BioModel Discussion Issue for eos1n4b

[Zainab-ik]

Summary !!!

• BioModel Name: Li2021 - HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors.

• BioModel Tag: Machine learning model, Ersilia, FAIR AIML

• Metadata Comments: The model predicts the inhibitory potential of small molecules against Histone deacetylase 3 (HDAC3)

1. The drug target "Histone deacetylase 3" is related to different diseases such as cancer, and diabetes. Those aren't related metadata.
2. This model was built using 5 algorithm and 3 descriptors; Algorithm - k-Nearest Neighbour (KNN), Support Vector Machine (SVM), Random forest (RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Network (DNN). Descriptors - Mordred descriptors, MACCS key, Morgan fingerprint.
3. The best performing model is the XGBoost with the Morgan fingerprint. (that's the deployed model to the GUI application) For our annotation, we'd only be including the best performing model and its feature.
4. We have an ROC enrichment as an evaluation metrics between the validation and training dataset. I'm not sure it fits into the metadata. What do you think?

• Contributor:

  roles	Names
  Curator	@Zainab-ik
  Code contributor	@Richiio
  Reviewer	@GemmaTuron

• Curation status : manually curated

• Annotation file: here

[Zainab-ik]

Hi @GemmaTuron Annotation complete. Kindly review.

[Zainab-ik]

This model was uploaded during the hackathon. I've currently reviewed, and updated the submission.