Do you need a computational biologist for sequence analysis?

Q: As a computational biologist, what roles have you played in drug discovery?

I've done everything from supporting drug discovery labs to translational research groups.

Q: Why is sequence analysis important in drug discovery?

In discovery, you can use sequence analysis for target discovery screening. You can also start looking for patient selection biomarkers. Is there a particular genetic profile, either at the expression level or the DNA sequence level, with various mutations? Are there certain mutations that are present in patients that make them more or less susceptible to your drug being effective? That is an extremely exciting and active area in the pharmaceutical industry. It can really make the difference between success and failure to be able to know before treatment, yes, this drug should work, or no, it probably won't work. Additionally, you're not wasting patients' precious time. Especially in a field like oncology, where patients don't have much time, you're not wasting their time with trial and error and multiple lines of therapy with drugs that just won't work. And so all of that involves sequence analysis. It touches on every aspect of the drug development process.

Q: Where do you think the biggest challenges are within sequence analysis?

I would say the challenges are actually dropping pretty darn fast because the technology is advancing every year. Storage is still an issue. Even in my most recent role, we had to move sequence data, and the fastest way to do that was to load it onto a hard drive and drop it in a FedEx box. Instead of moving hundreds of gigabytes or terabytes of data over the internet, it's faster to ship it in a box. Local storage is not a problem, it's the transmission of a large amount of data from one place to another. These days, once you have the data where it needs to be, you can find enough computing power to run your sequencing project, but getting the data to the machines is still the bottleneck. Patient-derived samples are a challenge, too. They're painful, biopsies are invasive, and sick people don't want to have to give multiple samples. Once they are taken, they're generally formalin-fixed and paraffin-embedded, so any nucleic acid material is going to be degraded to some degree. There are ways to try to extract and use that sort of prepared sample tissue for sequencing, but the quality of the sequence is always going to take a hit. If you're a small company, the technology is also expensive–these machines cost a lot of money. Similarly, computational biologists are becoming much more common, but still, they're not everywhere, and everybody wants to work for the biggest and the best and make the most income. The labor pool is growing, but it's still limited. In a way, nobody wants to work on sequence analysis anymore. They all want to make the next great learning model. The focus is not so much on number crunching and data analysis, it's now on advanced AI and ML. Everybody wants to be working on the new, hot, shiny technology–and that's not sequence analysis. So that is going to be a challenge soon.

Q: Do you really need a computational biologist to do sequence analysis now?

If you're doing cookie-cutter, well-established methodologies that are well-developed, validated, and documented, then no, you don't.

Q: Can AI and ML be helpful with sequence analysis?

With a well-curated data set, AI and ML can definitely help.

Q: What are your thoughts on AlphaFold, which performs AI predictions of protein structures?

I think AlphaFold is an absolute game-changer.

Q: What do you think is the cutting edge of AI and ML in drug discovery?

It's spatial, which is kind of the next generation of single cell. Multi omics.

Q: Do you think we're going to be creating models of biological systems?

If you'd asked me that when I was in grad school, I would have said humanity does not have the mathematics that can describe a biological system. But now, that is probably the direction it will have to go in. Will it require a quantum computer? Maybe? It might be after my lifetime, but I will say now with some confidence that at some point, humanity will be able to have accurate, reliable computational simulations of living systems. And that statement kind of scares me. I know there's a lot of work being done in the digital twin space. Limited first-step scenarios, but digital twins are online and being used in clinical trials now. That's kind of the beginning of it.

Q: What do you think is needed to drive these new frontiers forward? New algorithms, new frameworks, or something else?

It's all of it—we need new ways of thinking about the problem.

Q: If you had a magic wand to change anything in the drug discovery process, what would you change?

I would make all the data well-annotated and available to everyone.

As computational biology within drug discovery continues to push forward, new challenges and opportunities arise constantly. Sequence analysis has long been a key aspect of bioinformatics. In this article, we speak with Jefferson Parker, Ph.D., founder of NullSet Informatics Solutions and an expert in data analytics for life sciences, about the new frontiers in sequence analysis for drug discovery.

CAS: As a computational biologist, what roles have you played in drug discovery?

CAS: Why is sequence analysis important in drug discovery?

CAS: Where do you think the biggest challenges are within sequence analysis?

CAS: Do you really need a computational biologist to do sequence analysis now?

CAS: Can AI and ML be helpful with sequence analysis?

CAS: What are your thoughts on AlphaFold, which performs AI predictions of protein structures?

CAS: What do you think is the cutting edge of AI and ML in drug discovery?

CAS: Do you think we're going to be creating models of biological systems?

CAS: What do you think is needed to drive these new frontiers forward? New algorithms, new frameworks, or something else?

CAS: If you had a magic wand to change anything in the drug discovery process, what would you change?

Jefferson Parker, Ph.D., began his research career at MIT, exploring xenobiotic metabolism in the gram-positive soil bacterium Rhodococcus aetherovorans. He got into computing when faced with an overload of data trying to annotate the genome to develop DNA microarrays, and he’s been working at the intersection of biology, computing, and mathematics since. His career has taken him through small pharma, large pharma, and consulting organizations, including Novartis and Thomson Reuters. Along the way Jefferson acquired his Graduate Certificate in Applied Statistics from Pennsylvania State University and a master’s degree in computer science from Boston University. Jefferson has also forged a new path with his own bioinformatics consulting company, NullSet Informatics Solutions, providing data and analytics, data modeling, and technology project management services.