Abstract: Herein disclosed are a methods and systems of SAMMI—a machine learning-based workflow that uses human annotations as labels for training models—used to predict human-based annotations for drug discovery. SAMMI receives an input to a model trained using human-annotated data, wherein the human-annotated data comprises at least one annotation associated with a triage-progressability annotation of whether to progress the input for the drug discovery. SAMMI also receives a set of features. The set of features are associated with the input, the model, and the triage-progressability of the input. The set of features is applied to the model to predict whether the input is triage-progressible. A model output is provided based on the prediction.

Abstract: A computer-implemented method of querying a graph to assess relationships amongst graph nodes comprises determining a query node on the graph, identifying one or more target nodes on the graph in relation to the query node based on a set of connectivity patterns; generating graph-based statistics for each target node of the one or more target nodes, wherein the graph-based statistics are extracted for subgraphs associated with each target node and the query node; and assessing the graph-based statistics of each target node to determine predicted relationships between the one or more target nodes and the query node.

Abstract: Method(s), apparatus, and system(s) are provided for selecting a data model configuration for use in training predictive models comprise receiving two or more data model configurations, extracting a data model for each of the two or more data model configurations from a knowledge graph, generating a separate predictive model for each of the extracted data models, scoring the output of each separate predictive model based on a benchmark data set, and selecting at least one data model configuration of the two or more data model configurations based on the output scores.