Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and development tasks.
Biomedical networks are universal descriptors of systems of interacting elements, from protein interactions to disease networks, all the way to healthcare systems and scientific knowledge.
Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.
Next, these embeddings will be fed into the knowledge embedding module to generate knowledge embeddings that are pretrained using external knowledge on pharmaco-kinetic properties and trial risk from the web.
Unstructured clinical text in EHRs contains crucial information for applications including decision support, trial matching, and retrospective research.
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics.
Furthermore, most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is a more meaningful but harder task.
G-Meta learns how to quickly adapt to a new task using only a handful of nodes or edges in the new task and does so by learning from data points in other graphs or related, albeit disjoint label sets.
Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space.
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery.
Clinical notes contain rich data, which is unexploited in predictive modeling compared to structured data.
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality.
Clinical notes contain information about patients that goes beyond structured data like lab values and medications.