Behind the code: Meet one of the AI scientists revolutionizing AI antibody design
Jun 12, 2023
Amir Shanehsazzadeh shares his insider perspective on AI drug discovery and Absci’s de novo breakthrough.
At the Absci Town Hall celebrating the de novo breakthrough, Amir Shanehsazzadeh modestly accepted the mic and joked about not being able to spell ‘antibody’ before coming aboard.
“Of course, I knew what antibodies were,” Amir says, “and that you could use them as a class of therapeutics involved with the immune system. But I really didn’t know any of the details about CDRs, constant regions, variable regions, and so on.”
Amir is an AI scientist at Absci. He uses his computational skills to write the algorithms to design antibody drugs. Amir was raised in a family of doctors — his granddad was a doctor, and his mom and aunt are still doctors. Yet Amir was always drawn more to math than biology.
“My parents kind of wanted me to be a doctor, but I don’t really think that was for me. They gave me an inclination towards life sciences and biology though, which I thought was pretty interesting.”
A career begins
Amir got his first taste of life science research in high school at the Fox Chase Cancer Center in Philadelphia, where he worked as a computational biology intern.
“It turns out, I’m pretty sure I was looking at antibodies,” he says. “At the time, though, I just really didn’t understand the biology. I thought of it from a mathematical perspective.”
While studying math in college, Amir pursued several more internships in the biotech space, including his introduction to using large language models (LLMs) to design proteins. In retrospect, Amir sees these as the early days of the current AI wave.
“Deep learning for proteins is very young — as a field, maybe six or seven years. And I’d say I’ve been doing it for about five of those years. Computational protein design in biology is decades old. But the deep learning stuff is pretty new.” He says deep learning applications in fields like NLP and computer vision really took off in the early 2010s, and then it took a couple of years for people in the life sciences to get familiar with the software and methods. “And now, you know, it’s all the rage in biology.”
In the fall of 2018, Amir applied to intern at a lot of different places, just to get his foot in the door and try things out. He stumbled upon one company with a “kind of basic HTML page that just looked weird.” He wondered if it was even a real company.
The company was very real, in fact. It was D. E. Shaw Research, a company that uses sophisticated computer models to study molecular dynamics, the movements of atoms and molecules. It’s where Amir did his first deep learning work on proteins. It’s also where he met Joshua Meier, Absci’s future Chief AI Officer, who was at Facebook AI Research’s protein team at the time.
Around this time, Amir also applied to an options trading company – he didn’t get the job. Though it might have been a lucrative career, he ultimately found himself inclined toward opportunities where he could see the tangible benefits of his work on people’s lives. “I wanted to make something, whether it was a SaaS product, a consumer product, or a drug.”
Working and studying from home
Amir spent another summer interning at Google Brain, although his plans to split his time between their Mountain View and Cambridge offices were doused by the pandemic. His Google experience was spent at his parents’ home in front of a computer — and then his university went remote. Being stuck at home for his junior year of college gave Amir a lot of time to study, work, and think. He began working full-time at Dyno Therapeutics, a startup using machine learning to design AAV capsids for gene therapy, while also taking a full load of classes.
“I was pretty set on this field, but I began debating whether to work for an industry research lab like Google or Facebook, or go to more of a biotech startup company.” He also began to think about what kind of company could have the biggest impact.
“Whether it’s gene therapy or antibodies, I think it’s hard to argue which of these is better, impact-wise. They’re both trying to cure or treat diseases. Maybe antibodies are 5% more likely to make an impact, or gene therapies might help 1.3 times the number of people. At the end of the day,” Amir says, “if I can help 1000 or 1200 patients with their disease, I’m happy either way.”
As an AI scientist, there was one other factor that impacted Amir’s decision to ultimately join Absci: having faster cycle times in the lab to know how his models are working.
“Being able to get data back faster was really compelling for me. Data is the lifeblood of AI, and in vivo studies typically take a while. For example, it might take nine months to get data back from a non-human primate (NHP) study. I joke that you could have a child in that amount of time. Here, you can get back data from in vitro studies sometimes as fast as a month, usually faster than two months. It’s great.”
Life at Absci
Among Amir’s proudest accomplishments is his role in developing Absci’s de novo antibody design technology.
“Seeing that effort really grow from infancy is what I’m most proud of,” he says. “There’s still a lot of work we have to do, but I think the result we have going from one heavy chain CDR to designing all three heavy chain CDRs successfully is really encouraging. And hopefully, we can extend it to all six CDRs of the antibody.”
Amir says it’s one thing to put it on paper. Now, the next step is getting results to patients.
“How do we start impacting patients’ lives? How do we create a drug with this technology? Because ultimately, that’s the thing that will differentiate us. We have a mandate to create drugs that will improve patients’ lives. That’s what motivates us. And that is where we are all working to make progress.”