Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity
Journal article, 2022

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.

Toxicity

Environmental modeling

Molecular modeling

Extraction

Molecules

Author

Gulnara Shavalieva

Chalmers, Space, Earth and Environment, Energy Technology

Stavros Papadokonstantakis

Chalmers, Space, Earth and Environment, Energy Technology

Gregory Peters

Chalmers, Technology Management and Economics, Environmental Systems Analysis

Journal of Chemical Information and Modeling

1549-9596 (ISSN) 1549960x (eISSN)

Vol. 62 17 4018-4031

Subject Categories

Biophysics

Medicinal Chemistry

Cancer and Oncology

DOI

10.1021/acs.jcim.1c01079

More information

Latest update

7/27/2023