Dataset Information

Ye2021 - Identification of active molecules against Mycobacterium tuberculosis with ML

ABSTRACT: Identification of active molecules against Mycobacterium tuberculosis using an ensemble of data from ChEMBL25 (Target IDs 360, 2111188 and 2366634). The final model is a stacking model integrating four algorithms, including support vector machine, random forest, extreme gradient boosting and deep neural networks.. Model Type: Predictive machine learning model. Model Relevance: Predicts Probability of M.tb inhibition. Model Encoded by: Amna Ali (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos46ev

SUBMITTER: Zainab Ashimiyu-Abdusalam