Accessing our in-house literature mining tool for your projects saves a lot of time and gives added value to the project. The platform can be used to query large biological or chemical datasets for a given project, and is also able to find associations that might not be present in manually curated databases.


  • Updated weekly to accommodate new data as soon as it is available.
  • Built on top of industry standard Solr/Lucene engine with query response times of less than 15 milliseconds for complex queries.
  • Drug-Target-Disease associations
    1. - 90,000 important biomedical compounds.
      - 4000 diseases within a custom curated ontology, including custom developed rare disease knowledgebase.
      - 20,400 protein targets

Below mentioned are some of the areas we have utilized the potential of our literature mining tool and it has played a vital role.

Google's Bag-of-Words model used to extract synonyms

  • Given a word, train a neural network to recognize the "context" of the word.
  • Given the context, recognize the word.
  • Given word, reproduce the context.
  • For instance, "TB" and "Tuberculosis" may have similar contexts in the sentences.
    1. - TB kills 2 million people every year.
      - Tuberculosis related deaths have reached 2 million according to WHO estimates.

Semantic relationships using deep learning methods

  • Given multiple instances of a type of relationship, find others of the same type.
    1. - TREATMENT type
      A cures B or A is a treatment for B
      - CAUSE type
      A causes B or A leads to B
  • Based on word2vec models.
  • Trained deep learning models for 8 different types of relationships.
  • High accuracy.

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