ArticleChemistryOpen2025
Machine Learning-Based High-Throughput Screening, Molecular Modeling and Quantum Chemical Analysis to Investigate Mycobacterium tuberculosis MetRS Inhibitors.
Article in ChemistryOpen, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Inhibitors of Mycobacterium tuberculosis methionyl-tRNA synthetase are sensitive to the mutations E24A and L293A in aminoacyl-adenylate binding site.Biotechnology letters · 2026Article
- Discovery of PIM-1 kinase inhibitors from marine natural products through machine learning and structure-based screening.Research in pharmaceutical sciences · 2026Article
- Computational Study on Potentially Active Antibacterial Compounds in Secondary Metabolites of Extremophilic Microorganisms.ChemistryOpen · 2026Article
- Machine Learning-Based High-Throughput Screening, Molecular Modeling and Quantum Chemical Analysis to Investigate Mycobacterium tuberculosis MetRS Inhibitors.ChemistryOpen · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The increasing drug resistance of Mycobacterium tuberculosis (Mtb) complicates its effective treatment and often leads to severe side effects. This research aims to pinpoint the potential drug candidates targeting Mtb methionyl-tRNA synthetase (MtbMetRS) using in silico techniques. Employing machine learning algorithms, including Random Forest, Extra Trees, and Nu-Support Vector, a voting classifier was built to screen 10 million molecules. A total of 590 molecules were filtered and analyzed for mutagenicity and other toxicities, resulting in 169 candidates for molecular docking. Among these, 1-[4-(1,3-benzodioxol-5-ylmethyl)piperazin-1-yl]-2-phenylsulfanylethanone (L1) and 1-ethyl-6-fluoro-4-oxo-7-(4-pentanoylpiperazin-1-yl)quinoline-3-carboxylic acid (L2) demonstrated strong binding affinities (-12.74 kcal/mol for L1 and -11.83 kcal/mol for L2) and favorable pharmacokinetic properties. MM/PBSA, DFT calculations, and LD
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.