Abstract: A computer system is disclosed for designing molecules with a desired property (e.g., the ability to inhibit SARS-CoV-2). The computer system includes a computer-based processor and a computer-readable medium storing computer-readable instructions that, when executed by the computer-based processor, cause the computer-based processor to embody: a computer-based search engine configured to search for proposed molecules, a computer-based property predictor to predict a likelihood of the desired property being present in the proposed molecules, and a computer-based energy model to approximate a degree of similarity between the proposed molecules and molecules known to possess the desired property. The search is guided by the property predictor's predictions and the energy model's approximations.

Abstract: A computer-implemented method of characterizing a person's hand geometry includes inputting a three-dimensional (3D) point cloud of the person's hand into a clustered dynamic graph convolutional neural network (clustered DGCNN), and processing the 3D point cloud, with a shared network portion of the clustered DGCNN, to create a processed version of the three-dimensional point cloud. The method further includes, with a shape regression network portion of the clustered DGCNN, assigning each respective feature point in the processed version of the 3D point cloud to a corresponding one of a plurality of pre-defined clusters, and applying one or more transformations to the feature points assigned to each respective cluster to produce per cluster shape parameters that represent shapes associated with portions of the person's hand that correspond to associated ones of the pre-defined clusters. Each pre-defined cluster corresponds to a unique part of a hand's surface.

Abstract: A method for extracting hierarchical features from data defined on a geometric domain is provided. The method includes applying on said data at least an intrinsic convolution layer, including the steps of applying a patch operator to extract a local representation of the input data around a point on the geometric domain and outputting the correlation of a patch resulting from the extraction with a plurality of templates. A system to implement the method is also described.

Abstract: A method for extracting hierarchical features from data defined on a geometric domain is provided. The method includes applying on said data at least an intrinsic convolution layer, including the steps of applying a patch operator to extract a local representation of the input data around a point on the geometric domain and outputting the correlation of a patch resulting from the extraction with a plurality of templates. A system to implement the method is also described.

Abstract: A method for extracting hierarchical features from data defined on a geometric domain is provided. The method includes applying on said data at least an intrinsic convolution layer, including the steps of applying a patch operator to extract a local representation of the input data around a point on the geometric domain and outputting the correlation of a patch resulting from the extraction with a plurality of templates. A system to implement the method is also described.