persistently low drug success rates and increasingly long development cycles.

Clearly, researchers need ways to move faster. But it’s no good simply putting the foot on the accelerator. Decisions also need to be made with greater precision, certainty and confidence.  

This is where advances in AI can help.  

How AI can help break through industry challenges

AI has the potential to transform the speed and efficacy of the drug discovery process.  

Take the dizzying amount of scientific and biomedical data now available. It’s impossible to review manually. But new AI tools can analyze millions of data points within minutes and extract relevant insights.  

By finding these hidden insights, AI can also help R&D teams prioritize the most promising drug targets, identify novel biomarkers, and create effective therapeutic strategies. In turn, budgets can be invested in the right places and clinical trial failure rates reduced.

So far, so good. Except the industry is awash with generic AI tools that appear to promise similar benefits. When in fact, most, if not all, are simply not designed for the demands of life sciences research.

Make your breakthrough with Causaly

Causaly is an AI platform built by scientists, for scientists. And unlike most AI tools, it doesn’t just summarize what’s already known — it offers a foundation to help with reasoning, discovery, and decision-making.

It maps complex biological relationships, connects siloed knowledge, and helps researchers generate hypotheses grounded in the full scientific context. Crucially, all this happens in a platform that’s explainable, secure, and purpose-built for enterprise. 

A foundation for science, not search 

Causaly was built from the ground up to support how life sciences teams actually work, from discovery to decision-making. 

1. A knowledge graph designed for life sciences 

At the heart of Causaly is a domain-specific knowledge graph, which is the most precise in the marketplace. It uses custom biomedical ontologies and half a billion relationships across key biological concepts, documents, and ontologies.  And unlike generic tools, it’s designed to reflect how researchers think — modeling the logic, structure, and language of biology.

As a result, it lets researchers explore disease biology with greater nuance, trace complex mechanistic pathways, and surface relationships that traditional search engines simply miss. 

2. Enterprise-ready, compliance-built architecture 

Causaly was built with enterprise security and scientific governance at its core. It supports private VPC deployments, document-level access controls, and strict separation of licensed and proprietary data. 

Every output is explainable, auditable, and human-reviewable, so R&D teams can move fast without compromising compliance or trust. 

3. Internal and external data, unified and searchable 

Scientific answers rarely live in one place, which is why Causaly integrates both external and internal biomedical literature into a single, searchable knowledge base. This combines everything from PDFs to regulatory documents to experimental files.  

As a result, teams can remove silos without sacrificing security, allowing scientists to connect internal learnings with external evidence in one environment. 

4. Modular, frictionless, and built for scientists 

Causaly fits into how scientists already work. Its modular platform integrates through APIs, adapts to existing workflows, and requires no prompt engineering or retraining. 

The platform grows with your team, whether your focus is exploring new targets, prioritizing biomarkers, or accelerating early discovery. It can support you every step of the way, from quick search to complex reasoning.  

Solving for speed, scale, and scientific depth 

Life sciences R&D is under pressure to move faster, but speed without depth isn’t progress. The real challenge is to solve more complex problems with better tools, rather than simply processing more external and internal data.  

Causaly’s core components allow researchers to reason across disciplines, uncover new disease associations, and move from exploration to actionable insight. 

Because the platform models biology in a way that mirrors how scientists think (considering ambiguity, complexity, and exception), it surfaces relationships others miss. For example, instead of starting with a disease and asking, “What’s the biology behind it?”, Causaly enables teams to start with biology itself and ask, “Where else does this matter?” 

That shift makes a real difference. You might know CD8 is important in oncology, but unless you’ve also read the right immunology papers, you’d miss its broader immune relevance.  

Causaly connects those dots. And because it integrates internal and external sources, researchers can immediately see what’s worked, what hasn’t, and what’s worth further investigation, all in a system built for traceability and trust. 

Make your next R&D breakthrough  

With Causaly, your teams can move faster, go deeper, and generate insights grounded in the full complexity of biomedical research, with nothing left unturned. 

If you’re ready to shift from reactive searching to proactive discovery and reasoning, see how Causaly can help you make that breakthrough. Learn more at causaly.com.  

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