Artificial Intelligence

Combine deep learning and semantic algorithms to build powerful models that can exploit life science data and accelerate its use in R&D

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Within the pharmaceutical industry, the combination of Artificial Intelligence (AI) and big data is triggering a revolution across the entire drug development lifecycle; from the way new drugs and treatments are discovered to identifying opportunities to re-purpose those already in the market.

For a sector that has seen the cost of bringing a new drug to market rise from $1.2bn to $2bn in the last ten years, and its return on investment drop from 10% to under 2% over the same period, AI has the potential to deliver unprecedented productivity improvements and drive better outcomes for both pharmaceutical companies and patients.

The challenges posed by big data were well articulated in Doug Laney’s benchmark definition (the so-called 5 V’s):-

  • Volume: The quantity of data produced across all sources.
  • Velocity: The speed at which new data are created, collected and analysed.
  • Variety: The different types of data being created, including structured data sets, semi-structured data and unstructured text.
  • Veracity: The unpredictability of the data collected. Is it of good quality? Did it come from a trusted source?
  • Value: The worth of the data being collected.

Within the pharmaceutical and healthcare sectors, big data represents an even greater hurdle as approximately 80% of clinical data is stored as unstructured text.  AI techniques such as text mining and Natural Language Processing (NLP) are therefore required to identify concepts, entities and relationships within the document corpus.

While the volume and variety of big data represent a major technical challenge to any pharmaceutical organisation, the payoffs are also substantial: enabling patterns and trends to be identified that can inform decision making at all stages of the drug development process.

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