Prediction of antimicrobial class specificity using simple machine learning methods applied to an antimicrobial knowledge graph. The knowledge graph is built on ChEMBL, Co-ADD and SPARK. Endpoints are broad terms such as activity against gram-positive or gram-negative bacteria. The best model according to the authors is a Random Forest with MHFP6 fingerprints.
- EOS model ID:
eos74km - Slug:
antimicrobial-kg-ml
- Input:
Compound - Input Shape:
Single - Task:
Classification - Output:
Probability - Output Type:
Float - Output Shape:
List - Interpretation: Class probabilities for each antimicrobial class
- Publication
- Source Code
- Ersilia contributor: miquelduranfrigola
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