How will AI transform precision medicine? – Ava Amini
Summary
TLDRThe speaker from Microsoft Research shares a vision for AI-driven precision health, emphasizing the uniqueness of human biology and the need for personalized medicine. They highlight the limitations of current cancer treatments and propose leveraging AI to understand the complexity of biological data, collaborating with the Broad Institute for precision oncology. The presentation introduces 'evoi', a generative AI for designing novel protein therapies, showcasing its ability to create proteins with new functions, aiming to revolutionize treatment by personalizing discovery and experimental design.
Takeaways
- 🤖 The speaker emphasizes the potential of AI in revolutionizing biological discovery and precision health by leveraging the unique genetic makeup of individuals.
- 🧬 Human health is described as both robust and delicate, with a call for personalized medicine that takes into account the nuances of each person's biology.
- 🔍 Current medical practices are criticized for not fully utilizing the rich and complex biological data available, often treating patients based on population averages rather than individual differences.
- 💡 The speaker introduces a new vision for precision medicine using AI, aimed at understanding disease mechanisms and designing new treatments tailored to individual patients.
- 🧬 The script highlights the opportunity to 'unlock the language of biology' by using AI to interpret the vast amount of data stored within each person's biological makeup.
- 🧬 The speaker shares a concrete example of how AI can be applied to cancer treatment, noting the current limitations in targeted therapies and the need for more personalized approaches.
- 📈 The potential of AI is underscored by the ability to analyze nearly 50 million data points from a single tumor biopsy, offering a rich dataset for developing new AI models.
- 🤝 Collaboration with the Broad Institute of MIT and Harvard is mentioned to develop AI systems for precision oncology, integrating molecular measurements into AI training for personalized treatment recommendations.
- 🛠️ The speaker introduces 'evoi', a generative AI model developed by Microsoft Research, designed to create new protein sequences with potential therapeutic applications.
- 🔬 The script discusses the importance of reasoning across different scales and types of data to fully understand and utilize the language of biology for developing new treatments.
- 🔁 The concept of a 'data and learning flywheel' is presented as a method to continuously improve AI models by integrating new data from lab experiments, aiming to close the loop on precision medicine.
Q & A
What is the main vision being discussed in the script?
-The main vision discussed is building AI systems to enable and accelerate new biological discoveries towards a vision of Precision Health, aiming to personalize medicine and better understand the mechanisms of disease.
Why is it important to consider individual differences in medicine?
-Individual differences are important because each person has a unique genetic makeup and history of experiences. Current medicine often does not account for these nuances, leading to less effective treatments that are not personalized.
What is the current state of targeted therapies for cancer patients?
-Fewer than 40% of cancer patients have access to targeted therapies that are specific to changes in their cells. Of those, less than 5% respond effectively to the current treatments, indicating a need for improvement.
How does the speaker define the problem with the current approach to treating cancer?
-The speaker defines the problem as treating cancer in an ad hoc, reductionist way where the individual's richness and complexity are lost, often resulting in treatments that work on average but not personalized.
What is the scale of biological data that can be generated from a single tumor biopsy?
-From a single tumor biopsy, nearly 50 million individual data points can be generated, considering various levels of biological resolution such as DNA mutations, transcriptome, proteins, and cell interactions.
What is the opportunity presented by this scale of biological data for AI researchers?
-The opportunity is to develop new AI systems that can process and analyze this vast amount of data to unlock new biological insights, design new treatments, and improve personalized medicine.
What is the role of AI in the vision of precision oncology discussed in the script?
-AI is central to the vision of precision oncology, where it is used to analyze molecular measurements from patients, train AI systems to produce personalized treatment recommendations, and design new molecular therapies.
What is the generative AI model 'evoi' mentioned in the script, and what does it aim to do?
-Evoi is a new generative AI model developed to design new molecular therapies, such as proteins, by learning from large-scale evolutionary data sets. It aims to produce proteins with new functions that have never been seen before in nature.
How does the AI model 'evoi' utilize the functional part of a protein to design new proteins?
-Evoi uses the functional part of a protein as a prompt, learning from its training data to design a new protein sequence around that part, creating a brand new protein that can be linked to a specific biological function.
What is the ultimate goal of developing these AI systems in the context of precision medicine?
-The ultimate goal is to close the loop on precision medicine by generating a data and learning flywheel that can personalize discovery, experimental design, and ultimately lead to more effective, tailored treatments for patients.
Outlines
🧬 Embracing AI for Personalized Medicine
The speaker expresses gratitude for the opportunity to discuss the integration of AI in advancing biological discoveries towards precision health. They emphasize the uniqueness of human health, which is both robust and delicate, and the current inadequacy of medicine to account for individual differences. The speaker argues for a new approach to unlock biological discoveries using AI, particularly in understanding and treating diseases like cancer. They highlight the need for AI systems trained not just on human language but also on the 'language of biology,' which is rich in data due to evolutionary processes. The speaker introduces Microsoft Research's vision to develop AI for personalized medicine, focusing on understanding disease mechanisms and designing new treatments, with reliability and safety as priorities. The example of cancer is used to illustrate the current challenges in targeted therapies and the potential for improvement through AI-driven personalized approaches.
🛠️ Innovating with AI in Precision Oncology and Molecular Therapy Design
The speaker shares their excitement about Microsoft Research's collaboration with the Broad Institute to create a new vision for precision oncology, utilizing AI to tailor treatments to individual patients. They describe the process of taking molecular measurements from patients to train AI systems that can recommend personalized experiments and treatments. The speaker introduces 'evoi,' a new generative AI model capable of designing novel proteins with unprecedented sequences, expanding the range of functionalities for chemical, biological, and therapeutic applications. An example is provided where the AI model is trained on a specific protein function and then designs a new protein sequence from scratch, which can be linked to a measurable biological function. The speaker concludes by emphasizing the importance of reasoning across different scales and data types to create a comprehensive understanding of biology, which is essential for realizing the vision of precision medicine and more effective treatments.
Mindmap
Keywords
💡AI Systems
💡Precision Health
💡Genetic Makeup
💡Biological Discovery
💡Personalized Medicine
💡Cancer
💡Targeted Therapies
💡Biological Data
💡Evolution
💡Generative AI
💡Protein Function
Highlights
The presentation discusses the vision of building AI systems to accelerate biological discovery towards precision health.
The uniqueness of human health and life, being both robust and delicate, motivates the work on personalized medicine.
Current medicine largely fails to account for the nuances and complexities of individual health.
AI can unlock new biological discoveries by learning the language of biology, which is rich in data.
Microsoft Research aims to develop AI for personalized medicine to better understand disease mechanisms and design treatments.
Cancer as a use case highlights the need for precision in treatment based on individual biological changes.
Less than 5% of cancer patients respond effectively to current treatments, indicating a significant problem in the approach.
The reductionist approach to treating cancer overlooks the individual's biological complexity.
A single tumor biopsy can generate nearly 50 million data points, presenting an opportunity for AI.
Microsoft Research collaborates with the Broad Institute to create AI for precision oncology.
AI systems are being developed to produce personalized recommendations for patient-specific experiments.
A new generative AI model, Evo, is introduced to design new molecular therapies like proteins.
Evo learns from large-scale datasets to produce proteins with expanded functional capabilities.
An example demonstrates how Evo can design a new protein sequence based on a functional part.
The designed protein by AI shows a structure linked to measurable biological function.
The need for reasoning across scales and integrating various data types to understand biology fully.
The vision of closing the loop in precision medicine by generating a data and learning flywheel.
The goal is to realize more effective, personalized treatments through AI and data integration.
Transcripts
thank you so much Peter it's an absolute
honor to be here with you all to share
with you our vision of how we can build
AI systems that can enable and
accelerate new biological Discovery
towards a vision of Precision
Health our work is fundamentally
motivated by the fact that human health
and human life is a Marvel on one hand
incredibly robust and on the other hand
incredibly delicate and to me the most
amazing thing about all this is that
each and every one of us is unique we
all have different bodies different
genetic makeups a different history of
experiences that makes you you and me me
and yet somehow it all seems to work in
concert and yet shockingly in medicine
today the fact is that these Nuance
differences these richnesses and
complexities are largely not taken into
account to preserve our health in a way
that's truly personalized
we don't just need a better way to
detect and treat disease or discover new
drugs I'd argue that we need a whole new
way to think about how we unlock new
biological discoveries and learn those
patterns that differentiate us all and
deploy them
forward through the use of powerful
tools like AI now it's no secret that
today we're experiencing a revolution
with a new generation of AI systems
train trained on Words and text the
language of us as
humans but yet there is tremendous
opportunity to unlock and learn the
language of
biology because of the fact that biology
through the hand of evolution naturally
stores an incredible scale richness and
complexity of data within each and every
one of
us our vision at Microsoft research is
to leverage this opportunity to now
develop new AI towards the vision of
personalizing medicine to help us
discover the mechanisms of disease
better to design new treatments and
ultimately deploy these systems into the
real world in a way that's reliable and
safe let me start by showing you why
this matters and a concrete example of
why we should
care let's consider cancer as a use case
in the United States nearly 40% of the
population would will develop cancer in
their lifetime and while we've known for
a while that cancer is driven
fundamentally By changes in the biology
of our cells we yet don't understand how
to condition the therapies based on
those changes to deliver the right
treatment to the right patient at the
right time indeed for fewer than 40% of
cancer patients today there exists what
we call targeted therapies that are
therapies specific to changes in their
cells but what's even more shocking is
of those 40% less than 5% of patients
even respond effectively to today's
treatments so clearly there's a problem
and clearly we can do better but why are
we
failing the answer to that is that
because by and large cancer is treated
in this ad hoc reductionist way where
that richness and complexity of the
individual is lost because they're
viewed as rather an instance in a
population given a treatment that works
on average over large scale population
wise
studies and so I've been speaking a lot
about this richness this complexity this
scale of biological data what exactly do
I mean by this and how can we make this
concrete I'd like you to consider now
suppose we have one cancer patient and
we take a biopsy from one tumor from
that patient
at just this level thinking about the
scales of biological resolution from the
level of DNA mutations to the
transcriptome to proteins to the
interactions of cells in their
neighborhoods and how they communicate
with each other we can generate nearly
50 million individual data points from
just the single tumor biopsy using our
measurement and experimental techniques
that we have in the lab
today now let's pause for a second and
consider this number right when I look
at this as an AI researcher and as a
computer scientist I see tremendous
opportunity and if I look at this number
as a
biologist I see Power that comes at the
hand of this richness and
complexity what's beautiful is we can
put those together by using this ability
to directly measure biology at its
natural scale the Nano scale as it's
occurred in in real time this gives us
opportunity to unlock the development of
new Ai and today I'm very excited to
share with you two concrete ways in
which we at Microsoft research have
brought this to
life first in collaboration and in close
partnership with the broad Institute of
MIT and Harvard we're collaborating to
create a new vision for precision
oncology closing the loop on this Vision
that we been talking about by putting AI
front and Cent and Center such that we
can directly tail take these molecular
me measurements from the patient level
train and build AI systems that can now
produce recommendations about what
experiments are best to test for that
patient in a personalized way we can use
this as a flywheel to generate the new
data that we produce in the lab to then
iterate and improve our AI model and and
ultimately recommend or design new
treatments that are specific and
tailored to the needs and biology of
that
person we can dive deeper on one of the
components of this Pipeline and ask what
does it take to actually design new
molecular therapies new treatments that
have functional
ability and to this end we've built and
are building powerful new generative AI
systems to design new molecular
Therapies like
proteins by learning the language of
protein function learning from large
scale evolutionary scale data sets of 50
million unique protein
sequences we've trained a new generative
AI model that we call evoi that can now
produce brand new instances of proteins
that have never been seen before in
nature with the goal of expanding the
functional capabilities whether they be
chemical biological or therapeutic
available to
us let's bring this capability this
generative capability to life through an
example what I'm showing you here is an
example of one protein where in green
I've isolated the functional part of
that protein that is responsible for its
function binding to calcium in our cells
we can take that part and isolate it and
use this as a kind of prompt to our
model Evo diff which can then take that
prompt and learn from the information
that it's been trained on and see to now
design a brand new protein sequence
around that part step by step from the
bottom up creating a brand new protein
sequence that's never existed
before and what's amazing is that this
new protein designed by our AI model
shows a structure that we can then
explicitly link to biological function
measured in the lab in the real world
now I'd be remiss to say that it stops
there at our ability to generate new
molecules and proteins that's just the
start to actually learn and understand
this language of biology we need to be
able to reason across scales across
different types of data and put all
these components together from the
molecule level all the way up to that of
the
patient we see this as a foundation by
which we can now close the Loop and
generate a data and learning flywheel to
close this vision of precision medicine
for once and for all to be able to
personalize Discovery experimental
design and ultimately hopefully realize
more effective treatments thank you
[Applause]
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