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Database: AmericanChemicalSociety
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TY  - JOUR
AU  - Szabo, Drew
AU  - MacOscar, Kian
AU  - Plaisted, Katie
AU  - McGarrigle, Marie
AU  - Boxall, Alistair B. A.
AU  - Mosely, Jackie A.
T1  - fishFingers: Prioritization
of High-Resolution Mass
Spectrometry Features by Prediction of Bioconcentration Factors in
Fish Species
PY  - 2026
Y1  - 2026/10/24
DO  - 10.1021/acsesttox.6c00068
JO  - ACS ES&T Toxicology
JA  - ACS ES&T Toxicol.
SN  - 3070-863X
AB  - The number of novel entities produced around the world
exceeds
our capacity to evaluate the risk of each chemical under traditional
frameworks. Detecting, prioritizing, and identifying thousands of
chemicals present in the environment is a challenge to understanding
this planetary boundary. Machine learning has demonstrated quick and
accurate predictions for ecotoxicological end points for chemicals
with structural fingerprints derived from high-resolution mass spectrometry
(HRMS). This study provides a new nontargeted screening strategy to
prioritize HRMS features by the prediction of bioconcentration factors
(BCF) without a priori structural annotation. An
XGBoost model was trained on a database with structural fingerprints
from 1285 unique substances and log10 BCF measurements
from 111 species of fish. Validation was performed using structural
fingerprints derived from high-quality tandem mass spectrometry (MS2),
with RMSE = 1.02, MAE = 0.81, and ρ = 0.50, demonstrating its
capability in predicting log10 BCF and to rank HRMS
features by predicted bioconcentration from a wide chemical space.
Variations in BCF were observed in species such as common carp (Cyprinus carpio), which exhibits lower average BCF compared
with zebrafish (Danio rerio). Halogenation, aliphatic
and aromatic carbon, and heteroatomaticity were among the most important
structural fingerprints for BCF. The model and package are available
from https://github.com/drewszabo/fishFingers.
Y2  - 10/26/2026
UR  - https://doi.org/10.1021/acsesttox.6c00068
ER  -