We all have about 20,000 genes in our genomes. While this diversity is what makes the human experience so rich, our genetic differences can make things more difficult when it comes to medicine and the treatment of diseases.
Today, most treatments are a one-size-fits-all approach. Only a small fraction of cancer patients, for example, receive targeted therapies. But if AI could learn to read and write the language of biology, it could help customize treatments for the unique makeup of each patient.
Ava Amini, a principal researcher at Microsoft Research, is working to make that happen. She recently spoke about the potential of AI for biology at a crowded brewery in Cambridge, Massachusetts, as part of “Lectures on Tap,” an event series that combines expert lectures with interactive fun in casual pub settings around the U.S.
Here are five of the concepts she covered, from how precision medicine works to the grand vision of developing AI that can predict how cells behave.
How AI can help make sense of biology
Biology is incredibly complex — each person’s genetic makeup and cellular behavior is unique. Today, medicine often treats patients based on averages, not individual differences. Amini says AI offers a way to decode patterns in massive biological datasets that humans can’t process alone.
“Computation gives us this incredibly powerful toolkit to understand what I think is the most complex and intricate system that we have, which is the system and the language of biology,” she says. “We have this opportunity to build computational systems, AI models, that can harness the scale of data that we’re generating, to learn this biological language and ultimately be able to use that to make new discoveries, design new drugs and hopefully get closer to that vision of empowering people to live a healthier future.”
Amini says a single cancer biopsy, for example, can generate nearly 50 million individual data points. AI could help sift through this massive data, find patterns and enable personalized, precise treatment rather than generalized care.
How precision medicine can help people
Precision medicine aims to tailor treatments to the unique genetic, molecular and cellular makeup of each patient. But most treatments are generic, and only a small fraction of cancer patients receive targeted therapies. Even fewer experience lasting success, Amini says.
“The truth is that based on today’s targeted therapies, less than 5% of this population is even going to respond effectively,” Amini says of cancer treatment. “That’s because there are things like resistance or the cancer evolves, it spreads and grows, and these patients will not actually see durable, lasting, curative outcomes.”
Precision medicine seeks to overcome these limitations by leveraging the diversity and heterogeneity of diseases like cancer, moving beyond population averages to individualized care.
Using the language of biology to design new proteins
Back in 1965, American biophysicist Margaret Dayhoff gave biology an alphabet — a one-letter code for the 20 natural amino acids, the building blocks of proteins. Her creation of this code for amino acids enabled the representation of proteins as a language.
Microsoft is building on this foundation with EvoDiff and The Dayhoff Atlas, generative AI models to design new proteins. Amini says the concept is like Copilot for biology: Input a prompt and output a novel protein guided by that prompt.
These models can be prompted in the biological language to design proteins with specific functions.
AI-designed proteins show progress and promise
AI-designed proteins could help target cancer cells or bind to receptors for drug delivery, according to Amini.
She says Microsoft’s EvoDiff and Dayhoff models have generated proteins tested in the lab with successful functional outcomes. By learning from a greater scale and diversity of data, the Dayhoff models improved the success rate of producing new proteins from 16% with previous methods to 50%. These advances show that generative AI for biology isn’t just theory; it’s happening now.
“We’ve actually gone and measured and tested in the lab in the real world to show that these proteins have the functions that we meant and sought to have,” Amini says.
However, the quality and diversity of data remain critical for model performance, and there are still significant limitations — especially in modeling entire cells.
Working toward modeling human cells
An AI model designed to simulate the complexity of a human cell by learning patterns in biological data could predict how cells respond to drugs, unlocking precision medicine. Many consider it to be a “holy grail” in science, Amini says, and have pursued the idea of building AI models to predict how cells behave. Amini says their experiments at Microsoft have shown that existing AI models of cells often predict only average values, rather than real biological differences. Increasing data volume does not improve performance: Models saturate quickly and do not scale as expected. Recent critical studies, including those by Amini and team, have exposed these limitations.
Amini still has hope. While the promise of AI in biology is immense, she says, realizing personalized, precise medicine will require continued integration and collaboration across disciplines. She co-leads Project Ex Vivo, a research partnership between Microsoft and the Broad Institute with support from the Dana-Farber Cancer Institute, which is building a new framework for precision oncology, integrating experimentation and computation from the ground up toward the ultimate goal of improving patient outcomes.
“As a technologist, we use these findings as fuel, and we want to take as much as we can to actually go further,” she says. “And all of this information, all of these evaluations, help us do better and get closer to that promise.”
Lead image by Andriy Onufriyenko / Moment / Getty Images.
