metadata.yml

Identifier: eos3mk2
Slug: bbbp-marine-kinase-inhibitors
Status: Ready
Title: BBBP model tested on marine-derived kinase inhibitors
Description: A set of three binary classifiers (random forest, gradient boosting classifier,
  and logistic regression) to predict the Blood-Brain Barrier (BBB) permeability of
  small organic compounds. The best models were applied to natural products of marine
  origin, able to inhibit kinases associated with neurodegenerative disorders. The
  training set size was around 300 compounds.
Mode: Retrained
Task: Classification
Input: Compound
Input Shape: Single
Output: Probability
Output Type: Float
Output Shape: List
Interpretation: Classification score over three classifiers, namely random forest
  (rfc), gradient boosting classifier (gbc), and logistic regression (logres).
Tag:
- Drug-likeness
- Permeability
Publication: https://pubmed.ncbi.nlm.nih.gov/30699889/
Source Code: https://github.com/plissonf/BBB-Models
License: MIT
DockerHub: https://hub.docker.com/r/ersiliaos/eos3mk2
Docker Architecture:
- AMD64
- ARM64
S3: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos3mk2.zip