Supercharging R&D in Life Sciences

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Imagine a biotech company successfully integrating AI into its research and development (R&D) processes. Using AI algorithms, users in every division of the company can perform advanced analytics, predictive modeling and simulation studies. These capabilities allow them to quickly identify therapeutic targets, design more efficient clinical trials and enhance drug development. As a result, the company reduces costs, improves patient outcomes and boosts revenue. Additionally, it stays ahead of competitors by quickly adapting to new scientific discoveries and market demands.

How can your life sciences company achieve this level of success with AI? It all begins with a modern data strategy. Such a strategy will help you securely centralize, govern, share and collaborate on sensitive data, providing the foundation needed to power AI algorithms successfully. 

Companies can save untold amounts of time and money by using AI to sift through vast stores of real-world data, omics data and clinical trial data. With this data, there are countless possibilities for what researchers can do. Here are some highlights:  

  • Drug discovery models: Researchers can use AI models to predict protein structures, chemical properties and molecular docking, making the design and testing of new drug candidates much more efficient. 

  • Synthetic data generation for secondary analysis: AI-generated data can mimic real-world data for secondary analysis, helping researchers validate their findings without compromising patient privacy. 

  • Clinical text summarization: With natural language processing, AI can quickly summarize large volumes of clinical data, pulling out the important bits for researchers in no time. 

For these reasons and more, the industry is excited about AI. One report says using AI in drug discovery can slash the time and cost to bring a new drug to market by up to 75%. But on the flip side, it has been estimated that at least 80% of AI projects will fail. Reasons for this include poor data quality, inadequate risk controls, escalating costs and unclear business value.

To succeed in adopting AI, organizations need a robust data strategy that ensures that the data used to train AI models is high quality, relevant and accessible. Here are a few important questions companies should ask before they adopt AI: 

  • What is the total cost of ownership (TCO) for the solution or project, including additional costs like maintenance, training and future upgrades?

The answers to these questions will clearly demonstrate that AI is driving a major shift toward cloud technology. However, adopting AI can present significant challenges related to cost, complexity, security and governance. Retaining specialized hardware and software, along with skilled personnel, can strain budgets, while integrating AI with existing systems and managing large volumes of data can pose difficulties. Additionally, ensuring robust security and governance controls to protect sensitive data and comply with regulatory standards is a necessary but resource-consuming task.