Signature Therapeutics, Inc.

Aible Inc.

Theravance, Inc.

Jonathan W Wray

Thomas E Jenkins

Arijit Sengupta

Julie D Seroogy

Craig Husfeld

John H Griffin

Daniel S Kane

Geoffrey Grant

Grigory Nudelman

Yu-Hua Ji

Edmund J Moran

Roger Briesewitz

Richard Cooke

Joseph Veltkamp

Robert Shaw

John Delaney

Craig O Husfeld

A method includes receiving, via a graphical user interface, data characterizing user input specifying a cost of error of a prediction, a model type requirement, and a computational resource allocation for model development; determining, based on the received data and using a model, a prediction of model performance; and rendering, within the graphical user interface, a plot including a first axis, a second axis, and a region characterizing the determined prediction of model performance, the first axis including a characterization of a first performance metric and the second axis including a characterization of a second performance metric. Related apparatus, systems, techniques and articles are also described.

Interface for visualizing and improving model performance

Data is received characterizing an impact function, a first plurality of inputs to the impact function, and a plurality of weights associated with the first plurality of inputs. A first coefficient characterizing an interaction between a first input and a second input of the plurality of inputs is calculated based on a first set of weights of the plurality of weights. The first set of weights are indicative of interaction between the first input and the second input. A graphical object including a plurality of regions is displayed in a graphical user interface (GUI). A first region and a second region of the plurality of regions are indicative of the first input and the second input, respectively. A second graphical object connecting the first region to the second region is displayed in the GUI. Related apparatus, systems, techniques and articles are also described.

User interface for impact analysis

A method includes monitoring performance of a generated model while the generated model is being used for classification on live data, the monitoring including determining a first performance value of the generated model at a first point in time and determining a second performance value of the generated model at a second point in time; rendering, within a graphical user interface, a plot including a first axis and a second axis, the first axis including a characterization of a first performance metric and the second axis including a characterization of a second performance metric; and rendering, within the graphical user interface and the plot, a first graphical object at a first location characterizing the first performance value and a second graphical object at a second location characterizing the second performance value. Related apparatus, systems, techniques and articles are also described.

Data characterizing inputs to a prediction process that classifies events, an output of the prediction process, and feedback data characterizing a performance of the outcome is monitored. A resource capacity affecting the outcome of the prediction process, and/or a cost-benefit affecting the outcome of the prediction process is determined from the monitoring. The determined resource capacity and/or the determined cost-benefit is provided. Related apparatus, systems, techniques and articles are also described.

Automatically learning process characteristics for model optimization

A first node having a first location in the first GUI and indicative of a first analytical task is provided in a first graphical user interface (GUI). A multi-step analytical process includes the first analytical task. Data is received characterizing a first user input indicative of selection of the first node. A first set of nodes is displayed in the first GUI. The first set of nodes are adjacent to the first node and are associated with a next step of the multi-step analytical process. The multi-step analytical process includes at least one of importing a dataset, building a model using the dataset, and/or deploying the model to operate on live data. Related apparatus, systems, techniques and articles are also described.

User interface for collaborative analytics platform

A method includes receiving, via a model building platform, historical user behavior including historical data analysis characteristics; generating, based on the historical data analysis characteristics, a blueprint for guiding user action to accomplish a task, the generating including constructing the blueprint using the historical data analysis characteristics; receiving, via graphical user interface, user input requesting generation of a model and a task description; determining, using the blueprint and based on the task description, data analysis characteristics; and rendering, within the graphical user interface, a prompt to select the determined data analysis characteristics. Related apparatus, systems, techniques and articles are also described.

Interface for generating models with customizable interface configurations

Pharmaceutical compositions and their methods of use are provided, where the pharmaceutical compositions comprise an amphetamine prodrug that provides enzymatically-controlled release of amphetamine or an amphetamine analog. The composition can further comprise an enzyme inhibitor that interacts with the enzyme(s) that mediates the enzymatically-controlled release of amphetamine or the amphetamine analog from the amphetamine prodrug so as to attenuate enzymatic cleavage of the amphetamine prodrug.

Compositions comprising enzyme-cleavable amphetamine prodrugs and inhibitors thereof

Performance of a first generated model can be monitored while the first generated model is deployed for use on live data. The monitoring can include determining a first performance value of the first generated model. Performance of a second generated model can be monitored while the second generated model is deployed for use on live data. The monitoring can include determining a second performance value of the second generated model. A plot including a first axis and a second axis can be rendered. The first axis can include a characterization of a first performance metric and the second axis can include a characterization of a second performance metric. A first graphical object at a first location characterizing the first performance value and a second graphical object at a second location characterizing the second performance value can be rendered. Related apparatus, systems, techniques and articles are also described.

A method includes receiving data characterizing a target accuracy and a performance metric of a model; rendering, within a graphical user interface display space, a plot including a first axis and a second axis, the first axis including a characterization of false positive and the second axis including a characterization of false negative; and rendering, within the graphical user interface display space and within the plot, a graphical object at a location characterizing the performance metric and a visualization indicative of the target accuracy. Related apparatus, systems, techniques and articles are also described.

A method of providing a patient with controlled release of ketone-containing opioid using a prodrug capable, upon enzymatic activation and intramolecular cyclization, of releasing the ketone-containing opioid is disclosed. The disclosure also provides such prodrug compounds and pharmaceutical compositions comprising such compounds. Such pharmaceutical compositions can optionally include an enzyme inhibitor that interacts with the enzyme(s) to mediate the enzymatically-controlled release of the ketone-containing opioid from the prodrug so as to modify enzymatic cleavage of the prodrug. Also included are methods to use such compounds and pharmaceutical compositions.

Compositions comprising enzyme-cleavable ketone-modified opioid prodrugs and optional inhibitors thereof

Pharmaceutical compositions and their methods of use are provided, where the pharmaceutical compositions comprise an opioid prodrug that provides enzymatically-controlled release of an opioid, and an enzyme inhibitor that interacts with the enzyme(s) that mediates the enzymatically-controlled release of the opioid from the opioid prodrug so as to attenuate enzymatic cleavage of the opioid prodrug.

Compositions comprising enzyme-cleavable opioid prodrugs and inhibitors thereof

Pharmaceutical compositions and their methods of use are provided, where the pharmaceutical compositions comprise a phenolic opioid prodrug that provides enzymatically-controlled release of a phenolic opioid, and an enzyme inhibitor that interacts with the enzyme(s) that mediates the enzymatically-controlled release of the phenolic opioid from the prodrug so as to attenuate enzymatic cleavage of the prodrug.

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San Carlos, CA, United States of America

Jonathan W Wray