AI has branding and storytelling issues. That’s not to discount its growing usage, discussion, and potential. But if you ask 10 people what it is, he’ll probably get 10 different answers. That’s not where you want to be as a marketer.
It’s not uncommon for new technology to get lost in the weeds and the storytelling to become overly technical or too simplistic to move the story forward. After all, the best and easiest stories to tie together are the ones that show rather than tell what something does. Ironically, AI lacks human connection. To tell that story, you need a simple example that shows the direct impact on humans. The good news is that they aren’t difficult to find.
Cure for cancer is at the top of most people’s wish list for the promise of AI. That’s why millions of scientists are partnering with R&D software companies like Dotmatics to become AI-enabled. Because, due to practical effects, the period of scientific drug discovery has already been shortened from 10 years to less than 2 years.
Don’t worry if you haven’t heard of Dotmatics yet, its software has been used by companies from Bristol-Myers Squibb and Merck, to emerging biotech companies still operating in the incubator space, MIT and Oxford. Dotmatics has a team of 800 researchers, chemists, biologists and business leaders who accelerate scientific discoveries to create a healthier, cleaner and safer world. working behind the scenes for.
Two of their leaders talk about what it’s like to be on the ground during a transformative era in the life sciences. Melanie Nelson is a biochemist and currently she is Director of Product Management, Solutions and Integration at Dotmatics. She has her Ph.D. She is a research associate from the Scripps Research Institute, where she is currently working on building technology solutions that help streamline complex scientific workflows.
“Dr. Nelson and I met at a dot-com era biotech company in the early 2000s, when the first human genome data were released,” says Dr. Nelson, a former biotech executive and former administrator at California State University Biotechnology. said Director Dr. Susan Baxter. Program: “Melanie’s innate excitement for science, outgoing personality, and fortitude in the face of biological complexity enable her to bridge the gap between business, technology, and scientific groups to drive product development. It has a unique effect.”
Rebecca Sánchez Sarmiento joined Dotmatics as Chief Financial Officer two years ago, drawn to a mission-driven company focused on improving lives by driving scientific innovation.
“Rebecca’s approachable style and willingness to work in a ‘roll up your sleeves’ environment are a perfect fit for a dynamic, growing company like Dotmatics,” said Chedraui USA, an $8 billion multi-format grocery retailer. said Rick Pegley, executive vice president. With 375 stores in the Western United States, “and once the sleeves are rolled up, everyone who works with her learns how to process large amounts of data and create real insights for the benefit of businesses and their customers.” You learn quickly.”
Goldie Chan: Let’s start with your career. Combining scientific, technical, and business expertise within your team should create an interesting cultural mix, especially as a female leader. What advice would you both give to women pursuing STEM fields?
Melanie Nelson: Always try to approach disagreements within your team with the same curiosity that probably drew you to STEM in the first place. Ask why different people think the way they do, and keep asking follow-up questions until you really understand their perspective. Be willing to adjust your opinion based on what you learn, but also be willing to speak up and explain your own thinking.
Rebecca Sanchez Sarmiento: Don’t limit your potential. What makes a career in STEM so unique is the diversity of opportunities across technical, operational, and business-oriented roles. Feel free to explore different avenues and shape your career path over time.
Chan: Is there anyone who has influenced your career and inspired you along the way?
Nelson: Mr. Wood, my 9th grade chemistry teacher, was the first person to make me seriously consider the possibilities of science. She convinced me to participate in her project at the local science fair and didn’t let me write it off as a fluke when I came in second place for her. Based on her faith in me, I took a more advanced chemistry class the following year and loved it so much that I decided to major in chemistry in college.
Chan: AI is perhaps the biggest buzzword today, sometimes with excitement and sometimes with fear. Why is there so much optimism about AI in the life sciences industry?
Sarmiento: First, let’s get the big issue out of the way. The current drug discovery process is expensive, fragmented, time-consuming, and requires analyzing disparate mountains of data, often residing in disparate systems. On average, it costs about $2.5 billion and takes 10 years to bring a single drug to market. And for every drug that gets approved, thousands of compounds end up failing. This is a very complex process and ripe for innovation. But now imagine if we could fast-track the development of vaccines against newly emerging pathogens or discover new treatments to help chronic diseases like Alzheimer’s and Parkinson’s. What if we could do all of this not only faster, but with far fewer side effects, higher success rates, and ultimately lower costs for consumers?
Nelson: Yes, I think there’s a lot of optimism today. Because we see the potential for AI to support our activities. more and betEnhance your science and ultimately reach your research goals faster.
The drug discovery industry has been using computational methods to accelerate research for decades. So we’re all familiar with the idea that, if used wisely, these techniques can help build insights and advance science. We’re looking at what modern large-scale language models (LLMs) can do and what projects like AlphaFold have been able to achieve, helping us identify better drug targets, use the chemical space more efficiently, and more. discovery, discover promising drug leads faster, and target drugs more effectively to reduce toxicity.
Chan: What are the possible obstacles or challenges to achieving this?
Nelson: First and foremost, the challenge is to prepare the data to run AI. We know that scientists are working with larger and more complex datasets than ever before. Worldwide, the amount of data doubles every two years, and each new drug creates terabytes or petabytes of data at each stage of development. Therefore, scientists need help extracting meaningful insights from all their data. Moreover, not all of that data is available. They are often locked into disparate systems and are not compatible, unstructured, or “clean” within an organization.
Besides data preparation, quality and accuracy are also important factors, which are currently still difficult to assess using many AI techniques. While AI is expected to be increasingly used in the early stages of drug discovery, lab studies and clinical trials will always be needed to test the ideas and hypotheses provided by AI. You should also think carefully about how to construct your training set to avoid inadvertently biasing your results. And I cannot overstate the myriad ethical, social, and privacy considerations that we are all just beginning to explore as a society. The bottom line is that we must find a balance between harnessing the power of artificial intelligence while protecting people and the planet from future harm.
Chan: So where does your company Dotmatic fit into that continuum?
Sarmiento: We are building the world’s most powerful scientific research and development platform. This enables scientists and researchers to work from a single source that connects all their favorite applications to collaborate, provide automation, and perform analysis in the lab. Companies cannot realize AI until they build the necessary infrastructure to make predictions. That’s why scientists partner with Dotmatics to break down silos and apply data science to scientific data.
Chan: There are millions of scientists who trust your brand, so what generally sets you apart from your competitors?
Nelson: Clearly, we are deeply committed to helping scientists get the most out of their data, and that has been the case since our inception. But we believe that what sets us apart is the deep and diverse scientific experience of our team, which naturally extends to the range of products we develop and offer. Science is complex and constantly changing. So if we want to develop products that scientists want to use every day, empowering them and making their lives easier, we need ‘in the lab’ experience at the heart of our team. So we’re building solutions by scientists, for scientists.
Chan: What’s next for the company? Looking to the next few years, what excites you most about your job?
Sarmiento: It feels like we are on the brink of a revolution in how life-saving medicines are brought to market, which is very exciting. In addition to the R&D platform mentioned earlier, Dotmatics has already developed cutting-edge AI approaches within its own tools in specialized areas such as flow cytometry, and is working directly with customers to add more such AI analyses. doing. Delivering functionality to new areas and supporting needs. We are also constantly looking for new products to fit into our portfolio to expand the depth and breadth of our capabilities.
There is a lot of work ahead. The most pressing goal is to ensure that scientists using AI have an underlying data layer they can trust, ensuring that the data used to train AI is clean, organized, and unbiased. . Because we know that the promise of AI is only as good as the information it provides. Learn from. I think that’s an important lesson for all of us right now. We must approach this moment with humility and caution, and try to remain open-minded. If we work together to highlight concerns and take the necessary safeguards, AI has the power to transform scientific discovery in ways we can’t even imagine today.
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