Early assessment of the potential impact of chemicals
on health
and the environment requires toxicological properties of the molecules.
Predictive modeling is often used to estimate the property values
in silico
from pre-existing experimental data, which is
often scarce and uncertain. One of the ways to advance the predictive
modeling procedure might be the use of knowledge existing in the field.
Scientific publications contain a vast amount of knowledge. However,
the amount of manual work required to process the enormous volumes
of information gathered in scientific articles might hinder its utilization.
This work explores the opportunity of semiautomated knowledge extraction
from scientific papers and investigates a few potential ways of its
use for predictive modeling. The knowledge extraction and predictive
modeling are applied to the field of acute aquatic toxicity. Acute
aquatic toxicity is an important parameter of the safety assessment
of chemicals. The extensive amount of diverse information existing
in the field makes acute aquatic toxicity an attractive area for investigation
of knowledge use for predictive modeling. The work demonstrates that
the knowledge collection and classification procedure could be useful
in hybrid modeling studies concerning the model and predictor selection,
addressing data gaps, and evaluation of models’ performance.
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