Data science, meet drug discovery

At its core, science is about discovering hidden truths. Some of those truths are so microscopic or macroscopic that only technology can aid their discovery. Nearly every major breakthrough in science that has advanced human well-being started with a question and was uncovered through an often tedious and arduous experimentation process.

Scientists are being pushed to innovate rapidly, consistently deliver, improve ROI on capital investments, implement adaptable processes, collaborate more broadly and convert data into insights. Yet too often, scientists have found that data science has not matured to the extent of its older sibling, science data.

It takes on average 10 to 20 years to develop and bring a drug to market in the U.S.—and only 5% of compounds from the discovery stage ever launch. According to Accenture, the average cost to develop a new drug is between $2.6 and $6.7 billion. In a recent study 80% of scientists said that the workarounds currently required to get data into meaningful outputs are negatively impacting their work and almost 70% reported compromised decision-making because of this.

One thing the industry has been missing is an integrated R&D platform with the depth, breadth and connectivity to the best scientific applications to help solve these complex data challenges in labs. That means scientists working on life-saving discoveries struggle with their data in silos, and they are using dozens of favorite apps to aid them in the process.

That’s where Dotmatics comes in. We give scientists the first true end-to-end solution for scientific R&D. And based on research we’ve conducted with a third party, we’re finding that by using that technology, scientists are realizing up to 70% reduction in time spent on data integration and analysis and up to 50% reduction in time spent on documentation.

The roots of Dotmatics’ story comes from multiple startup science application companies that were created for scientists by scientists to help address issues they were dealing with in the research and development process. GraphPad Software was founded more than 30 years ago by Dr. Harvey Moltulsky, who was looking for software that could provide graphing and biostatistical features to aid scientists. SnapGene was established in 2004 by founder Benjamin Glick, Ph.D., a faculty member in the University of Chicago’s Molecular Genetics and Cell Biology department. Glick’s frustration with significant inefficiencies in software while cloning DNA in a lab led to the creation of SnapGene. 

In 2021, all of these solutions were brought together under one roof of Dotmatics through its merger with Insightful Science. The result is an 800-person-strong team that is unified around creating a core scientific platform. We know that today science is multimodal. Scientific domains are more complex. Data must be able to flow between areas, teams and organizations. Ultimately innovation and transformation are as much about data science as science data. And it’s the harmonization of those two things that will drive transformation.

But how do you go from being data-driven to data-led? The silver lining of that future is the advancements in computational power, creating the possibility for AI and machine learning. Scientists have looked forward to the promise of a digitally transformed lab, one that offers unified solutions for connecting science, data and decision-making.

The realization of this promise would be data science and science data on an equal playing field using innovative technologies that support chemistry, biology and formulations in ways that speed time to market and reduce costs.