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# Ignota DSG Day 4 

## Images
- 

## Graphs
### Graphormer
- Results on finetuned Nav fold 1:
    - Accuracy: 0.7574931880108992
    - Precision: 0.7919463087248322
    - Recall: 0.6704545454545454
    - F1 Score: 0.7261538461538461
    - Cohen's Kappa: 0.5112304906699388
- Accidentally trained using MSE loss instead of BCE loss
- Graphormer has ~50M params, fine tuning on ~1.5k molecues of Nav is very small. Potential future work: finetune on larger herg dataset (~100k molecules) and then use that finetune model to further finetune on Cav and Nav datasets as they are more similar tasks compared to the default graphormer PCQM4Mv2 dataset 

## Presentation Plan

[Presentation](https://docs.google.com/presentation/d/1lf8ymuSpzCN8bXFrVBTtsMo3k5Ce2uMXsJjMqkRkv5Q/edit?usp=sharing)

1. Introduction // Ben 
	1. Why is toxicity prediction important?
	2. Current methods for estimating toxicity
	3. Role of ML in tox prediction
2. Overview of Ignota challenge // Mariana
	1. Datasets
	2. Challenge
	3. Aims
3. Graphs // Ben
	1. Representation
	2. What we did 
	3. Results
4. Images / 2D representations // Mariana
	1. Representation
	2. What we did
	3. Results
5. Comparison of results & a bit on representations // Lilli
	1. Pros & cons ?
6. Future Work & Conclusions // Lilli
	1. Impact of our work for Ignota

Scaffolds???