Adaptation for Single-Task Prediction on Molecular Data

[wdon021]

Hello PAR-NeurIPS21 Team,

I'm currently exploring the capabilities of your framework for a project involving molecular data sourced from PubChem. Specifically, my dataset includes 26,564 SMILES strings, each associated with IC50 values for approximately 10 different kinases. The IC50 values have been standardized within a range of [0-1]. Notably, the majority of the molecules in the dataset typically have IC50 values for only one kinase.

Given the structure of this dataset, I am interested in adapting your multi-task learning framework for a single-task prediction setup. Here are a few questions and considerations I have:

1. Data Compatibility: Can the current MoleculeDataset class be directly used for datasets structured as described above, or would it require modifications to handle the specifics of IC50 data and missing values efficiently?
2. Model Adjustment: What changes would be recommended to adjust the model from handling multiple tasks to focusing solely on predicting the IC50 value for a specific kinase?
3. Training and Evaluation: Could you provide guidance on simplifying the training loops and evaluation metrics to suit a single-task learning scenario? Are there specific parts of the Meta_Trainer class that should be modified or removed?
4. Loss Function and Metrics: What loss function and performance metrics would you recommend for a regression task focused on IC50 value prediction?

I appreciate any insights or suggestions you could provide to help adapt this framework to my needs. Thank you for your support and for the development of this interesting project.

Best regards,