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

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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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How AI-based technologies improve clinical trial design, site selection and competitive intelligence

[Photo by Tara Winstead on Pexels]

Clinical trials form the cornerstone of evidence-based medicine and are essential to establishing the safety and efficacy of new drugs. However, only some of the information in clinical trial reports is well-structured and searchable via keywords; much of the information is buried in unstructured text.

In the past, uncovering actionable insights from this unstructured text meant that documents such as clinical trial reports had to be searched and read individually, a process that can be time-consuming and subject to human error. It is estimated that 80% of clinical data is unstructured and difficult to analyze.

To overcome these limitations, life sciences companies are using natural language processing (NLP), an artificial intelligence-based technology that extracts and synthesizes high-value information hidden in unstructured text. NLP-based text mining solutions can an…

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