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

PROVIDER: MODEL2404080003 | BioModels | 2024-04-08

REPOSITORIES: BioModels

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Identification of active molecules against Mycobacterium tuberculosis through machine learning.

Ye Qing Q Chai Xin X Jiang Dejun D Yang Liu L Shen Chao C Zhang Xujun X Li Dan D Cao Dongsheng D Hou Tingjun T

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]

PMID: 33822874

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