REPO4EU launches DrugRepoChatter, an AI timesaver tool for drug repurposing researchers

Honestly, it’s the sheer explosion of scientific papers out there! Keeping up, especially in a field that’s moving as fast as drug repurposing, is just… insane. As researchers ourselves, we were constantly drowning in papers, trying to find the right tools, methods, and insights. It felt like there was so much great knowledge, but it was all scattered and hard to access efficiently.

That’s the real problem DrugRepoChatter solves. We wanted to build something that acts like your personal expert assistant, helping you cut through the noise and find the crucial information fast. Think of it as taking all that scattered knowledge and putting it into one place, making it super easy to get to what you need. No more endless PDF wading! You ask a question, and boom, you get answers grounded in solid, curated literature.

Picture this: you’re a researcher, maybe a bioinformatician or data analyst, and you’re knee-deep in a drug repurposing project. You’ve got this awesome dataset, and you’re wondering, “What computational tools are actually good for this kind of data?” Instead of spending days Googling and digging through papers, you just hop onto DrugRepoChatter and ask, “What are the best AI tools for analyzing transcriptomics data in drug repurposing?” Within seconds, the chatbot gives you expert-selected articles that break down the best methods, compare them, and even link you to the databases you need. It’s like getting instant, expert advice.

Or, imagine you’re a clinician or biomedical researcher. You’ve got unique patient data and you’re trying to figure out the right analysis techniques. Instead of getting lost in literature reviews, you ask DrugRepoChatter,“Which databases can I use to find validated clinical biomarker discovery in neurodegenerative diseases?” And bam! You get curated insights, pointing you to the best approaches backed by solid research. It’s purely about making research faster and smarter.

Zero, nada! Honestly, we made DrugRepoChatter super simple on purpose. You absolutely do not need to be an AI guru or have used chatbots before. If you can type a question, then you can use DrugRepoChatter – it’s as simple as that. Whether you’re a seasoned pro or just starting out, we wanted to make sure this tool is accessible to everyone.

It has been a joint effort, with many colleagues from across the whole REPO4EU Consortium involved.
We had experts from all over Europe – University of Hamburg (UHAM), Technical University of Munich (TUM), GeneSurge, STALICLA, University of Vienna (UNIVIE), Brigham and Women’s Hospital (BWH), Maastricht University (UM), even Concentris! It was a big group effort.

And the selection criteria were pretty rigorous. We used a structured annotation process in Paperpile, where 13 of us from six different institutions collaboratively labeled publications. We categorized reviews by focus – clinical, computational, tools. Databases had to be usable, either downloadable or via an Application Programming Interface (API). Methods and tools were classified by strategy, technique, and usability – code, package, graphical user interface, API.

Basically, we wanted things that were actually useful for REPO4EU’s work, especially our tasks in WP2 on real-world data-driven AI in drug repurposing. And of course, they had to be either highly cited – 30+ PubMed citations – or very recent, published in the last 5 years.

I want to take this opportunity to mention some of the key people involved in the curation: Markus List, Quirin Manz, Judith Bernett and Johannes Kersting from TUM; Michael Hartung, Andreas Maier, Olga Tsoy and myself from UHAM; Emre Guney, Montserrat Puiggròs and Francesco Sirci at STALICLA; Julia Guthrie from UNIVIE; Ruisheng Wang at BWH; Robert Löwe from GeneSurge; Hermann Mucke from HMPC; and Harald Schmidt from UM.

I spearheaded the development, really making sure DrugRepoChatter brought together cutting-edge AI with a user-friendly design for researchers from all sorts of backgrounds. I’m personally fascinated by large language models (LLMs), and I quickly realized they were a game-changer for how we access scientific info. Think about the time we’re saving researchers by using AI!

I worked closely with computational biologists, clinicians, data scientists within REPO4EU. Collaborators like Lisa Marie Spindler, Farzaneh Firoozbakht and Andreas Maier were key in building the backend, while our clinical partners helped us make sure it was really useful from a translational perspective. It was truly interdisciplinary, which is what makes this project so special.

We know many researchers have accessed it already and are actively using it, which has been really encouraging! The response is mostly about how much time DrugRepoChatter saves on literature searches – that’s a big win. This feedback is gold for us because it helps us keep improving the tool, making sure it really meets the needs of the community. We’re still refining it based on what users are telling us, but yeah, overall the initial response has been great!

We wanted to go a step further than just open access. We aimed to make this knowledge even more accessible by building tools that make it easy to find and use. And let’s be clear: DrugRepoChatter, and tools like it, are completely dependent on open access science. Imagine trying to build this chatbot with articles locked behind paywalls – it’s impossible! The chatbot needs to read the articles, to understand them, to answer questions based on them. That’s why open science isn’t just important for researchers; it’s fundamental to creating the very technology that can democratize scientific knowledge and accelerate progress.