DrugBank is the world’s largest online database of drug and drug-target information. We provide trusted and structured data to customers and researchers who are discovering drugs and pursuing innovative approaches to healthcare.
Our curation team has pulled together a spotlight of companies at the forefront of their fields that are using DrugBank to discover more.
Check out previous Trends & Insights Spotlights:
Dr.Evidence (AKA Doctor Evidence or DRE) aims to produce better health outcomes by providing solutions for more accurate and efficient decisions in healthcare. Using artificial intelligence (AI), they provide evidence-based answers when posed with healthcare questions.
The three main products they offer are doclabel, docsearch, and docanalytics.
- doclabel is a labelling intelligence solution which informs product strategy and development through precedent research, automated comparisons, and competitive monitoring.
- docsearch is a real-time, AI-powered medical search engine which not only generates actionable insights, it also answers business and research questions based on a collection of published medical information. The docsearch database contains over 65 million biomedical citations, 108 biomedical relationships, and 5.3 million medical concepts.
- docanalytics is an evidence synthesis solution which generates real-time insights that can inform strategy and product differentiation while also exploring hypotheses that help advance market positioning.
Dr.Evidence uses DrugBank data for the purpose of annotating medical concepts within their software platform. Their annotation process involves using machine learning to annotate natural language documents containing medical knowledge.
BenchSci aims to increase productivity and solve problems due to reagent waste during preclinical research. The specific reagent waste they are focusing on is antibodies, although they’ve also expanded into other areas according to their website.
The company employs machine learning in order to teach computers the ability to choose the appropriate reagent(s) for different experiments. The end goal is to be able to reduce the time and money used on drug discovery and development by being able to accurately choose reagents suited for each individual experiment. The hope is that this will result in better research and development outcomes, which will ultimately improve medicine. BenchSci is backed by Gradient Ventures (Google’s AI fund) and states that they are the “industry standard for antibody selection.”
BenchSci AI-Assisted Reagent Selection (This also expanded into additional reagents as well as into model systems).
BenchSci uses the DrugBank knowledge base to support their own knowledge base. The purpose of this support is to develop a biomedical experiment design platform, with the DrugBank datasets assisting with understanding how to make successful experiment designs in addition to other research as well.
Researchers and companies from across the globe, with a range of focuses choose DrugBank as their source for reliable, verified data. From using our structured data to validate machine learning models to finding potential drug candidates, we offer data-driven solutions for many use cases.
DrugBank's database includes more than 21,000 drug-protein interactions, 130,000 drug product listings, drug-target data that includes proteins, gene identifiers, sequences, and so much more.