ArticleACS omega2026
Combining Artificial Intelligence and Human Expertise in Pursuit of Novel PolQ Inhibitors.
Article in ACS omega, 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
No grant is acknowledged in the PubMed record.
Abstract
Application of generative AI, synergistically with traditional knowledge-based approaches, has expedited the discovery of small-molecule PolQ polymerase domain inhibitors. Leveraging AI-generated ligand designs, a structurally diverse set of compounds were synthesized and evaluated, ultimately identifying a novel oxime-based scaffold as a promising starting point. Although the initial hit demonstrated only modest biochemical potency, the oxime moietyan unexplored vector in this chemical spaceoffered an opportunity for strategic substitution. Gratifyingly, using this strategy, a highly potent array of functionalized oxime PolQ inhibitors displaying promising initial ADME properties was delivered, underscoring the power of generative AI to unlock new chemical space and accelerate lead optimization.
Identifiers
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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 Socratic graph.