ReviewEMBO molecular medicine2026
Integrating chemical, genetic, and feasibility assessments for anti-tubercular target validation.
Review in EMBO molecular medicine, 2026. 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
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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
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Authors and funding
23 authors.
Funding
Abstract
Despite the approval of two first-in-class anti-tuberculars over the past two decades, the global burden of tuberculosis (TB) remains unacceptably high, in part due to the emergence and spread of drug-resistant strains of Mycobacterium tuberculosis (Mtb). This review summarizes advances and ongoing challenges in anti-TB drug discovery, focusing on identifying and validating novel targets. Highlighted is a framework developed by the TB Drug Accelerator (TBDA) consortium for target validation in Mtb. Two computational platforms, DAIKON and PARSNIP, allow the systematic evaluation of targets across multiple dimensions, including chemical validation, genetic essentiality, vulnerability, and the feasibility to identify drug-like molecules for a target of interest. Case studies of Pks13 and NadE illustrate how these parameters guide target prioritization and risk assessment. By integrating these metrics, the framework enables dynamic, transparent target ranking, supporting development of both pan-TB and treatment-shortening regimens. This paradigm is adaptable to other bacterial pathogens and is designed to improve evidence-based decision-making in antibacterial drug discovery.
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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.