ReviewACS omega2026
Multiscale Computational Enzymology of CYP450 Biotransformation of Organic Halogenated Pollutants: Methods and Environmental Perspectives.
Review 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
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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Halogenated organic pollutants (HOPs) represent a persistent class of environmental contaminants with high stability, bioaccumulation potential, and toxicity. Cytochrome P450 enzymes (CYP450s) play a pivotal role in their oxidative biotransformation, yet mechanistic details often remain unresolved by experimental methods alone. To bridge this gap, this review synthesizes recent advances in multiscale computational enzymology to establish an integrated workflow. This approach links molecular docking and molecular dynamics (MD) for conformational sampling with quantum mechanics and molecular mechanics (QM/MM) and density functional theory (DFT) for electronic structure analysis. We critically evaluate how this multiscale framework complements experimental data to resolve enzyme-substrate recognition, conformational gating, and bond-activation energetics. These are mechanistic details that single-scale approaches cannot provide. Furthermore, we discuss emerging data-driven tools while addressing critical limitations, including training-data bias in machine learning and the lack of standardization. By connecting atomistic mechanistic insights to broader environmental implications, we propose a roadmap for developing predictive models to support HOP risk assessment and bioremediation strategies.
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 OpenQuestion graph.