Sieci Bayesa w kontekście przewidywania toksyczności związków chemicznych
Ładowanie...
Data
2022
Autorzy
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
Bayesian Belief Networks (BBN) are a graph structure for representing the
relationships between descriptors and a target variable in a given data set. This
study investigated the use of this algorithm in modeling the Quantitative Structure-
Activity Relationship (QSAR) of chemical compounds using toxicity
assessment as an example. Selected tests from a database containing toxicity
information of 12,000 compounds for 12 different toxicity tests were used.
The influence of parameters describing the representation of the compound
was checked. In addition, toxicity prediction models were created for selected
tests and a model predicting the outcome of several tests simultaneously
(multi-target). The results indicate that the obtained models have relatively
good accuracy and precision. Using multi-target models based on the BBN
algorithm can lead to better predictions than models for predicting a single
target variable.
Opis
Słowa kluczowe
quantitative structure – activity relationship (QSAR), Bayesian Belief Network (BBN), toxicity, green chemistry, struktura ilościowa – związek aktywności (QSAR), toksyczność, zielona chemia
Cytowanie
Baran K., Sieci Bayesa w kontekście przewidywania toksyczności związków chemicznych. W: Zarządzanie i innowacje u progu XXI wieku, Malinowski D. (Red.),
Sośnicka J. (Red.)., Seria: Monografie PŁ; Nr 2414, Wydawnictwo Politechniki Łódzkiej, Łódź 2022, s. 181-189, ISBN 978-83-66741-66-9, DOI:
10.34658/9788366741669.3.3.