Leveraging Graph Attention Mechanisms to Create an Explainable Multi-Function Machine Learning Model
| dc.creator | Mathew, Ezek | en_US |
| dc.creator | Madugula, Sita Sirisha | en_US |
| dc.creator | Emmitte, Kyle | en_US |
| dc.creator | Liu, Jin | en_US |
| dc.date.accessioned | 2024-04-18T13:03:25Z | |
| dc.date.available | 2024-04-18T13:03:25Z | |
| dc.date.issued | 2024-03-21 | en_US |
| dc.description.abstract | Purpose: Identifying target-specific ligands is a difficult task, especially in cases where receptors display high structural similarity. Such is the case for metabotropic glutamate receptor subtype 2 (mGlu2) and metabotropic glutamate receptor subtype 3 (mGlu3), which are prime targets for various neurological treatments. However, signal transduction through these two receptors often yields opposing physiological function and differentially affect pathologies. Methods: Understanding the need to differentiate ligands based on their binding to mGlu2 and mGlu3, we employed a machine learning (ML) approach. The ML model performed three distinct tasks and leveraged transfer learning to inform each subsequent task. Task 1: Simple Classification was performed, as the ML model predicted if the ligands displayed selectivity for the mGlu2 or mGlu3 class. Task 2: Regression was performed, as the ML model estimated the IC50 values of individual input ligands. The classification weights from Task 1 were broadcasted into the attention layers of the ML model for Task 2, serving as a starting point. Task 3: Classification was performed, as the ML model sought to determine if a ligand displayed low or high potency for the target class. Classification weights and regression weights from previous tasks were broadcasted into the model. Results: The model yielded greater than 99% accuracy in the selectivity classification task, while also delivering satisfactory performance when predicting potency (72.80% error). The model yielded 83% accuracy in correctly identifying high potency mGlu2 ligands, as high potency mGlu2 compounds. Meanwhile, the algorithm displayed 75% accuracy in correctly identifying high potency mGlu3 ligands, as high potency mGlu3 compounds. Conclusions: This approach allows for prediction of multiple target properties using a single model. With access to other high-quality datasets, this model has the potential to apply to other ligand classes of interest, posing great potential for drug repurposing studies. | en_US |
| dc.description.sponsorship | This work is partially supported by a grant (#RP210046) from the Cancer Prevention and Research Institute of Texas (CPRIT). | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12503/32766 | |
| dc.language.iso | en | |
| dc.title | Leveraging Graph Attention Mechanisms to Create an Explainable Multi-Function Machine Learning Model | en_US |
| dc.type | poster | en_US |
| dc.type.material | text | en_US |