The development of a new drug takes an average of ten years from discovery to approval, with the discovery and pre-clinical stages taking three to four of those years. Critical to the development process, successful drug research and discovery requires analyzing numerous data sources such as clinical trials, pharmacology reports, small compounds and more. These data sources are rich with valuable insight but largely unstructured making them increasingly challenging to analyze via traditional methods. 

The industry estimates that it costs $2.7 billion to bring each drug to market and that worldwide spending on pharmaceutical R&D will reach $230 billion by 2026, accelerating the research and discovery process poses significant time and financial savings. 

Accelerating R&D with Deep Learning 

When considering deep learning and artificial intelligence, most think of the technology’s ability to predict outcomes. While these capabilities show future promise, there are areas where deep learning is making an impact today – particularly in enabling individuals and organizations to unlock and operationalize challenging data sets. 

Advanced deep learning models are improving just that. At Vyasa, we’ve developed a series of deep learning applications that enhance the way individuals and organizations alike explore and uncover insights in their data through natural language question answering. 

We’re collaborating with NVIDIA to enhance the deployment and use of Vyasa for life science organizations by streamlining integration and workflows through solutions such as NVIDIA AI Enterprise software suite, NVIDIA Clara Discovery and more. These platforms can be combined with innovative computing and storage hardware from Dell Technologies to increase capacity and provide a data fabric solution within familiar server environments.

Vyasa for Enhanced Drug Discovery 

Backed by powerful deep learning models, Vyasa has developed a suite of applications that life science companies can leverage to streamline the drug discovery process by:

  • Unifying Siloed Data: Vyasa’s core solution, Layar, is a deep learning data fabric that allows organizations to unify disparate data sources regardless of file structure or storage location.
  • Querying Your Data in Natural Language: Vyasa enables users to ask questions of their data in natural language, allowing for easy data exploration in a low code environment.
  • Exploring Your Data in Highly-Visual Formats: Users can explore query results in highly-visual knowledge graphs and smart spreadsheets, accelerating their time to answer. Named entity recognition models built within Vyasa highlight key concepts streamlining a user’s ability to identify key insight. 
  • Turning Unstructured Data into Structured Insight: Insights discovered within Vyasa can be extracted into common formats including .csv, JSON and GraphML immediately turning unstructured data into structured insights.

As the world continues to grapple with emerging diseases amidst global outbreaks, the ability for life science companies to quickly analyze data and identify drug candidates is critical to maintaining public health.

By combining deep learning with a data fabric architecture, Vyasa is helping life science professionals accelerate drug discovery by improving query accuracy by 97% while reducing research duration by as much as 90%.

Vyasa will be presenting on its deep learning applications for healthcare and life sciences with Dell Technologies from May 3-5, 2022 at Bio-IT World Conference and Expo. Join us at Dell Technologies booth #913.