Trends & Insights: Researcher Highlights Fall Edition

Discover how Academic Researchers use DrugBank data to power groundbreaking research.

Trends & Insights:  Researcher Highlights Fall Edition

DrugBank is passionate about propelling innovation and equipping leading researchers with the data they need for new discoveries. As a trusted partner to numerous world-renowned researchers, we are proud to be contributing to groundbreaking research and aiding in solving some of the industry's most pressing issues. Our products, including limited free datasets and paid licenses, help academic researchers drive innovation and push research further.

Our Curation team has put together a highlight of some of these Academic researchers, the impressive work they have been focused on in 2021, and how they’ve powered their research with DrugBank’s data:

Dr. Hua Gao

  • Postdoctoral researcher, Leeper Lab, Stanford University
  • Ph.D. in Bioinformatics, Peking University

Research focus: Ph.D focuses on studying human genetics, discovering the casual and susceptibility genes for childhood neurological diseases by using the next-generation sequencing technology. Current research focus is the genetic and epigenetic profiles in the single-cell level under the progress of atherogenesis.

DrugBank powered research: Dr. Gao plans to utilize DrugBank’s extensive Structured Drug Interactions to assess drug-symptom relationships to filter out candidate drugs with the goal of validating a drug predictive model.

More info:

Dr. Monte Winslow

  • Member of Stanford Cancer Institute and BioX
  • Ph.D. in Immunology, Stanford University

Research focus: Using unbiased genomic methods and in vivo models to better understand the molecular and cellular changes that underlie tumor progression and each step of the metastatic cascade.

DrugBank powered research: Using DrugBank’s drug and drug target datasets Dr. Winslow hopes to identify compounds that could be employed in lung cancer mouse models to potentially treat cancer cells.

More info:

Dr. Jun Wen

  • Research fellow in biomedical informatics and postdoctoral fellow, Harvard Medical School
  • Ph.D. in Computer Science, Zhejiang University
  • Funding from the VA Medical Center
  • Works with Professor Tianxi Cai in the Translational Data Science Center for a Learning Health System (CELEHS)

Research focus: Using a computer-aided healthcare system with the aim to help people enjoy a healthier life. He also focuses on EHRs data, knowledge transfer, and multimodal learning.

DrugBank powered research: Dr. Wen hopes to use DrugBank’s structured data, focusing on drug-symptom relationships and adverse effects to filter out candidate drugs with the goal of validating a drug prediction model.

More info:

Dr. Anne Trinh

  • Senior research officer, Garvin Institute for Medical Research
  • Ph.D. in Oncology, University of Cambridge

Research focus: A retrospective analysis of five colorectal cancer cohorts including two clinical trials to identify characteristics of patient subpopulations amenable to existing therapies. She’s also investigated the role of the immune system in breast cancer progression and immunotherapy strategies towards progression.

DrugBank powered research: Using DrugBank’s highly structured data, Dr. Trinh hopes to find drug candidates that can target cancers based on their mutational profile with the end goal of finding personalized medicine for a given cancer patient.

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Dr. Woo Sung Son

  • Associate Professor, CHA University, South Korea
  • Ph.D. in Physical Pharmacy, Seoul National University

Research focus: His past research has included studying nuclear magnetic resonance (NMR) spectroscopy of proteins.  He focuses on dynamics and molecular dynamics simulation of antimicrobial peptides in membranes, structural studies using solution and solid NMR on membrane proteins, and membrane protein works including cloning, expression, and purification.

DrugBank powered research: Dr. Son intends to use DrugBank for new chemical discovery and target identification for cancer and has the end goal of confirming and verifying a machine learning model.

More info:

Researchers with a range of focuses from across the globe 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. With both paid and free academic licenses available, there are options to suit all of your research needs.

Sign up now to explore our extensive datasets and see how we can help you get to your findings faster.