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ife sciences researchers face increasing pressure to deliver results faster and more efficiently. A new generation of purpose-built AI platforms is finally giving them what they need.
Investment trends reflect growing confidence in AI’s power to transform research and development, especially in the early stages. But success depends on using the right kind of AI, built for life sciences research.
Challenges for Drug Discovery
Scientists and researchers are overwhelmed by information, with billions of data points available for life sciences development. Fragmented knowledge across therapeutic areas, data types, and modalities makes comprehensive analysis difficult. Reviewing so much information “manually” can take months and still leave gaps. Building in-house AI platforms means pivoting away from research goals and can be very costly and time consuming—and still not function well.
Target identification remains manual and inconsistent, and competitive intelligence such as understanding marketing trends, patient demographics, competitor pipelines and expenditures, regulatory and policy compliance, and similar strategic research often arrives too late. Costs soar as teams repeat slow review cycles, and failure in late stages can be especially costly. For example, one promising antipsychotic drug that was part of a $9 billion acquisition failed midstage trials, tanking the business’s value. That’s an expensive miss.
The AI Case for Change
Today’s advanced AI platforms have the power to solve this problem by accessing and leveraging massive amounts of structured and unstructured data, and are specifically designed to shorten the lifecycle of bringing new developments to market.
This new generation of scientific AI platforms can unpack multistep questions, almost mimicking the step-by-step process of researchers. But not all AI platforms are created equal, and understanding the differences is crucial to ensuring success in early-stage discovery.
Scientists are frustrated with the likes of ChatGPT or Perplexity for in-depth research because such tools highlight information selectively, and they too often ”hallucinate.” These GenAI tools often surface evidence based on what’s popular, most recent, or most successful. That creates a limited view of the subject matter and lacks sophistication in terms of understanding the relevance of the information.
A purpose-built scientific AI might also use multiple, different search and retrieval methods to meticulously review documents and rank them based on keywords, context, and more. It can scan all available literature and distinguish between correlation and causation while surfacing explainable insights grounded in biology.
Agentic AI refers to AI-driven “agents” that can complete complex, multistep knowledge work across scientific discoveries. Crucially, these agents don’t just retrieve information: They mimic the scientific process. While GenAI provides chat responses and document summaries, agentic AI goes further, generating new hypotheses and producing structured, explainable output. Using agentic AI, research teams can:
- Read and interpret structured (and unstructured) public and internal scientific data
- Integrate public and internal applications and tools for agentic research
- Uncover biological connections that cross disciplines
- Move from exploration to hypothesis with speed and structure.
The result is a shift from fragmented information to integrated scientific insight, enabling teams to move faster, ask better questions, and reduce time to discovery.
Life Sciences AI Platforms: New Paradigm for Early Drug Discovery
Life sciences AI platforms act as a “single source of truth” for internal and external data, including biomedical papers, research reports, experimental data, and databases. Today, many scientists lose precious time switching between siloed tools, such as raw data in one system, literature in another, and insights from past experiments buried in PDFs or old presentation decks. A single platform, in contrast, brings all that information together to enable researchers to move from question to answer in days rather than weeks.
For example, this platform approach saved the R&D team from one Top Ten pharmaceutical company four months in the discovery phase of drug development, identifying the right research target and not wasting time on other efforts; on another project, this platform reduced by 90% the time one company expended through their traditional approach of identifying, prioritizing, reviewing, and selecting drug target opportunities, from 60 to 80 days to 4 to 8 days.
Science-based AI platforms scale research across hundreds of mechanisms or indications, applying consistent scientific rigor throughout. They connect internal and external evidence quickly, creating richer scientific context for decision-making. Doing so significantly accelerates research, enabling scientists to accomplish exponentially more than what they had been able to do before.
The result isn’t just faster work; it’s also better science. This platform approach saved another pharmaceutical project an estimated $42M by reducing research timelines by 90% in drug target identification. Cutting decision-making from months to days allows scientists to increase their breadth of research, accelerate the timeframe and quality of target pipelines, and fuel better drug discovery and outcomes.
AI Workflows in Action
A researcher begins by typing a topic into a platform and quickly receives a list of qualified drug targets. This replaces having to juggle a dozen browser tabs from PubMed or ClinicalTrials.gov, or cross-reference multiple spreadsheets.
The next step typically involves examining the rationale behind those targets. Connected pathways, gene-disease associations, and the most relevant studies are displayed and summarized in plain language.
With access to both internal results and public data sets, researchers can quickly distinguish between oversaturated targets and those that hold promise for new therapeutic pathways.
Finally, fully traceable reports are automatically generated, saving hours of manual effort otherwise spent formatting documents and linking supporting data.
73% of Researchers Already Reducing Costs
According to a NVIDIA survey of life sciences researchers, the benefits of scientific AI and unified platforms for drug discovery are already being felt:
- 83% believe AI will revolutionize healthcare and life sciences within five years
- 73% say it’s already reducing operational costs
- 59% of pharmaceutical and biotech respondents cite drug discovery and development as a leading use case.
On average, companies that have leveraged this type of life sciences AI platform can save up to $42 million by making better decisions early on and reducing risk, while life sciences researchers have reported average time savings of 40% by increasing efficiency.
Assessing AI Platforms for Life Sciences R&D
It’s not uncommon for life sciences leaders to suggest that their internal IT department should custom-build an AI platform for research needs. But building an in-house AI platform can become a major distraction from the core business. It’s expensive and takes years to develop. A few years ago, the Harvard Business Review found that businesses were already choosing to invest in commercially available AI platforms rather than build their own.
In contrast, advanced scientific AI platforms benefit from economies of scale. They can deliver what life sciences researchers need, exactly as they need it, while continuously improving and maintaining the platform without burdening an IT department.
The challenge for researchers is ensuring they invest in the right AI platform to meet their needs. Teams should ask scientific and practical questions about the AI platforms they’re considering such as:
- Is the system intuitive and easy to use?
- Are results explainable, trustworthy, and grounded in evidence?
- Will the platform scale to cover multiple use cases and areas of research?
- Does the system support and enhance collaborative research?
The answers to those questions should fall into the following categories:
- Transparency: Every claim the platform makes should be tied to source literature or data, with confidence indicators, to ensure that all information is trustworthy.
- Coverage: Trusted external databases critical to life sciences research, such as PubMed, Human Protein Atlas, or ClinicalTrials.gov, need to be fully integrated into the platform to give researchers the most relevant and up-to-date information.
- Complementary workflows: Purpose-built platforms should support how scientists already work. They enhance existing methods, versus demanding entirely new workflows.
- Intuitive User Experience (UX): Platforms should be designed for scientists, with minimal ramp-up time, not overly complicated for technical audiences, for example, or too basic.
By addressing these concerns, modern scientific AI platforms become not just powerful, but able to fast-track development without adding complexity.
Scientific AI Powers the Future of Life Sciences Research to Scale
Research and development has long required manual processes, fragmented data, and large budgets, even while leading to missed targets, extended time frames, and high costs. Scientific AI is changing this, with platforms purpose-built for research and development that can increase productivity, enhance accuracy and alignment, and create cost-saving efficiencies for the early stages of drug development.