Dataset Information
Ye2021 - Identification of active molecules against Mycobacterium tuberculosis with ML
Ontology highlight
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.. Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos46ev
ORGANISM(S): Homo sapiens
SUBMITTER: Zainab Ashimiyu-Abdusalam
PROVIDER: MODEL2404080003 | biostudies-other |
SECONDARY ACCESSION(S): 33822874
REPOSITORIES: biostudies-other
Publications
Identification of active molecules against Mycobacterium tuberculosis through machine learning.
Briefings in bioinformatics 20210901 5
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb) and it has been one of the top 10 causes of death globally. Drug-resistant tuberculosis (XDR-TB), extensively resistant to the commonly used first-line drugs, has emerged as a major challenge to TB treatment. Hence, it is quite necessary to discover novel drug candidates for TB treatment. In this study, based on different types of molecular representations, four machine learning (ML) algorithms, including suppo ...[more]
