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.

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.

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.

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.

Can AI and ML be helpful with sequence analysis?

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

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

I think AlphaFold is an absolute game-changer.

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.

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.

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.

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.

염기서열 분석을 위해 전산 생물학자가 필요하십니까?

신약 개발과 관련이 있는 전산 생물학이 꾸준하게 발전하면서, 새로운 과제와 기회가 끊임없이 대두되고 있습니다. 염기서열 분석은 오랫동안 생물정보학의 핵심 요소였습니다. 이 기사에서는 Informatics Solutions의 설립자이자 생명과학 부문의 데이터 분석 전문가인 Jefferson Parker 박사와 함께 신약 개발을 위한 염기서열 분석과 관련된 새로운 발전 가능성에 대해 알아봅니다.

CAS: 전산 생물학자로서 신약 개발 부문에서 어떤 역할을 해 오셨습니까?

CAS: 신약 개발에서 염기서열 분석이 중요한 이유는 무엇입니까?

CAS: 염기서열 분석의 가장 큰 과제는 무엇이라고 생각하십니까?

CAS: 오늘날 염기서열 분석을 수행하기 위해서는 반드시 전산 생물학자가 필요한가요?

CAS: AI와 ML이 염기서열 분석에 도움이 될까요?

CAS: 단백질 구조에 대한 AI 예측을 수행하는 AlphaFold에 대해 어떻게 생각하십니까?

CAS: 신약 개발과 관련해 AI와 ML은 어떠한 방향으로 발전하고 있다고 생각하십니까?

CAS: 향후 생체계 모델이 만들어질 것이라고 생각하십니까?

CAS: 이러한 새로운 영역을 개척하기 위해 무엇이 필요하다고 생각하십니까? 새로운 알고리즘이나 새로운 프레임워크, 아니면 다른 무언가가 필요할까요?

CAS: 만약 신약 개발 과정에서 무엇이든 바꿀 수 있는 마법의 지팡이가 있다면, 무엇을 바꾸고 싶으신가요?

Jefferson Parker 박사는 MIT에서 그람 양성 토양 세균인 Rhodococcus aetherovorans의 외인성 물질 대사를 탐구하며 연구 경력을 시작했습니다.그는 DNA 마이크로어레이를 개발하기 위해 유전체를 주석화하는 과정에서 방대한 데이터에 직면했고, 이를 계기로 컴퓨팅에 뛰어들었습니다. 그 이후로 생물학, 컴퓨팅, 수학의 교차 영역에서 연구를 이어오고 있습니다. 그의 경력은 Novartis와 Thomson Reuters를 포함해 소규모 제약사, 대규모 제약사, 컨설팅 기관을 두루 거쳤습니다. 그 과정에서 펜실베이니아 주립대학교에서 응용 통계학 대학원 전문 수료 과정을 이수했고, 보스턴대학교에서 컴퓨터 과학 석사 학위를 취득했습니다. 또한 Jefferson 데이터 및 분석, 데이터 모델링, 기술 프로젝트 관리 서비스를 제공하는 생물정보학 컨설팅 회사인 NullSet Informatics Solutions를 설립하며 새로운 길을 개척하고 있습니다.