Systems and methods for implementing drug mechanisms of action with machine learning

A computer-implemented method for predicting whether a post-marketing requirement (PMR) will be imposed on a clinical trial, the method comprising:

• obtaining data associated with a clinical trial;
• obtaining mechanism of action (MOA) data associated with the clinical trial, the MOA data indicative of a hierarchical tree structure of relationships between the MOA data;
• generating a linear representation of one or more branches of the hierarchical tree structure;
• and
• generating a prediction of whether a PMR will be imposed on the clinical trial by applying a trained machine learning model to the data associated with the clinical trial and the linear representation of the one or more branches, the trained machine learning model having been trained based on clusters of linear representations of historical MOA data.

A system for predicting whether a post-marketing requirement (PMR) will be imposed on a clinical trial, the system comprising:

• one or more processors;
• and
• a non-transitory computer-readable medium storing instructions that are executable by the one or more processors to perform operations, including:
• obtaining data associated with a clinical trial;
• mechanism of action (MOA) data associated with the clinical trial, the MOA data indicative of a hierarchical tree structure of relationships between the MOA data;
• generating a linear representation of one or more branches of the hierarchical tree structure;
• and
• generating a prediction of whether a PMR will be imposed on the clinical trial, by applying a trained machine learning model to the data associated with the clinical trial and the linear representation of the one or more branches, the trained machine learning model having been trained based on clusters of linear representations of historical MOA data.