One of the major technologies introduced to the biotech industry in recent years has been artificial intelligence. Artificial intelligence (AI) is technology that utilizes human patterns and behaviors to mimic their problem solving in order to perform tasks like answering questions or providing relevant information.
Alternatively, machine learning (ML), a subset of AI, relies on mathematical algorithms to perform tasks like processing large bodies of data in order to draw conclusions and learn from the results without additional external human input. Artificial intelligence and machine learning are often mentioned in connection to one another.
Using AI as a strategic tool
There are multiple ways in which AI can be implemented to support biopharma operations. And as it is a relatively new tool, we are learning new ways to utilize it every day. Four main areas in which AI has been shown to increase efficiency and improve processes in biopharma include research and development (R&D), drug discovery, development and manufacturing processes, and large-scale data analysis.
Research and development
Due to the pattern recognition and predictive capabilities of AI that far surpass human ability, AI has become a crucial tool during R&D. The research aspect of R&D can involve large bodies of data, which AI can quickly comb through to reach helpful conclusions, including determining viable drug candidates and forecasting their safety and effectiveness.
The utilization of AI during R&D has been found to be historically time and money saving. Not only can AI quickly search through large amounts of information to find target candidates, but it is also able to evaluate more of the intricate relationships between potential drug products and the multifaceted nature of human biology.
Drug discovery
Similarly to research and development, AI and ML are able to expedite the drug discovery process by processing high volumes of data and information to identify new solutions to new and existing health conditions. It should be noted that in addition to AI, we currently have greater availability of relevant and helpful biomedical data.
AI algorithms have been used in drug discovery to predict molecular behavior, delineate pathways associated with certain diseases, expedite compound selection, and increase the probability of clinical trial success.
Development and manufacturing processes
During everyday operations, the implementation of AI and ML can help to automate tedious and routine processes, allotting more time to employees to work on other tasks, which can overall increase operational efficiency. Specific algorithms can also help with minimizing waste, compliance monitoring, and task management.
One particularly helpful implementation of AI for manufacturing and development processes is AI’s ability to run small experiments or run data to find correlations that will allow scientists to set optimal parameters once production begins.
Large-scale data analysis
AI’s ability to process large-scale data is helpful and relevant in almost every step of the bioprocess. Data analysis can be a tedious and long process, and can result in costly human error. By using AI and ML to conduct data analysis, there is a better chance for accuracy, and computations and conclusions will be completed at a much quicker rate. AI can also mine other resources for conflicting or agreeing data.
Implications of AI on the Biopharma industry
The AI market within the healthcare sector has had, and will continue to have a major impact on research and development, drug discovery, data analysis, and much more. As of 2023, there were nearly 270 AI-driven companies in the drug discovery industry.[1] Over half of those companies were located in the US, followed by major hubs in Western Europe, Southeast Asia, and the United Kingdom.
The possible gain for the operational profit of pharma companies across the board with the strategic implementation of AI is estimated to be an additional $254 billion by 2030 worldwide.[2]
Additionally, many mergers, acquisitions, and partnerships have resulted from the rise of AI. Since 2013, there have been around 161 deals focused on enhancing R&D, as well as 95 deals for development and commercialization licensing.[3]