We know that one of the biggest problems in molecular discovery isn't that researchers don't have enough data; it's that they have too much of it, and often in the wrong places. Data overload is everywhere, and with every year, it feels like things are getting worse. The problem isn't that we lack information - it's that we don't know if it's any good, and even if it is, we don't know how to make sense of it. Hence, our obsession.
Humans are designed to process information visually, to show instead of tell. We can see it in the eyes of researchers when we show them that our interactive, structure-based maps of entire molecular idea spaces could replace eyewatering PDFs and spreadsheets they had been using.
When we started, we knew that creating visualizations of data alone would have limitations. We used to talk about collapsing 96 dimensions of data into two so our visually-honed brains could better make sense of it, but we knew that by making it two or even three-dimensional, we would be losing so much.
Imagine trying to visually show how thousands—or millions—of interconnected data points interact. Building data layers, filters, and flexible views, we did everything we could to get around the limitations to show all of the deeper meanings. In our opinion, our IP-GeoScape® visualization tool provides the very best expression of the "idea space" for human consumption within the parameters for now.
In the age of AlphaFold3 and other massively powerful foundational models, the need to reduce the dimensions for the data to be intelligently analyzed have gone away. These non-human intelligences do and will not need a simplified, flattened expression of the data space. So, suddenly, we are free to find a way to express the data and the true complexity of its dimensionality.
Enter the knowledge graph—a way to visualize complex data relationships in a way that's both understandable and actionable. In this sense, a knowledge graph doesn't just show you what's there but how it all fits together. A knowledge graph helps an AI model see relationships, find patterns, and make predictions—much like how the ones we've been building for years have been helping us mere mortals understand things at a glance.
Knowledge graphs are networks where every node is a concept, and every line between them explains how they're connected. It's like your brain's mental map for understanding the world, but on a much larger scale and much more organized. It's data with context, meaning, and purpose. With a knowledge graph, you're not just looking at raw data—you're seeing the bigger picture.
For humans, visualization of information provides clarity; for machines, it's fuel. It connects data points through labelled relationships, giving meaning and revealing patterns that would otherwise remain hidden in isolated datasets. Their ability to integrate and unify diverse, often unstructured data sources makes knowledge graphs so powerful, creating a web of interconnected knowledge. This contextual richness enables advanced reasoning, better decision-making, and more insight, from answering complex queries to powering recommendation systems and aiding scientific discovery. By turning raw data into a map of relationships, knowledge graphs transform information into actionable insights.
Say you're working in drug discovery (as many of our clients are). You have millions of data points—chemical structures, patents, biological activities, and market data—all siloed in different places. Even when brought together individually, these are just dots on a map. But with a knowledge graph, those dots become connections:
Suddenly, you're not looking at isolated data points; you're looking at a story. And better yet, you're looking at the right story—the one tailored to what you need to know.
Taking Knowledge Graphs and "Idea Graphs" Into the Future
After years of working in this space, we've realized what graphs really are: they're bridges. They connect raw, overwhelming data to real, actionable insights. They're how you turn the theoretical into the practical, the chaos into clarity.
So, the next time you hear someone say a picture is worth a thousand words, you can smile and nod because you know that a graph is worth a billion or more. If you haven't started using them to unlock your data's full potential, now's the time. Trust us: once you start using knowledge graphs or "idea graphs," you and your AI will see things very differently.
Our cloud-based subscription products provide context to innovation using data-rich interactive landscapes that accelerate and enhance research, discovery, and commercialization across multiple science-based industries and beyond. Contact us today to understand how to elevate and accelerate your research.