Abstract: A system includes a memory and a processor coupled to the memory. The processor receives a user credential associated with a first user and authorizes an avatar of the first user to enter a virtual environment. When the first user is registered with a first entity, the processor obtains a user profile of the first user. Based on the user profile, the processor determines and indicates to the first user in the virtual environment that a first type of data file of the first user qualifies the first user to obtain a product or service for a first amount of data objects. The processor receives a selection of the product of service from the first user, receives a command to transfer the first amount of data objects from the first type of data file to a second data file, and transfers the first amount to the second data file.

Abstract: Aspects of the present disclosure provide methods, devices, and computer-readable storage media that support dynamic enforcement of access control policies in a standardized manner. An administrator console enables access control policies to be defined as classes that may be combined and leveraged to rapidly define access control policies for enforcement in a standardized manner. An interceptor operates to detect access requests and perform policy administration (e.g., determining to grant/deny access) for the access requests and where access is granted, initiate policy resolution (e.g., determine any restrictions on the granted access request). An enforcer provides functionality for enforcing policy resolution outcomes, such as restricting access to information stored in a database or disabling interactive elements of a user interface.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify molecules or compounds for use in pharmaceuticals. In aspects, one or more machine learning (ML) models may be trained to identify molecules based on pharmaceutical data that indicates properties of previously-identified pharmaceutical molecules, such as physiochemical structure, side effects, toxicity, solubility, and the like. The ML models may include generative models, such as generative adversarial networks or variational autoencoders. The trained ML models may be used to identify new (e.g., previously-unidentified) molecules, or the trained ML models may be provided to client devices for use in molecule identification (e.g., drug discovery).

Abstract: Implementations of the present disclosure include receiving a document including data representative of an adverse event, providing a case including a set of fields, each field being populated with a value, querying a safety system based on a query that includes values of the case, receiving a sparse matrix based on query results responsive to the query, the sparse matrix including two or more vectors, each vector of the two or more vectors representing a historical case score, for each historical case, determining a score based on a respective vector of the two or more vectors and an input vector representative of the case, each score representing a degree of duplicity between the case and a respective historical case, and providing historical cases for display to a user, the historical cases including the two or more historical cases in rank order based on respective scores.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to screen candidate pharmaceutical molecules or compounds for pharmaceutical uses (e.g., treating diseases or conditions). The screening may be based on structural similarity between the candidate pharmaceutical molecules and various pharmaceutical targets (e.g., proteins, nucleic acids, etc.). In aspects, one or more machine learning (ML) models may be trained to assign a candidate pharmaceutical molecule to one of multiple clusters based on chemical/physical properties of the candidate pharmaceutical molecule and chemical/physical properties of pharmaceutical targets associated with the clusters. The pharmaceutical targets associated with the cluster may be scored based on comparisons between the candidate pharmaceutical molecule and the pharmaceutical target, and a subset of the pharmaceutical targets may be identified based on the scores.

Abstract: Implementations of the present disclosure include receiving a document including data representative of an adverse event, providing a case including a set of fields, each field being populated with a value, querying a safety system based on a query that includes values of the case, receiving a sparse matrix based on query results responsive to the query, the sparse matrix including two or more vectors, each vector of the two or more vectors representing a historical case score, for each historical case, determining a score based on a respective vector of the two or more vectors and an input vector representative of the case, each score representing a degree of duplicity between the case and a respective historical case, and providing historical cases for display to a user, the historical cases including the two or more historical cases in rank order based on respective scores.