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How AI Is Transforming R&D (for the Better)

Use Cases & Projects Catie Grasso

The impact of AI on various business units throughout an organization spans from finance and marketing to HR and customer service — and certainly doesn’t stop when it comes to the research and development (R&D) arm.

Not only does AI promise to improve existing goods, services, and organizational operations and efficiency, but it will play an important role when it comes to shaping the future of innovation and R&D. Just as the computer revolution brought about significant reductions in the costs associated with manual calculations and the rise of the internet transformed the way we access information, AI — while still nascent in the enterprise — can help organizations reduce costs and simultaneously tap into new business opportunities.

In a report by the National Bureau of Economic Research, the authors (economists at MIT, Harvard, and Boston University) argue that AI is in a lane of its own as a research tool that can be applied to any domain and doesn’t discriminate based on the organization’s industry, size, or makeup.

Machine learning algorithms play a key role in research problems that require classification and prediction through their ability to aid in cost optimization and improve performance across R&D projects where these tasks pose roadblocks. A lot of innovation and R&D involves making predictions based on data and, with machine learning, these predictions can be done in a faster and less costly way.

Okay, but how can this be practically implemented? We’ll provide context and give some examples from the pharmaceuticals industry.

AI in R&D for Pharmaceutical Drug Discovery

Overall R&D spending in the pharmaceutical industry is slated to reach nearly $204 billion by 2024. With drug discovery development, for example, pharmaceutical companies can use machine learning techniques to comb through academic literature and journal publications using natural language processing (NLP) to identify compounds that they should experiment with.

By scouring both scientific literature databases and patient-level data, R&D teams can leverage AI to propose a drug target (a molecule in the body that is associated with a particular disease process and could be addressed by a drug to produce a desired effect), design a molecule, and find patients to test the molecule with.

Further, today it can take an average of 20 years to come up with a new material, from the time the idea is introduced to insertion. With AI, the materials development process can be expedited, giving R&D teams — such as within biopharma organizations — time back in their day, which will, in turn, allow research productivity to skyrocket.

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