Hyper Lab interview with José Antonio Costoya

In this Hyper Lab interview, José Antonio Costoya, Head of the Molecular Oncology Laboratory at the University of Santiago de Compostela CiMUS, talks about his research and the AI drug discovery platform Hyper Lab.

Introduce yourself

I am José Antonio Costoya. I am the Head of the lab of molecular oncology in CiMUS at University of Santiago de Compostela.

And our main goal is to elucidate some oncogenic mechanisms with the purpose of finding new targets both for diagnosis and for therapy.

I mean my background is mainly biomedical; I am an MD, not a chemist.

What's the hardest part of your research?

I think it is crucial to manage all this amount, big amount of information and be able to get the real important information for your work.

Did Hyper Lab help you solve that challenge?

All the information you have from ADME/T from the small molecules in that, you basically brought in your your database that you are linked with your tool. I think it’s really useful, really fast, and I mean can be really helpful for us.

I think that it's a really cool tool. I mean, as my first contact was at the conference, I feel that was an interesting tool for us because, First of all, I think it's quite important for this kind of tools especially for the researchers in general and all the people who’s working and specializing in, for example in AI or in specific tools,

That it’s user-friendly. I think is crucial.

And of course, as a starting point and to play with some data we have and with both from especially for docking some targets that we have proteins that we have- with some small molecules and was really cool to see, to visualize. You get a really nice visualization.

Do you think research using AI is accurate?

We are a wet lab and we need to use these tools, but we are not specialized on those tools. We are not focused on those tools.

José Antonio Costoya's first encounter with Hyperlab was at AACR 2024.

AI Drug discovery platform, it's really powerful. we experiment that it’s still learning and growing.

but you still need experimental information to reach the final point. I don't know if it's fortunately or unfortunately, but the experimental part is needed.