AI in a Research and Development Lab: Are We There Yet?
Artificial intelligence (AI) is a term we see and hear a lot nowadays; you could say it’s trending and becoming a household term. It is associated with the things surrounding us daily: home electronics, cars, public transportation, shopping, and more. This term is inclusive and encompasses intelligent products designed to make our lives easier and use computational power to reduce analysis time and effort.
Has this trend reached the benches of the general research and development (R&D) lab yet? Can AI make the lives of R&D lab analysts easier? Well, yes, but not in all aspects of the work—at least not yet. This blog looks at potential applications of AI in R&D labs today and where it might still need to improve.
Where AI Can Shine in R&D Labs
AI has grown in importance for data analytics applications, making the researchers’ data mining or data dissecting easier and enabling data scientists to create timely and actionable models from almost immeasurable data lakes, warehouses, and fabrics. In drug development, AI can help cut development costs and the risk of failure with preclinical analytics.
AI is often the basis for an autonomous lab, where AI-powered data analysis and robotic systems can discover and synthesize new drugs, for example. AI can automate repetitive tasks involved in scientific research, like simple pipetting or weighting. AI is making inroads to improving manufacturing yields by learning from existing quality, environmental, and manufacturing data to predict actions that can increase yield, address quality issues, or optimize capacity.
AI-based hands-free digital lab assistants, like LabTwin, are making lab life more efficient and accurate. A digital lab assistant can talk researchers through common methods and help them develop new ones. Simple queries about scientific information like molecular weights or dilution ratios are also possible. Extracting information from a data repository and analyzing it for overlooked insights or using it to predict future research targets are real-world examples of promising applications for AI in labs.
Where AI Still Needs Development
There are myriad processes in an R&D lab that could be automated and are not… yet. Artificial intelligence is evolving and growing, and in an R&D research lab, its applications are just in their infancy. Most AI models still require a human to check the results and perform validating experiments to ensure that the suggested processes or materials will work at scale.
Data collection and transfer from scientific instruments are key areas where AI can be developed in an R&D laboratory. However, implementing a comprehensive solution is difficult because of the numerous distinct instruments and their associated software. Existing data siloes keep the data separated and form barriers to the adoption of AI. A key framework that can help with gathering the data from instruments so that it can be accessible to AI will be in data harmonization. This is where the FAIR data principles and efforts of organizations like the Pistoia Alliance or AnIML will become invaluable.
In some instances, machine learning algorithms are used to analyze data and identify patterns to aid in automatically transferring data from the instrument to the data collection area, but this is just the beginning. Furthermore, master data management tools are developing promising capabilities to ease scientific data integration. Data transfer and management tools are entering platform technologies such as Zontal, Scitara, Ganymede, and others. However, this is an acknowledged area where improvements are needed.
Another area where AI struggles with broader adoption is the issue of trust. Skepticism of AI and its methods abounds. Trusting sensitive personal information to an AI tool isn’t yet seen as a wise business move. The processes behind AI tools are not as transparent as they could be to improve the public’s confidence in the results. Some AI-generated models and results have raised ethical issues, as well. Regulated businesses will shy away from AI-based tools until a model is developed to ensure that the tools are regulated, reproducible, and traceable.
We look forward to the day when AI can identify and implement data automation from any scientific instrument to the best data collection area for that data type; meanwhile, we rely on human-based intelligence to automate every instrument in an R&D lab.
How comfortable are you with using AI and machine learning in your scientific research?
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