ArticleJournal of computer-aided molecular design2025
Mycobacterium tuberculosis FAS-II pathway targeted integrative deep learning based identification of potential anti-tubercular agents.
Article in Journal of computer-aided molecular design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mycobacterium tuberculosis (Mtb) continues to be one of the major contributors to the global burden of infectious diseases. Many drugs used in the current treatment regime have fallen prey to the puzzling phenomenon of antimicrobial resistance. Despite various attempts, few recent drugs have been developed against the bacterium (Sharma A, Vadodariya PK, Vaddoriya VN, Dhameliya TM (2025) Comprehensive updates on antitubercular endeavors identified in 2023. Synlett 36:2393-2410. https://doi.org/10.1055/a-2595-8032 ; Patel KI, Saha N, Dhameliya TM, Chakraborti AK (2025) Recent advancements in the quest of Benzazoles as anti-Mycobacterium tuberculosis agents. Bioorg Chem 155:108093. https://doi.org/10.1016/j.bioorg.2024.108093 ; Dhameliya TM, Bhakhar KA, Gajjar ND, Patel KA, Devani AA, Hirani RV (2022) Recent advancements and developments in search of anti-tuberculosis agents: a quinquennial update and future directions. J Mol Struct 1248:131473. https://doi.org/10.1016/j.molstruc.2021.131473 ). The proteins involved in Mtb's fatty acid synthase II (FAS-II) system are suitable drug targets. Many of the enzymes in this pathway, like β-ketoacyl-acyl carrier protein (KasA), 3-oxoacyl-[acyl-carrier-protein] synthase II (KasB) and β-ketoacyl-[acyl-carrier-protein] synthase III (FabH), are indispensable to Mtb but have no counterpart in humans. Here, we present an integrative approach starting with the curation of site specific dataset, exploratory data analysis with multiple machine learning models, virtual screening of compound library with hypertuned artificial neural networks (ANN) having hidden layers, molecular docking studies and in vitro validation to target some of the key elements involved in the mycolic acid chain elongation step during biosynthesis. By employing a multi-target paradigm, which is more resilient to antibiotic resistance due to simultaneous effect on multiple targets, we have targeted the above key synthases in the FAS-II pathway and validated the identified compounds' potential as anti-mycobacterial agents using in vitro biological evaluation. Molecular dynamics (MD) simulations further corroborated the potential of active compounds across targets. These molecules present new starting scaffolds, having inhibitory activities of up to 90% with respect to the positive control, for further improvement in terms of their potency as FAS-II pathway inhibitors with the help of medicinal chemistry efforts.
Indexed as
Identifiers
41186754What 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.