metadata.yml

Identifier: eos74km
Slug: antimicrobial-kg-ml
Status: In progress
Title: Antimicrobial class specificity prediction
Description: 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.
Mode: Pretrained
Task: Classification
Input: Compound
Input Shape: Single
Output: Probability
Output Type: Float
Output Shape: List
Interpretation: Class probabilities for each antimicrobial class
Tag:
- Antimicrobial activity
Publication: https://www.biorxiv.org/content/10.1101/2024.12.02.626313v1.full
Source Code: https://github.com/IMI-COMBINE/broad_spectrum_prediction
License: MIT
S3: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos74km.zip
DockerHub: https://hub.docker.com/r/ersiliaos/eos74km
Docker Architecture:
- AMD64
- ARM64