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On Tue, 3 Dec, 12:06 AM UTC
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[1]
Better drugs through AI? Insitro CEO on what machine learning can teach Big Pharma
WASHINGTON (AP) -- Artificial intelligence is changing the way companies do business -- helping programmers write code and fielding customer service calls with chatbots. But the pharmaceutical industry is still waiting to see whether AI can tackle its biggest challenge: finding faster, cheaper ways to develop new drugs. Despite billions poured into research, new medicines still typically take a decade or more to develop. Founded in 2018, Insitro is part of a growing field of AI companies promising to accelerate drug discovery by using machine learning to analyze huge datasets of chemical and biological markers. The South San Francisco-based company has signed deals with drugmakers like Eli Lilly and Bristol Myers Squibb to help develop medicines for metabolic diseases, neurological conditions and degenerative disorders. CEO and founder Daphne Koller spoke with the AP about what AI brings to the challenges of drug discovery. The conversation has been edited for length and clarity. A: I think the problem with drug discovery is that we are trying to intervene in a system that we only slightly understand. Many of the successes that we've seen in the last 15 to 20 years have been when we arrive at a sufficient understanding of the system so we can really design interventions to align with it. So one of the things that we try to do at Insitro is unravel the underlying complexity of heterogeneous diseases and identify new intervention modes that could help, maybe not the entire population, but perhaps just a subset of it. That way we can really identify the right therapeutic hypothesis to intervene in a particular patient population. And that, I think, is the real crux of the industry's lack of success. A: One of the things that has been happening in parallel to the AI revolution is a much quieter revolution in what I call quantitative biology, which is the ability to measure biological systems with unprecedented fidelity. You can measure systems like proteins and cells with increasingly better measurements and technology. But if you give that data to a person, their eyes will just glaze over because there's only so many cells someone can look at and only so many subtleties they can see in these images. People are just limited in their ability to perceive subtle differences. So you end up with a very reductionist view of a very complex, multifaceted system which is really important to unraveling the distinctions between patients and uncovering where an intervention really can make a difference. A: My PhD was in computer science. But I started to get into the field of machine learning in the service of biomedical problems back in 1998 or 1999. At that time, the problems that machine learning was able to tackle were, frankly, uninspiring. How inspired can you get about classifying spam versus non-spam in a dataset of email messages? I was looking for something that had more richness to it. And my first foray into this field was not because I was particularly interested in becoming a biologist, but because I was looking for more technically challenging questions. And then, as I started looking into it, I became interested in biology in its own right. A: This is probably one of the most important things we've achieved as an organization. You can take the most sophisticated, best meaning scientists from either side and put them in the same room together and they might as well be speaking Thai and Swahili to each other. When you're an engineer, you're looking for the strongest, most consistent patterns that are going to allow you to make predictions about a majority of cells or individuals. When you're a life scientist, oftentimes you're actually looking for the exceptions because those are the threads that can lead to new discoveries. So we've put in place a number of cultural elements and organizational elements to help people engage with each other openly, constructively and with respect.
[2]
Better drugs through AI? Insitro CEO on what machine learning can teach Big Pharma
WASHINGTON (AP) -- Artificial intelligence is changing the way companies do business -- helping programmers write code and fielding customer service calls with chatbots. But the pharmaceutical industry is still waiting to see whether AI can tackle its biggest challenge: finding faster, cheaper ways to develop new drugs. Despite billions poured into research, new medicines still typically take a decade or more to develop. Founded in 2018, Insitro is part of a growing field of AI companies promising to accelerate drug discovery by using machine learning to analyze huge datasets of chemical and biological markers. The South San Francisco-based company has signed deals with drugmakers like Eli Lilly and Bristol Myers Squibb to help develop medicines for metabolic diseases, neurological conditions and degenerative disorders. CEO and founder Daphne Koller spoke with the AP about what AI brings to the challenges of drug discovery. The conversation has been edited for length and clarity. Q: Why is drug development so difficult? A: I think the problem with drug discovery is that we are trying to intervene in a system that we only slightly understand. Many of the successes that we've seen in the last 15 to 20 years have been when we arrive at a sufficient understanding of the system so we can really design interventions to align with it. So one of the things that we try to do at Insitro is unravel the underlying complexity of heterogeneous diseases and identify new intervention modes that could help, maybe not the entire population, but perhaps just a subset of it. That way we can really identify the right therapeutic hypothesis to intervene in a particular patient population. And that, I think, is the real crux of the industry's lack of success. Q: Companies like Eli Lilly employ thousands of medical scientists and researchers. What can your technology do that those experts can't? A: One of the things that has been happening in parallel to the AI revolution is a much quieter revolution in what I call quantitative biology, which is the ability to measure biological systems with unprecedented fidelity. You can measure systems like proteins and cells with increasingly better measurements and technology. But if you give that data to a person, their eyes will just glaze over because there's only so many cells someone can look at and only so many subtleties they can see in these images. People are just limited in their ability to perceive subtle differences. So you end up with a very reductionist view of a very complex, multifaceted system which is really important to unraveling the distinctions between patients and uncovering where an intervention really can make a difference. Q: How did you become interested in this field? A: My PhD was in computer science. But I started to get into the field of machine learning in the service of biomedical problems back in 1998 or 1999. At that time, the problems that machine learning was able to tackle were, frankly, uninspiring. How inspired can you get about classifying spam versus non-spam in a dataset of email messages? I was looking for something that had more richness to it. And my first foray into this field was not because I was particularly interested in becoming a biologist, but because I was looking for more technically challenging questions. And then, as I started looking into it, I became interested in biology in its own right. Q: Insitro employs both computer scientists and medical researchers. Was there any culture clash in getting those two groups to work together? A: This is probably one of the most important things we've achieved as an organization. You can take the most sophisticated, best meaning scientists from either side and put them in the same room together and they might as well be speaking Thai and Swahili to each other. When you're an engineer, you're looking for the strongest, most consistent patterns that are going to allow you to make predictions about a majority of cells or individuals. When you're a life scientist, oftentimes you're actually looking for the exceptions because those are the threads that can lead to new discoveries. So we've put in place a number of cultural elements and organizational elements to help people engage with each other openly, constructively and with respect.
[3]
Better drugs through AI? Insitro CEO on what machine learning can teach Big Pharma
WASHINGTON -- Artificial intelligence is changing the way companies do business -- helping programmers write code and fielding customer service calls with chatbots. But the pharmaceutical industry is still waiting to see whether AI can tackle its biggest challenge: finding faster, cheaper ways to develop new drugs. Despite billions poured into research, new medicines still typically take a decade or more to develop. Founded in 2018, Insitro is part of a growing field of AI companies promising to accelerate drug discovery by using machine learning to analyze huge datasets of chemical and biological markers. The South San Francisco-based company has signed deals with drugmakers like Eli Lilly and Bristol Myers Squibb to help develop medicines for metabolic diseases, neurological conditions and degenerative disorders. CEO and founder Daphne Koller spoke with the AP about what AI brings to the challenges of drug discovery. The conversation has been edited for length and clarity. A: I think the problem with drug discovery is that we are trying to intervene in a system that we only slightly understand. Many of the successes that we've seen in the last 15 to 20 years have been when we arrive at a sufficient understanding of the system so we can really design interventions to align with it. So one of the things that we try to do at Insitro is unravel the underlying complexity of heterogeneous diseases and identify new intervention modes that could help, maybe not the entire population, but perhaps just a subset of it. That way we can really identify the right therapeutic hypothesis to intervene in a particular patient population. And that, I think, is the real crux of the industry's lack of success. A: One of the things that has been happening in parallel to the AI revolution is a much quieter revolution in what I call quantitative biology, which is the ability to measure biological systems with unprecedented fidelity. You can measure systems like proteins and cells with increasingly better measurements and technology. But if you give that data to a person, their eyes will just glaze over because there's only so many cells someone can look at and only so many subtleties they can see in these images. People are just limited in their ability to perceive subtle differences. So you end up with a very reductionist view of a very complex, multifaceted system which is really important to unraveling the distinctions between patients and uncovering where an intervention really can make a difference. A: My PhD was in computer science. But I started to get into the field of machine learning in the service of biomedical problems back in 1998 or 1999. At that time, the problems that machine learning was able to tackle were, frankly, uninspiring. How inspired can you get about classifying spam versus non-spam in a dataset of email messages? I was looking for something that had more richness to it. And my first foray into this field was not because I was particularly interested in becoming a biologist, but because I was looking for more technically challenging questions. And then, as I started looking into it, I became interested in biology in its own right. A: This is probably one of the most important things we've achieved as an organization. You can take the most sophisticated, best meaning scientists from either side and put them in the same room together and they might as well be speaking Thai and Swahili to each other. When you're an engineer, you're looking for the strongest, most consistent patterns that are going to allow you to make predictions about a majority of cells or individuals. When you're a life scientist, oftentimes you're actually looking for the exceptions because those are the threads that can lead to new discoveries. So we've put in place a number of cultural elements and organizational elements to help people engage with each other openly, constructively and with respect.
[4]
Better Drugs Through AI? Insitro CEO on What Machine Learning Can Teach Big Pharma
WASHINGTON (AP) -- Artificial intelligence is changing the way companies do business -- helping programmers write code and fielding customer service calls with chatbots. But the pharmaceutical industry is still waiting to see whether AI can tackle its biggest challenge: finding faster, cheaper ways to develop new drugs. Despite billions poured into research, new medicines still typically take a decade or more to develop. Founded in 2018, Insitro is part of a growing field of AI companies promising to accelerate drug discovery by using machine learning to analyze huge datasets of chemical and biological markers. The South San Francisco-based company has signed deals with drugmakers like Eli Lilly and Bristol Myers Squibb to help develop medicines for metabolic diseases, neurological conditions and degenerative disorders. CEO and founder Daphne Koller spoke with the AP about what AI brings to the challenges of drug discovery. The conversation has been edited for length and clarity. Q: Why is drug development so difficult? A: I think the problem with drug discovery is that we are trying to intervene in a system that we only slightly understand. Many of the successes that we've seen in the last 15 to 20 years have been when we arrive at a sufficient understanding of the system so we can really design interventions to align with it. So one of the things that we try to do at Insitro is unravel the underlying complexity of heterogeneous diseases and identify new intervention modes that could help, maybe not the entire population, but perhaps just a subset of it. That way we can really identify the right therapeutic hypothesis to intervene in a particular patient population. And that, I think, is the real crux of the industry's lack of success. Q: Companies like Eli Lilly employ thousands of medical scientists and researchers. What can your technology do that those experts can't? A: One of the things that has been happening in parallel to the AI revolution is a much quieter revolution in what I call quantitative biology, which is the ability to measure biological systems with unprecedented fidelity. You can measure systems like proteins and cells with increasingly better measurements and technology. But if you give that data to a person, their eyes will just glaze over because there's only so many cells someone can look at and only so many subtleties they can see in these images. People are just limited in their ability to perceive subtle differences. So you end up with a very reductionist view of a very complex, multifaceted system which is really important to unraveling the distinctions between patients and uncovering where an intervention really can make a difference. Q: How did you become interested in this field? A: My PhD was in computer science. But I started to get into the field of machine learning in the service of biomedical problems back in 1998 or 1999. At that time, the problems that machine learning was able to tackle were, frankly, uninspiring. How inspired can you get about classifying spam versus non-spam in a dataset of email messages? I was looking for something that had more richness to it. And my first foray into this field was not because I was particularly interested in becoming a biologist, but because I was looking for more technically challenging questions. And then, as I started looking into it, I became interested in biology in its own right. Q: Insitro employs both computer scientists and medical researchers. Was there any culture clash in getting those two groups to work together? A: This is probably one of the most important things we've achieved as an organization. You can take the most sophisticated, best meaning scientists from either side and put them in the same room together and they might as well be speaking Thai and Swahili to each other. When you're an engineer, you're looking for the strongest, most consistent patterns that are going to allow you to make predictions about a majority of cells or individuals. When you're a life scientist, oftentimes you're actually looking for the exceptions because those are the threads that can lead to new discoveries. So we've put in place a number of cultural elements and organizational elements to help people engage with each other openly, constructively and with respect. Copyright 2024 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.
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Daphne Koller, CEO of Insitro, explains how AI and machine learning could revolutionize drug discovery, potentially accelerating the development of new medicines and overcoming longstanding industry challenges.
Artificial intelligence is revolutionizing various industries, and now it's setting its sights on one of the most challenging sectors: pharmaceutical drug discovery. Insitro, a South San Francisco-based AI company founded in 2018, is at the forefront of this movement, aiming to accelerate drug development using machine learning to analyze vast datasets of chemical and biological markers [1][2][3][4].
Daphne Koller, CEO and founder of Insitro, explains that the fundamental difficulty in drug discovery lies in attempting to intervene in biological systems that are only partially understood. Despite billions invested in research, new medicines typically require a decade or more to develop [1][2][3][4].
Koller states, "Many of the successes that we've seen in the last 15 to 20 years have been when we arrive at a sufficient understanding of the system so we can really design interventions to align with it" [1][2][3][4].
Insitro's strategy involves unraveling the complexity of heterogeneous diseases and identifying new intervention modes that could benefit specific patient subsets. This approach aims to pinpoint the right therapeutic hypothesis for particular patient populations, addressing what Koller believes is the core of the industry's lack of success [1][2][3][4].
Koller highlights a "quieter revolution" occurring alongside AI advancements: quantitative biology. This field enables the measurement of biological systems with unprecedented precision. However, the sheer volume of data generated overwhelms human capacity for analysis [1][2][3][4].
"If you give that data to a person, their eyes will just glaze over because there's only so many cells someone can look at and only so many subtleties they can see in these images," Koller explains [1][2][3][4].
Insitro's success hinges on effectively combining expertise from both computer science and life sciences. Koller, whose background is in computer science, became interested in applying machine learning to biomedical problems in the late 1990s, seeking more challenging and meaningful applications for AI [1][2][3][4].
One of Insitro's most significant achievements has been fostering collaboration between computer scientists and medical researchers. Koller notes the stark differences in approach: "When you're an engineer, you're looking for the strongest, most consistent patterns... When you're a life scientist, oftentimes you're actually looking for the exceptions" [1][2][3][4].
To address this, Insitro has implemented cultural and organizational elements to facilitate open, constructive, and respectful engagement between these diverse scientific perspectives [1][2][3][4].
Insitro has already caught the attention of major pharmaceutical companies, signing deals with Eli Lilly and Bristol Myers Squibb to assist in developing medicines for metabolic diseases, neurological conditions, and degenerative disorders [1][2][3][4]. These partnerships signal growing industry interest in AI-driven drug discovery approaches.
Reference
[2]
[4]
U.S. News & World Report
|Better Drugs Through AI? Insitro CEO on What Machine Learning Can Teach Big Pharma
Iambic Therapeutics, a biotech firm backed by Nvidia, has introduced a new AI model called Enchant that could revolutionize drug discovery by significantly reducing development time and costs.
2 Sources
Google DeepMind and BioNTech are developing AI-powered lab assistants to enhance scientific research, automate routine tasks, and potentially predict experiment outcomes, marking a significant advancement in the application of AI in biology and drug discovery.
2 Sources
Researchers at SMU have developed SmartCADD, an open-source tool that combines AI, quantum mechanics, and computer-assisted drug design to significantly speed up the drug discovery process.
4 Sources
Harvard researchers develop an AI model called TxGNN that identifies existing drug candidates for over 17,000 rare and untreated diseases, offering hope for millions of patients worldwide. The tool outperforms current models in drug repurposing and side effect prediction.
5 Sources
OpenAI and Thrive Capital have backed a six-month-old AI drug discovery startup, signaling a significant investment in the intersection of artificial intelligence and pharmaceutical research.
2 Sources
