DrugBank is the world’s largest online database of drug and drug-target information. We provide trusted and structured data to researchers who are discovering drugs and pursuing innovative approaches.
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 and Insights Spotlights:
Harmonic Pharma [Lead optimization]
Harmonic Pharma specializes in quality chemical information that they use to assess risks and benefits for their customers. Specifically, they can seek successful candidates for trials by finding toxicity issues in early stages of drug development. Harmonic’s technology uses predictive modeling to guide users in selecting drug compounds for their research as a means of shortening the time it takes to get a drug to market, thus reducing their overall costs.
Harmonic Pharma relies heavily on DrugBank data for predictive modeling in drug repurposing, and to compare their own new molecules against.
They are quite well-known for serving a wide range of industries, including pharmaceutical, nutraceutical, cosmetics, and chemical. Some of their most notable clients include L’oreal where they help find alternative compounds that are deemed less toxic and Institut Curie which focuses on the treatment of Non-Small Cell Lung Cancer.
Dr. Evidence [Hit Identification]
Dr. Evidence (also known as Doctor Evidence or DRE) is a medical intelligence platform that aids users in identifying insights from complex clinical data, published medical information, real-world evidence, and proprietary data. DRE focuses on the AI-enabled health technology market, and is classified under IT Services and IT Consulting.
Dr. Evidence uses DrugBank in multiple ways, including leveraging machine learning to annotate natural language documents containing medical knowledge, integrating DrugBank API into their software products, integrating DrugBank data for drug repurposing, and developing COVID-19 responses through drug repurposing.
BenchSci [Target Identification]
BenchSci prides itself on reducing the time it takes for scientists to complete their experiments. They do this using their AI powered platform to help researchers find proper reagents, antibodies, and other chemical entities.
BenchSci uses DrugBank internally to help with their research and development of AI technologies for experiment design in drug discovery.
Some of their most notable customers include AstraZeneca and Moderna.
Valo [Hit identification]
Valo, also known as Valo Health, aims to transform drug discovery and development through human-centered data and machine learning.
Valo Health uses DrugBank’s pharma data for in silico testing for drug discovery where they leverage integrated biology in order to investigate health and disease states. They do this as a means to identify ways to manipulate underlying networks and create therapeutic benefits.
They have partnered with organizations such as Charles River Laboratories (pharmaceutical company) and Global Genomics Group (where Valo gained access to a large and detailed cardio-metabolic dataset).
Spring Discovery [Hit Identification, Target Identification]
Spring Discovery focuses on targeting age-related diseases in order to prolong lifespans. Their machine learning is used to find therapies that will hopefully progress to clinical stages for faster treatment of the patient.
DrugBank plays a role in their search for these therapies and helps cut down the time it takes during the discovery and experimental phases.
VantAI [Hit Identification, Target Identification, Lead Optimization]
VantAI states that it is “the world's leading engine for the computational design and optimization of targeted protein degraders.” They specialize in de novo drug design, target prediction, interactome mapping, and in silico ADMET. VantAI covers many areas of drug discovery which makes them a well-rounded contender to partner with several pharmaceutical and biotech companies to progress AI within the field.
VantAI integrates DrugBank’s API into their platform for drug repurposing. An example of this is their work predicting side effects and in silico toxicology.
Systems Oncology [Target Identification, Hit Identification]
Systems Oncology mines biomedical data to find missing opportunities in cancer research. After determining a suitable candidate, they begin experimenting so that they can vouch for the candidate’s potential to be therapeutically significant. The hope is that by collaborating with pharmaceutical companies these prospective molecules can be life-saving cancer therapies for patients.
Systems Oncology integrates DrugBank into its drug discovery platform. Through aggregation and mining of data, they can spot areas of cancer research that have a high probability of creating a breakthrough in science.
Researchers 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.