Integrated Transfer Learning and Multitask Learning Strategies to Construct Graph Neural Network Models for Predicting Bioaccumulation Parameters of Chemicals

Accurate prediction of parameters
related to the environmental
exposure of chemicals is crucial for the sound management of chemicals.
However, the lack of large data sets for training models may result
in poor prediction accuracy and robustness. Herein, integrated transfer
learning (TL) and multitask learning (MTL) was proposed for constructing
a graph neural network (GNN) model (abbreviated as TL-MTL-GNN model)
using n-octanol/water partition coefficients as a
source domain. The TL-MTL-GNN model was trained to predict three bioaccumulation
parameters based on enlarged data sets that cover 2496 compounds with
at least one bioaccumulation parameter. Results show that the TL-MTL-GNN
model outperformed single-task GNN models with and without the TL,
as well as conventional machine learning models trained with molecular
descriptors or fingerprints. Applicability domains were characterized
by a state-of-the-art structure–activity landscape-based (abbreviated
as ADSAL) methodology. The TL-MTL-GNN model coupled with
the optimal ADSAL was employed to predict bioaccumulation
parameters for around 60,000 chemicals, with more than 13,000 compounds
identified as bioaccumulative chemicals. The high predictive accuracy
and robustness of the TL-MTL-GNN model demonstrate the feasibility
of integrating the TL and MTL strategy in modeling small-sized data
sets. The strategy holds significant potential for addressing small
data challenges in modeling environmental chemicals.