& Life Sciences
Discover how NaturalText A.I. is used during drug discovery to make early-stage research and development more efficient.
PHARMACEUTICALS & LIFE SCIENCES USE CASE
Drug Discovery & Target Identification
R&D in the pharmaceutical, biotechnology, and life sciences industries incur a daily flood of data—mostly in the form of unstructured text. While this provides fertile ground for drug discovery and pipeline development, the huge volume of information also challenges traditional search methods at each stage. First, mapping the disease pathway and genes involved requires a comprehensive review of public domain literature. Examining patent filings are also critical for understanding the current market and its gaps, which key intervention targets should be prioritized for development, and unmet medical needs.
Staying abreast of the latest developments is necessary to maintaining your competitive advantage, but traditional search methods are not equipped to meet the demands of today's seemingly-limitless supply of literature and data. Critical pieces of information can fall between the cracks.
Research is filled with jargon that keyword search ignores, making it extraordinarily difficult to compare, rank, and cluster data within and across organizations.
Manual methods of handling data, the lack of a standard storage format, and data silos frustrate attempts to integrate data for better understanding and usage.
Complicating things further is the massive size of R&D datasets: with dimensions in the millions, it can be nearly impossible to get any output at a business-viable cost.
Traditional search methods make conducting research very costly: you must guess the keywords that will return the most robust and relevant set of results, search keyword-by-keyword, and then spend countless hours determining what is useful. The volume of information available would take far too long for a human to read and digest, but machine learning solutions designed to fix this issue have their own limitations.
Algorithms are only trained to deliver results based on your interests and reading habits. This stunts your ability to find related and eye-opening results from fields outside of your expertise, or interdisciplinary research projects. Different data formats must be analyzed separately, causing the cost to balloon. Both of these types of solutions have no concept of context, leaving you to conjecture about relationships and trends buried in the data.
Next-generation text mining solutions, powered by natural language processing, can help your organization access more information in less time, uncover new insights from existing information, and be confident in the evidence supporting strategic decisions.
Traditional keyword search excludes jargon, synonyms, and misspellings. It may also return irrelevant results, such as documents appearing to use the same keyword, but with a different meaning.
Today’s enterprise search platforms rank results based on the number of times a keyword appears in each document, not contextual relevance. This still forces users to read each result and decide, on their own, which ones are most relevant.
Machine learning methods require extensive preparation, training of data, and tuning and retraining over time, incurring high upfront costs. Each model is prepared for one set of data, and cannot be applied across domains (contexts) or data formats.
Because NaturalText A.I. understands the content and context of text data—structured and unstructured—you can be confident that you have the latest, most relevant information. Its powerful natural language processing technology can integrate various data formats from internal and public sources, revealing hidden relationships and insights that other software solutions miss. Search for multiple keywords or concepts at a time to extract existing relationships, such as between genes and pharmaceuticals, or uncover new relationships, such as between a disease and a mutation.
On their own, researchers and scientists would not be able to stay up-to-date on the constantly-evolving landscape of information, including patent filings, internal documents, scientific literature, clinical trial documentation, and competitor intelligence. NaturalText makes it possible to use the latest developments to your advantage and acquire efficiency during throughout the drug discovery process.
Works out-of-the-box to save companies and labs valuable time and money—no need to tag, train, and tune models!
Produces both results and rationales behind the results in human-readable formats, making it infinitely easier to analyze, understand, and explain
Leverages information more effectively through seamless integration of text data, bio-sequences, and other formats, so you can make informed decisions more quickly
Enables you to search billions of concepts and entities to identify hidden trends and relationships, and find new information
Draws the most value out of existing intellectual property and prior research