Author: Lillian Eden | Department of Biology<\/p>\n
A few years ago, Gevorg Grigoryan PhD \u201907, then a professor at Dartmouth College, had been pondering an idea for data-driven protein design for therapeutic applications. Unsure how to move forward with launching that concept into a company, he dug up an old syllabus from an entrepreneurship course he took during his PhD at MIT and decided to email the instructor for the class.<\/p>\n
He labored over the email for hours. It went from a few sentences to three pages, then back to a few sentences. Grigoryan finally hit send in the wee hours of the morning.<\/p>\n
Just 15 minutes later, he received a response from Noubar Afeyan<\/a> PhD \u201987, the CEO and co-founder of venture capital company Flagship Pioneering<\/a> (and the commencement speaker<\/a> for the 2024 OneMIT Ceremony).<\/p>\n That ultimately led Grigoryan, Afeyan, and others to co-found Generate:Biomedicines<\/a>, where Grigoryan now serves as chief technology officer.<\/p>\n \u201cSuccess is defined by who is evaluating you,\u201d Grigoryan says. \u201cThere is no right path \u2014 the best path for you is the one that works for you.\u201d<\/p>\n Generalizing principles and improving lives<\/strong><\/p>\n Generate:Biomedicines is the culmination of decades of advancements in machine learning, biological engineering, and medicine. Until recently, de novo design of a protein was extremely labor intensive, requiring months or years of computational methods and experiments.<\/p>\n \u201cNow, we can just push a button and have a generative model spit out a new protein with close to perfect probability it will actually work. It will fold. It will have the structure you\u2019re intending,\u201d Grigoryan says. \u201cI think we\u2019ve unearthed these generalizable principles for how to approach understanding complex systems, and I think it\u2019s going to keep working.\u201d<\/p>\n Drug development was an obvious application for his work early on. Grigoryan says part of the reason he left academia \u2014 at least for now \u2014 are the resources available for this cutting-edge work.\u00a0<\/p>\n \u201cOur space has a rather exciting and noble reason for existing,\u201d he says. \u201cWe\u2019re looking to improve human lives.\u201d<\/p>\n Mixing disciplines<\/strong><\/p>\n Mixed-discipline STEM majors are increasingly common, but when Grigoryan was an undergraduate, little-to-no infrastructure existed for such an education.\u00a0<\/p>\n \u201cThere was this emerging intersection between physics, biology, and computational sciences,\u201d Grigoryan recalls. \u201cIt wasn\u2019t like there was this robust discipline at the intersection of those things \u2014 but I felt like there could be, and maybe I could be part of creating one.\u201d<\/p>\n He majored in biochemistry and computer science, much to the confusion of his advisors for each major. This was so unprecedented that there wasn\u2019t even guidance for which group he should walk with at graduation.<\/p>\n Heading to Cambridge<\/strong><\/p>\n Grigoryan admits his decision to attend MIT in the Department of Biology wasn\u2019t systematic.<\/p>\n \u201cI was like, \u2018MIT sounds great \u2014 strong faculty, good techie school, good city. I\u2019m sure I\u2019ll figure something out,\u2019\u201d he says. \u201cI can\u2019t emphasize enough how important and formative those years at MIT were to who I ultimately became as a scientist.\u201d<\/p>\n He worked with Amy Keating<\/a>, then a junior faculty member, now head of the Department of Biology, modeling protein-protein interactions. The work involved physics, math, chemistry, and biology. The computational and systems biology PhD program was still a few years away, but the developing field was being recognized as important.<\/p>\n Keating remains an advisor and confidant to this day. Grigoryan also commends her for her commitment to mentoring while balancing the demands of a faculty position \u2014 acquiring funding, running a research lab, and teaching.<\/p>\n \u201cIt\u2019s hard to make time to truly advise and help your students grow, but Amy is someone who took it very seriously and was very intentional about it,\u201d Grigoryan says. \u201cWe spent a lot of time discussing ideas and doing science. The kind of impact that one can have through mentorship is hard to overestimate.\u201d<\/p>\n Grigoryan next pursued a postdoc at the University of Pennsylvania with William \u201cBill\u201d DeGrado<\/a>, continuing to focus on protein design while gaining more experience in experimental approaches and exposure to thinking about proteins differently.<\/p>\n Just by examining them, DeGrado had an intuitive understanding of molecules \u2014 anticipating their functionality or what mutations would disrupt that functionality. His predictive skill surpassed the abilities of computer modeling at the time.<\/p>\n Grigoryan began to wonder: Could computational models use prior observations to be at least as predictive as someone who spent a lot of time considering and observing the structure and function of those molecules?<\/p>\n Grigoryan next went to Dartmouth for a faculty position in computer science with cross-appointments in biology and chemistry to explore that question.<\/p>\n Balancing industry and academia<\/strong><\/p>\n Much of science is about trial and error, but early on, Grigoryan showed that accurate predictions of proteins and how they would bind, bond, and behave didn\u2019t require starting from first principles. Models became more accurate by solving more structures and taking more binding measurements.<\/p>\n Grigoryan credits the leaders at Flagship Pioneering for their initial confidence in the possible applications for this concept \u2014 more bullish, at the time, than Grigoryan himself.<\/p>\n He spent four years splitting his time between Dartmouth and Cambridge and ultimately decided to leave academia altogether.<\/p>\n \u201cIt was inevitable because I was just so in love with what we had built at Generate,\u201d he says. \u201cIt was so exciting for me to see this idea come to fruition.\u201d<\/p>\n Pause or grow<\/strong><\/p>\n Grigoryan says the most important thing for a company is to scale at the right time, to balance \u201chitting the iron while it\u2019s hot\u201d while considering the readiness of the company, the technology, and the market.<\/p>\n But even successful growth creates its own challenges.<\/p>\n When there are fewer than two dozen people, aligning strategies across a company is straightforward: Everyone can be in the room. However, growth \u2014 say, expanding to 200 employees \u2014 requires more deliberate communication and balancing agility while maintaining the company\u2019s culture and identity.<\/p>\n \u201cGrowing is tough,\u201d he says. \u201cAnd it takes a lot of intentional effort, time, and energy to ensure a transparent culture that allows the team to thrive.\u201d<\/p>\n Grigoryan\u2019s time in academia was invaluable for learning that \u201ceverything is about people\u201d \u2014 but academia and industry require different mindsets.<\/p>\n \u201cBeing a PI [principal investigator] is about creating a lane for each of your trainees, where they\u2019re essentially somewhat independent scientists,\u201d he says. \u201cIn a company, by construction, you are bound by a set of common goals, and you have to value your work by the amount of synergy that it has with others, as opposed to what you can do only by yourself.\u201d\u00a0<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"