NLP in drug discovery and the quest for the ‘right’ research elements

[Kishore Newton/Adobe Stock]

In drug discovery and development, data sources are as diverse as they are plentiful. There are comprehensive databases brimming with molecular targets, cellular processes, genomic sequences, proteomic profiles, and metabolite patterns that shed light on disease pathways. Data possibilities in the patient care realm are similarly vast, spanning electronic medical records, imaging datasets, and even patient-reported outcomes and adverse events reported on social media. The biomedical research site PubMed has tens of millions of research articles and studies. 

Yet, it’s easier to drown in such turbulent data volumes than it is to swim. Various estimates over the past decade have projected that 80% of healthcare data are unstructured. “There’s a huge amount of information that’s not standardized,” said Jane Reed, director of life sciences with Linguamatics, an IQVIA company.…

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