Atomwise

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Atomwise


This research and subsequent developments led to AtomNet. Now, through its many partnering programs and robust venture funding, Atomwise is actively seeking new collaborators to address the global health challenges of our time using the power of artificial intelligence. All told, Atomwise has over fifty distinct molecular discovery programs. Our AI technology makes this optimization smarter and faster by finding patterns in the data that a human would never be able to see. Atomwise uses AI for its structure-based, drug design technology, which is designed to enable scientists to predict how well a small molecule will bind to a target protein of interest, as well as remove sole reliance on empirical screening. In this way, AtomNet autonomously learns the features governing molecular binding, and avoids the manual process of tweaking and over-parameterizing binding features that typified traditional computational methods. In return for the access, Pfizer agreed to pay a technology access fee and additional milestone payments for each target protein of interest; the amounts were not disclosed at the time. Dozens of its discovery programs have achieved success in the hands of its partners, contrasting with an industry that typically has extremely high rates of failure for comparable work. Convolutional networks are known to achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. What potential drugs will bind my target protein? It is a problem faced by researchers in every pharmaceutical company, whether small or large, and in thousands of research institutions across the world. Biochemical interactions are primarily local, and can be modelled by similarly-constrained machine learning architectures. Specifically, where an image is represented as a 2-dimensional grid of pixels containing channels for red, green, and blue colors, AtomNet represents a protein-ligand pair as a set of 3-dimensional volumetric pixels containing channels for carbon, oxygen, nitrogen, etc atom types. Our technology uses a statistical approach that extracts the insights from millions of experimental affinity measurements and thousands of protein structures to predict the binding of small molecules to proteins. Atomwise can analyze a very large chemical space — billions and billions of compounds — to identify a small subset with high specificity for synthesis and testing. The Atomwise approach demonstrates a new model for a pharmaceutical industry that is facing a crisis of declining productivity, spending more on research each year, yet achieving fewer breakthroughs per dollar. Its groundbreaking AtomNet drug research technology learns like a human chemist, using powerful deep learning algorithms and elastic supercompute platforms to analyze millions of potential medicines each day. Atomwise uses AI for its structure-based, drug design technology, which is designed to enable scientists to predict how well a small molecule will bind to a target protein of interest, as well as remove sole reliance on empirical screening. As part of the agreement, Atomwise said, it will have the option to develop compounds from the collaboration that Lilly chooses not to advance into clinical testing. How do I reduce off-target effects? Existing portfolio companies include AImotive, Bright. AtomNet showed that the convolutional concepts of feature locality and hierarchical composition could be applied to the modeling of bioactivity and chemical interactions. The company says its tech can screen billions of compounds, and has demonstrated success using homology-modeled proteins. In return for the access, Pfizer agreed to pay a technology access fee and additional milestone payments for each target protein of interest; the amounts were not disclosed at the time. When chemical groups interact, such as through hydrogen bonding or pi-bond stacking, the strength of their repulsion or attraction may vary with their type, distance, and angle.

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Atomwise

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The World’s Largest Prospective Validation of Machine Learning for Drug Discovery




Its groundbreaking AtomNet technology reasons like a human chemist, using powerful deep learning algorithms and supercomputers to analyze millions of potential molecules each day. The greatest challenge today in pre-clinical drug discovery and development is identifying a drug candidate that is both effective and safe. This fundamental tool makes it possible for chemists to pursue hit discovery, lead optimization and toxicity predictions with unparalleled precision and accuracy. Dozens of its discovery programs have achieved success in the hands of its partners, contrasting with an industry that typically has extremely high rates of failure for comparable work. What potential drugs will bind my target protein? Our AI technology makes this optimization smarter and faster by finding patterns in the data that a human would never be able to see. For more information on DCVC, visit www. DCVC brings to bear a unique model that unites a team of experienced venture capitalists with more than 50 technology executives and experts CTOs, CIOs, Chief Scientists, Principal Engineers, Professors at Stanford, Berkeley, and major technical universities with significant tenures at top technology companies and research institutions worldwide. This research and subsequent developments led to AtomNet. Atomwise uses AI for its structure-based, drug design technology, which is designed to enable scientists to predict how well a small molecule will bind to a target protein of interest, as well as remove sole reliance on empirical screening.

Atomwise


This research and subsequent developments led to AtomNet. Now, through its many partnering programs and robust venture funding, Atomwise is actively seeking new collaborators to address the global health challenges of our time using the power of artificial intelligence. All told, Atomwise has over fifty distinct molecular discovery programs. Our AI technology makes this optimization smarter and faster by finding patterns in the data that a human would never be able to see. Atomwise uses AI for its structure-based, drug design technology, which is designed to enable scientists to predict how well a small molecule will bind to a target protein of interest, as well as remove sole reliance on empirical screening. In this way, AtomNet autonomously learns the features governing molecular binding, and avoids the manual process of tweaking and over-parameterizing binding features that typified traditional computational methods. In return for the access, Pfizer agreed to pay a technology access fee and additional milestone payments for each target protein of interest; the amounts were not disclosed at the time. Dozens of its discovery programs have achieved success in the hands of its partners, contrasting with an industry that typically has extremely high rates of failure for comparable work. Convolutional networks are known to achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. What potential drugs will bind my target protein? It is a problem faced by researchers in every pharmaceutical company, whether small or large, and in thousands of research institutions across the world. Biochemical interactions are primarily local, and can be modelled by similarly-constrained machine learning architectures. Specifically, where an image is represented as a 2-dimensional grid of pixels containing channels for red, green, and blue colors, AtomNet represents a protein-ligand pair as a set of 3-dimensional volumetric pixels containing channels for carbon, oxygen, nitrogen, etc atom types. Our technology uses a statistical approach that extracts the insights from millions of experimental affinity measurements and thousands of protein structures to predict the binding of small molecules to proteins. Atomwise can analyze a very large chemical space — billions and billions of compounds — to identify a small subset with high specificity for synthesis and testing. The Atomwise approach demonstrates a new model for a pharmaceutical industry that is facing a crisis of declining productivity, spending more on research each year, yet achieving fewer breakthroughs per dollar. Its groundbreaking AtomNet drug research technology learns like a human chemist, using powerful deep learning algorithms and elastic supercompute platforms to analyze millions of potential medicines each day. Atomwise uses AI for its structure-based, drug design technology, which is designed to enable scientists to predict how well a small molecule will bind to a target protein of interest, as well as remove sole reliance on empirical screening. As part of the agreement, Atomwise said, it will have the option to develop compounds from the collaboration that Lilly chooses not to advance into clinical testing. How do I reduce off-target effects? Existing portfolio companies include AImotive, Bright. AtomNet showed that the convolutional concepts of feature locality and hierarchical composition could be applied to the modeling of bioactivity and chemical interactions. The company says its tech can screen billions of compounds, and has demonstrated success using homology-modeled proteins. In return for the access, Pfizer agreed to pay a technology access fee and additional milestone payments for each target protein of interest; the amounts were not disclosed at the time. When chemical groups interact, such as through hydrogen bonding or pi-bond stacking, the strength of their repulsion or attraction may vary with their type, distance, and angle.

Atomwise


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