In the rapidly-evolving landscape of drug discovery and development, traditional approaches to R&D in biopharma are no longer sufficient. 

Artificial intelligence (AI) continues to be a game-changer across a wide range of industries, especially with the advent of ChatGPT and Large Language Models. Biopharma is no different, and pharmaceutical companies and contract research organizations (CROs) are feeling increased urgency to integrate AI into their labs and workflows. 

Despite the potential value however, laboratories have been tentative and even sluggish for a variety of reasons. Integrating AI technologies in pharma has historically been an overwhelming proposition, given multiple stakeholder groups, complicated processes, and often bureaucratic technology infrastructure. Organizational decision-makers and even researchers themselves also sometimes perceive AI-enhanced tools as cost-prohibitive, too disruptive to current projects, or requiring a high degree of involvement from their IT department.

Enthought is a globally recognized leader in scientific computing and has been developing transformative solutions that leverage AI to accelerate innovation in R&D for over 20 years. We understand the complexities of pharmaceutical data and processes, as well as the importance of solutions purpose-built for the iterative nature of scientific research. 

With the right AI strategy, research organizations can maximize the output of in-house scientific staff, with minimal IT involvement, and seamlessly scale as their research needs grow.

AI as a Valued Partner in Research

Traditional research methods often involve time-consuming and resource-intensive procedures, leading to inefficiencies and delays. But AI can make a significant impact by revolutionizing the way research labs operate. R&D organizations can optimize processes and enhance decision-making, enabling breakthroughs that were once unimaginable. 

When properly tailored to a lab’s needs, AI technology can fundamentally change the scientists’ work and becomes an indispensable research partner.

Slower, more tedious processes such as the review of data can be handled by AI, revealing new insights, hidden patterns, and data relationships that otherwise might be missed. It can effectively manage large volumes of data that are beyond human capabilities, while correlating findings with institutional data when needed. AI can assist with target identification, lead optimization, toxicity prediction, and much more.

Through the automation of repetitive tasks and optimization of workflows, processes that previously took weeks or months can be reduced to days or even minutes. Pharmaceutical labs that implement AI solutions have a much greater chance of successfully completing testing and advancing to clinical trials ahead of their competitors.

The Role of AI in Streamlining Scientific Workflows

A key advantage of leveraging AI in drug development is freeing scientists from mundane and repetitive tasks, allowing them to focus on high-value scientific work. As is the case across many industries integrating new AI technologies, there are concerns that certain functions and roles will be rendered obsolete. For R&D, however, when implemented effectively, AI will not replace but augment the human scientist’s capabilities while accelerating the pace of research.

AI algorithms can automate data analysis, literature reviews, and experimental design, significantly reducing the time and effort required for these activities. This enables scientists to dedicate their expertise and creativity to more critical tasks such as hypothesis generation, experimental interpretation, and innovative problem-solving.

Through AI-driven automation, organizations can achieve higher throughput and increase overall productivity. This not only saves time and resources but also enhances the probability of success in drug discovery and development. Research teams will also be better prepared to handle increased workloads as projects grow, or even as staffing levels are reduced.

The Limitations of Data Management-Only Solutions

As organizations look to integrate AI into their laboratories, many executives and IT departments try to apply data management-only platforms and tools in R&D, overlooking the unique requirements of scientific data. Solutions and tools that solely focus on data management, however, fall short of delivering real value in the biopharma lab. 

Scientific data is inherently more complex than data in other parts of a business, involving multidimensional datasets, unstructured data, and intricate relationships among various parameters.

The prevalence of unstructured data (images, graphics, videos, distributions, spectra, chemical structures, genetic sequences, etc.) and binary files (vs. csv, text files) requires computational and domain expertise to effectively leverage. Scientific data is also typically generated to answer one research question, and captures only part of the picture. Knowing how to capture comprehensive metadata is key to extracting the data’s full value in order to conduct secondary analysis, data mining, or even experiment replication. And given the exploratory nature of R&D, tools need to be flexible, with the ability to easily accommodate evolving data models to rapidly test new ideas and hypotheses.

To harness the true power of AI in drug development, R&D organizations need technology infrastructure specifically built for scientific data. Enthought has a deep understanding of the complexities of scientific data and workflows and advanced computing techniques. We help labs leverage digital tools for more efficient research workflows to meet critical pipeline milestones. 

"Enthought helps take labs to the next level,” said Dr. Jim Corson, neuroscientist and Enthought Vice President, Professional Services & Customer Success. “We do that by working with them to map out their business goals and then develop tailored solutions to meet their unique pipeline, workflow, and data needs. We get them moving fast and put advanced digital tools directly in the hands of scientists so there is minimal friction in their innovation processes."

A Seamless Transition to AI for Biopharma

Traditionally, the process of developing new drugs has been complex, costly, and slow, with the average time of getting a new drug from discovery to market being 12+ years. As a result, companies in the biopharma space have continually sought to optimize their processes. While these companies have typically focused on clinical development to bring about such improvements, their attention has now expanded into the optimization of the preclinical development stages to accelerate discoveries and reduce failure in clinical trials. The advancements in AI are a key aspect of this shift.

Given the power and potential of AI in R&D, biopharma companies are now heavily investing in the technology. The McKinsey Global Institute (MGI) has estimated that the technology could generate $60 billion to $110 billion a year in economic value for the pharma and medical-product industries. Enthought continues to be at the forefront of this movement, and is uniquely positioned to help biopharma companies transition from outmoded approaches in R&D to AI-driven solutions that accelerate research today while preparing for what’s coming next. 

Contact us to discuss how to integrate AI to accelerate your R&D workflows.