Bringing Artificial Intelligence to Pharma

Augmenting drug discovery, leveraging A.I

Cutting literature review time by 80%
Make sense of big-data in drug discovery
Ease in biological pathway mapping
Sift enormous data to find ways of drug-repurposing

Why A.I in drug discovery

Small molecule drug discovery (SMDD) poses a multidimensional challenge involving huge expenditure and worse, is heavily time consuming. On an average the invention to market time remains at around 14-years and the costs can go up to a whopping $2.8Bn.

Efficacy and toxicity are two major causes of failures in drug discovery. Huge amount of experimental data is accumulated from the past decades including in vitro (biochemical) assays, in vivo assays and clinical trials. We deploy A.I/M.L engines to this data thus turning it into a valuable source (really available) for learning and understanding the success and failures of compounds in the entire discovery process. Here ‘known data’ is extracted from the data-graveyard and timely knowledge/hypothesis is generated from it by using M.L and deep learning methods.

Problems at a glance

  1. Lack of biological pathway
  2. Difficulty in gaining insights from data-graveyards
  3. Little computational framework
  4. No powerful integrated graph database

What we deploy

  • ML/NLP powered technology to turn random text into actionable data
  • Optimized experimental approach to drug discovery using data
  • Sift through big-data to generate new insights in drug-discovery process
  • Help save 80% time in literature review
  • Effectively curate big-data for Pharma
  • Efficiently mine and build hypothesis using ML/DL models

Solutions we target

  • Making sense of big-data in drug discovery
  • Helping scientists and researchers keep up with the exponential growth of biomedical knowledge
  • Extracting unique actionable insights from Data-Graveyards
  • Building systematic computational framework and integrated databases for biological pathways
  • Deploying machine learning for deciphering quantitative structure-activity relationship (QSAR)
  • 3D Pathway Visualization and prediction
About Us

On a mission to simplify drug discovery using A.I

BioStream is an AI-first company started by a group of techies with strong A.I and finance sector domain expertise. The founders set on a passionate mission to use A.I for solving complex unstructured data extraction problems faced by the Pharma sector.

What started as passion, soon took the shape of a comprehensive drug-discovery and pharma big-data solutions company as our team grew with time. BioStream is now a trusted customer centric AI solutions partner and we love all things big-data for the pharma, right from risk factor detection, exploring treatment landscape from data-graveyards, research trends from millions of sources, literature review and more. At BioStream, we deliver accurate data and solutions to our patrons -all this, while we boast of industry leading timelines. Our mission is to help researchers and scientists make sense out of complex data thus aiding impeccable and timely decision making in their drug-discovery journey.

Given the cumulative experience of successfully navigating 100+ domains, 200+ technologies -no matter what the challenge, we enjoy building seemingly impossible custom-solutions for our patrons.

Partners

Strategic Partners

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