Data Science Leaders Podcast: Overcoming the data challenges of AI-driven drug discovery

BioLizard’s Volodimir Olexiouk joins Domino Data Lab’s Data Science Leaders podcast to discuss overcoming data challenges in AI-driven drug discovery and how combining biology with data science drives smarter, faster R&D in life sciences.

Volodimir Olexiouk, our Director of Scientific Engagement and Data Science Team Lead, was featured in episode 70 of Domino Data Lab‘s Data Science Leaders podcast.

In this episode, host Kjell Carlsson speaks with Volodimir about best practices for overcoming the data challenges for AI-driven drug discovery and combining scientific expertise with data science for augmented intelligence in the life sciences.

Listen to the whole episode now →

Want to learn more about how leveraging artificial intelligence can support your research & development activities? Check out our white paper about how you can use machine learning to develop better biomarkers – a powerful tool for de-risking drug discovery projects.

How spatial biology improves clinical trial success in oncology

How spatial biology improves clinical trial success in oncology

In oncology, the drug development path is unique: Phase 0 and Phase I trials are typically conducted in patients rather than healthy volunteers, allowing for early assessment of efficacy and patient selection alongside safety. Yet, even with this early clinical insight, many cancer drugs show promise in the lab but fail to transition effectively into the clinic. This often happens because, while we verify that a drug’s target is present, we frequently overlook its context, specifically its location, the surrounding microenvironment, and its interaction with neighboring cells. By revisiting real-world examples of discontinued trials, this post explains why understanding the “where” is just as critical as the “what”, and how spatial biology is positioning itself as a valuable avenue for validating clinical potential.

Why bioinformatics workflows require experienced software engineers

Bioinformatics pipelines break for the smallest reasons: package updates, shifting dependencies, or “it only works on my machine.” This post explains why experienced software engineers and DevOps practices (Git, CI/CD, IaC) are essential to keep workflows reproducible, stable, and scalable.