Small molecule discoveries comprised 62% of drug approvals in 2021, including new treatments for cancer, HIV/AIDS, and infectious diseases. Maintaining the necessary pace of innovation in the small molecule drug discovery space requires the development of tools to optimize the collection of data and its gathering, analysis, and visualization across activities including hit and lead identification, lead optimization, and candidate selection. Dotmatics has developed a Small Molecule Drug Discovery Solution to help enhance insights and innovation, improve productivity and collaboration, and drive operational efficiency during drug discovery. In this Q&A, Melanie Nelson, Ph.D., Senior Solutions Architect at Dotmatics, discusses this solution and the broader focus and expertise of Dotmatics.
David Alvaro (DA): Can you start by sharing some historical background on Dotmatics and the company’s vision?
Melanie Nelson (MN): Although I haven’t been here from the beginning, Dotmatics was founded with a focus on helping scientists find and use their data. The difficulty that researchers faced in finding and integrating existing data was the fundamental problem our founders were trying to solve when they developed the technology that eventually spun out from Merck Sharpe and Dohme, and it has been a focus for us ever since. Too often in our industry, tools focus on capturing data without really considering how the scientists will ultimately access and use that data. Don’t get me wrong — data capture is essential; but it’s really just the first step of good scientific data management.
Scientists cannot get value from the data that was captured until they can easily access and integrate it with other bits of data to help drive decisions, and our mission has been to help scientists unlock that value. Now that our company includes beloved scientific applications like GraphPad Prism, we have even greater opportunities to do that. It’s a really exciting time for us.
DA: Before we focus on the Small Molecule Drug Discovery Solution, can you share an overview of the full breadth of your portfolio of products and their applications?
MN: Our platform and scientific applications have capabilities that reach across a wide range of scientific processes, including but not limited to drug discovery, both in small molecule and biologics, chemicals and materials, and even agricultural science. The key connecting thread in all of our products is that they help support scientists in their research workflows. I think that’s really the key message more than a laundry list of product names: we focus on helping scientists support their research workflows and streamline them.
DA: Is there added value for a particular lab or larger research organization to leverage multiple Dotmatics products or pieces of software?
MN: Yes, definitely. Obviously, within our platform things have been built to flow seamlessly, and, now that we have this larger family of scientific applications, there’s a big focus on making that integration seamless as well. As a scientist — I have a Ph.D. in biochemistry and I’ve been doing scientific informatics for more than 20 years — I’m very excited about the possibilities as we bring in these scientific applications and integrate them with our platform. I think that’s really a key value driver for us.
DA: Can you discuss your product development process? Where do the ideas come from and how are they nurtured?
MN: We’ve been implementing scientific informatics systems to support small molecule drug discovery for more than 15 years. It was our initial founding space; so that’s a lot of experience as a company. We also have a very collaborative culture that brings together people from a variety of different backgrounds and specializations, and we come together to exchange ideas and identify best practices, both from what we learn from our customers and what our team members bring to Dotmatics from other places. We leverage all of that experience to develop products like the Small Molecule Drug Discovery Solution.
In addition, over the years, we’ve been working with our customers to understand their specific needs, determine how to map those needs to best practices, and collaborate with them to define the best approach for that particular customer. We plan to continue to do that, and as we do, that process will feed back in and help us enhance our solutions in future versions. It’s really an iterative process, taking ideas from anywhere we find them and trying to map those best practices and deliver something that’ll help our customers.
DA: As part of this iterative feedback process, do you draw from a combination of ideas that are coming directly from customers and proactive work that you do to anticipate what customers may want in the future, even before they realize it?
MN: Definitely. Because so many of us come from diverse industry backgrounds, we bring in ideas we’ve seen elsewhere that we can then feed in. We’ve always done that with our customers. Dotmatics has traditionally had really strong science backgrounds within our company.
DA: Focusing now on the Small Molecule Drug Discovery Solution, can you tell me about some of the challenges facing small molecule developers that you sought to address?
MN: I think that a lot of drug discovery folks would say that their challenge is biology: it's complex and tends to foil the best laid drug discovery plans. We can’t fix that. But what we can do is help them with their challenges in leveraging all of their available data to make the best decisions about what molecules to synthesize next. We significantly reduce the labor-intensive, error-prone data analysis and data transfers that are so unfortunately all too common in the industry today. Doing that will help them make better decisions, which should ultimately drive better outcomes for their drug discovery projects.
DA: In terms of the scope of the workflow of drug discovery, where does the solution provide that support?
MN: We support the innovation cycle that often gets described as “make–test–decide.” We particularly focus on the lead optimization phase; although we provide tools that can be used in other phases, lead optimization has been our key focus. To expand on this “make–test–decide” cycle: compounds are synthesized in the make phase and assayed in the test phase. Then, in the decide phase, you bring those assay data together with other data, such as compound properties, to really do your structure activity relationship (SAR) analysis and decide what steps to take in your next loop around the cycle. Our solution supports all phases of that cycle. There are templates to guide chemists through designing both singleton and array synthesis experiments and capturing the experimental data details of those experiments. There are assay data capture and analysis capabilities and capabilities to review that assay data to enable researchers to easily move into the SAR process.
We also provide the pieces to help stitch this workflow together. One thing I’ll call out in particular is the ability to track compounds as they’re sent out to contract research organizations (CROs) for synthesis and to make and track assay requests. Those are two places where the project team often really struggles to keep track of what’s going on, and they will often fall back to inefficient spreadsheets and the like.
DA: Is there a fundamental core technology underlying everything that makes all these applications possible?
MN: Our solution is cloud-based, which facilitates the collaboration between CROs and external strategic partners, which is becoming more and more common in drug discovery today. The Browser technology was one of our founding technologies and has been at the core of our offerings from the beginning. It’s at the core of the solution, helping us stitch the data together and bring together the disparate pieces of data relevant to the scientist. Our deep experience in drug discovery informatics has allowed us to leverage that technology to build an integrated solution tailored to the small molecule drug discovery process.
DA: I would imagine that this kind of integrated solution can replace a very piecemeal mosaic of individual systems from individual vendors that may not have integrated well. How big of an issue was that, and what are the real benefits of that integration?
MN: I’m so glad you asked this question, because it’s near and dear to my heart. Before joining Dotmatics, I spent the majority of my career stitching together workflows, using multiple different systems from different vendors, because that’s what scientific informatics specialists had to do to support our scientists’ needs. It is very exciting for me personally to be part of a team building an integrated solution that makes it possible to avoid cobbling together workflows like that.
The problem with the cobbling together approach is that you spend a lot of time shuttling data between systems. It often requires converting data across different formats, and each transfer risks either data loss or data corruption. For instance, a field could mean one thing in one system and another thing in another system, and if you don’t catch that and map it properly, your data gets corrupted. It can also be a fragile approach — the strategic linkage of the systems is very fragile in that it can break any time a single vendor upgrades their piece. Then you have to go back and look and say, “Hey, can I make this whole workflow still work?” Using a single vendor avoids all those problems and can represent really significant time and cost savings for the customer.
DA: Even with this integrated solution, there is probably some need to have it be able to interface with some additional systems. Is it still a challenge to interface with different vendors using different data formats, or are those becoming more standard?
MN: I’d say it’s getting better across the industry. At Dotmatics, we have published application programming interfaces (APIs) that people can use to access our data. We can also use APIs to import data from other systems into our system, and that’s something that our browser was initially built to do.
DA: Dealing with the diversity and the complexity of the inputs is a big part of the challenge, but the other part is making the data usable and targeted for the user. Could you tell me a little bit about how you work to create an optimal user experience and who you see as the potential users of the solution?
MN: User experience is very important to us, and it is very challenging in such a complex space. We are continuously actively innovating to improve it, and we think that’s the only way to get to a good user experience. We have a user interface (UI) and user experience (UX) team who do proactive research to figure out how best to improve our UI and UX, and we listen carefully to the feedback from our customers on what does and does not work for them and feed that into next iterations of solutions. Our ultimate users of this solution are scientists and scientific decision makers, including medicinal chemists, assay scientists, and project leads.
DA: Could you concisely sum up what you see as the full set of benefits that a potential customer would gain from utilizing this solution in terms of efficiency, time, error avoidance, costs, and so on?
MN: The Small Molecule Drug Discovery Solution will reduce errors and improve manual data transfer and convergence. It will increase efficiency by making it easier for scientists to track their experiments and the data they produce, and that should therefore speed drug discovery timelines.
DA: In terms of the relationships that you form with customers, do you typically look for an ongoing partnership where you not only provide products but also supportive services?
MN:MN: We prefer and we try to be an ongoing trusted partner. As their science changes, their informatics needs change, and we want to work with them to meet their needs.
DA: We’ve discussed the small molecule solution, but you also have a similar solution for antibody discovery. Is that fundamentally an analogous tool, or are there important differences beyond the biology that they’re investigating?
MN: It is fundamentally analogous — both products draw from our experience implementing scientific informatics systems and all the best practices we derive from that experience, and they share a lot of the same core platform technologies. They obviously differ in the specific scientific workflows that they aim to support, which means that they drive different integrations with our broader Dotmatics scientific applications portfolio. So, both have integrations with GraphPad Prism — that’s relevant to both. The Biology Solution is looking also at Geneious Prime, SnapGene, and Geneious Biologics for integrations.
DA: Moving forward, what future solutions does Dotmatics have in the works, and what potential opportunities do you see?
MN: We see a lot of potential opportunities to expand both small molecule and antibody discovery solutions, and that will be a focus for us in the coming quarters. We also see opportunities to integrate with recently acquired scientific applications. We are still defining what our next steps will be and what will have the most impact for our customers, and that will guide where we focus next.
