Transfer Learning with a Graph Attention Network and Weighted Loss Function for Screening of Persistent, Bioaccumulative, Mobile, and Toxic Chemicals

In silico methods for screening hazardous
chemicals
are necessary for sound management. Persistent, bioaccumulative, mobile,
and toxic (PBMT) chemicals persist in the environment and have high
mobility in aquatic environments, posing risks to human and ecological
health. However, lack of experimental data for the vast number of
chemicals hinders identification of PBMT chemicals. Through an extensive
search of measured chemical mobility data, as well as persistent,
bioaccumulative, and toxic (PBT) chemical inventories, this study
constructed comprehensive data sets on PBMT chemicals. To address
the limited volume of the PBMT chemical data set, a transfer learning
(TL) framework based on graph attention network (GAT) architecture
was developed to construct models for screening PBMT chemicals, designating
the PBT chemical inventories as source domains and the PBMT chemical
data set as target domains. A weighted loss (LW) function was proposed and proved to mitigate the negative
impact of imbalanced data on the model performance. Results indicate
the TL-GAT models outperformed GAT models, along with large coverage
of applicability domains and interpretability. The constructed models
were employed to identify PBMT chemicals from inventories consisting
of about 1 × 106 chemicals. The developed TL-GAT framework
with the LW function holds broad applicability
across diverse tasks, especially those involving small and imbalanced
data sets.