AI Impact on Research and Development (R and D)

Data is the foundation of drug discovery, but for it to be valuable, the data must be well-managed. If it’s siloed, unorganized, or not curated, it becomes meaningless. Data needs to be structured, analyzed, and integrated into a framework that allows researchers to draw meaningful insights and make informed decisions. Without proper data management, its potential remains untapped, hindering the progress of drug discovery efforts.

Data integrity and management with AI is a major concern of R&D scientists around the globe who either already use AI or are considering using it. However, the potential opportunities are so significant they are willing to work through the challenges to reap the benefits it provides:
 

Speed of Processing:

• High Throughput: AI can rapidly process and analyze large datasets, allowing for the quick identification of patterns, correlations, and potential drug candidates.
• Data Integration: AI can integrate vast amounts of structured and unstructured data from various sources (scientific literature, databases, experimental results) in seconds, and provide comprehensive insights.

Volume of Data:

• Big Data Handling: AI can handle enormous datasets, processing millions of data points simultaneously. 
• Pattern Recognition: AI excels at recognizing complex patterns in data that might not be immediately obvious to a human researcher, enabling the discovery of novel drug targets or repurposing opportunities.

Automation and Efficiency:

• Automated Workflows: AI can automate repetitive tasks such as data cleaning, molecule docking simulations, or virtual screening, freeing up scientists to focus on more creative and strategic aspects of drug discovery.
• Scalability: AI can scale to process multiple datasets or run parallel simulations, significantly accelerating the drug discovery timeline.

Enhanced Scientific Insight: 

• AI enables scientists to focus on core scientific tasks by acting as a diagnostic tool that supports strategic planning and experiment refinement. It allows for a more proactive research approach—test, analyze, decide, plan, and then test again—leading to deeper scientific understanding. Through tools like auto-authoring and AI-based analytics, which reveal patterns across multiple variables, AI enhances the overall process of experiment development, driving more effective and insightful research.

With the growing complexity of drug discovery and the urgency to bring life-changing therapeutics to market, R&D scientists recognize these powerful benefits when using AI. 

However, the enthusiasm is tempered by a valid concern: Can we trust the accuracy of the data?
 

Trusting the Data & AI 

Research scientists require vast amounts of accurate data to do their job safely and effectively. Data is the lubricant that drives research forward and AI can’t work without data.

Partnering with AI, which can process millions of data points simultaneously and see patterns that a human might miss, is obviously smart business - provided the data entered in the system is reliable. Ensuring that accuracy is crucial, as the quality of AI's output depends completely on the information it receives.

One way to ensure data accuracy is through the prompt engineering process. This process allows research scientists to carefully craft and refine the prompts given to AI models, guiding them to produce the desired output. When data reliability is in question, scientists must provide precise instructions (or prompts) for the AI to verify the data, such as cross-checking it against trusted sources. This technique maximizes the effectiveness of interactions with AI models. Once this combination is in place, trust in the data output from AI will rise significantly.

Another way of improving data accuracy is to immediately correct any errors found and update the AI system to prevent similar mistakes in the future. Ensuring the accuracy of AI-generated data is a key responsibility of scientists. This approach is standard in deep learning, where models are trained on large datasets. The model's predictions are compared to actual outcomes, and any errors are used to adjust and refine the model. This iterative process helps the model improve its accuracy and generate more reliable content.

It is strikingly clear that the reliability of AI-generated data is not just a technical challenge but a business imperative. Data accuracy directly impacts the effectiveness of AI solutions, making AI more attractive for investment and integration into core processes, which could accelerate time to market. The foundation for trust lies in robust data management systems that capture accurate, contextualized data, ready to be harnessed by LLMs for increased knowledge and accuracy.

Optimizing data management 

Today's R&D scientists encounter complexities in drug design that previous generations couldn’t imagine. With emerging areas like gene therapies and RNA-based treatments, there are potentially terabytes or even petabytes of data that are beyond human capacity to fully interpret. 

The pharmaceutical industry will have to prioritize implementing data management systems that continuously capture and preserve the context of all lab data for input into AI. Without such a system, the potential benefits of a scientist-AI partnership may be lost. 

One such data management strategy is 'late binding of schema.’ This is a method for data capture that addresses both current and future research needs. This approach involves capturing data in a way that allows for flexibility in its presentation and structure, accommodating future changes as needed. It recognizes the evolving nature of scientific discovery, where each experiment may bring new insights. 

By deferring some data structuring until closer to the time of analysis, late binding of schema ensures that data can be adapted to test new hypotheses or refine existing ones, making it a versatile tool for dynamic research environments. “Late binding of schema ensures data remains a living entity, capable of continual re-examination.”

Bridging the Gap: From Data Flexibility to AI Literacy

Understanding the importance of flexible data management is only one piece of the puzzle. To truly harness the power of AI, companies also need to ensure that end users are well-versed in AI concepts, which is critical to long-term success. 

Leadership must recognize that AI’s success in their business depends on how effectively their teams understand, accept, and use it. Digital competency for everyone in the business down to the scientist end users is critical. So, integrating training and, particularly on-going support into the AI implementation process is necessary to ensure efforts aren’t siloed, but rather accepted as a company-wide initiative. 

Another area of particular concern is that some people fear that AI will make their job obsolete. Their concerns need to be addressed and assuaged. Ensuring transparency, using clear communication, and showing them how AI can enhance their roles, rather than replace them, is vital. AI needs to be seen as a valuable tool for researchers, not a threat.

Learning from Failures: Documenting Unsuccessful Experiments

An often overlooked but invaluable component of innovation is the recording and learning from experiments that didn't yield the expected results. Encouraging your research scientists to document these "failures" can provide rich insights that drive future success. Granted, while compliance mandates documentation, an added value of this documentation is that AI can analyze these failed experiments providing scientists with an understanding of what went wrong. It also can prevent repetition of the same mistakes, fostering a culture of continuous improvement.

Understanding failures can be the first step toward future breakthroughs.

From Failures to Breakthroughs

Understanding failures is just the beginning. With high quality data input AI efficiently and effectively models complex biological systems, predicts drug interactions, and accelerates the time to market for promising drugs. AI technology is a tool for scientists that enhances the identification of viable candidates and improves the accuracy of targeting specific diseases, leading to more personalized treatments. 

As the pharmaceutical industry faces increasing pressure to innovate and address global health challenges, AI's role in drug discovery is becoming indispensable. It enables researchers to test hypotheses more rapidly and efficiently than is possible without AI, providing a competitive edge which unlocks new opportunities for market leaders.

A Collaboration with AI

Leveraging AI, Revvity Signals offers solutions such as Signals Research Suite, which enables researchers to make changes to their working hypotheses and experimental designs without having to start from scratch, boosting productivity and shortening the time to market for new therapeutics. This ability to define and refine data structures in real time ensures data remains agile and adaptable—critical qualities that support evolving AI capabilities driving modern scientific discovery.

Read our whitepaper: Harnessing AI for Breakthroughs in pharma and biotech, here.

To understand more about our current AI capabilities: contact us here.