Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points

In silico models
for predicting physicochemical
properties and environmental fate parameters are necessary for the
sound management of chemicals. This study employed graph attention
network (GAT) algorithms to construct such models on 15 end points.
The results showed that the GAT models outperformed the previous state-of-the-art
models, and their performance was not influenced by the presence or
absence of compounds with certain structures. Molecular similarity
density (ρs) was found to be a key metrics characterizing
data set modelability, in addition to the proportion of compounds
at activity cliffs. By introducing molecular graph (MG) contrastive
learning, MG-based ρs and molecular inconsistency
in activities (IA) were calculated and
employed for characterizing the structure–activity landscape
(SAL)-based applicability domain ADSAL{ρs, IA}. The GAT models coupled with ADSAL{ρs, IA} significantly
improved the prediction coefficient of determination (R2) on all the end points by an average of 14.4% and enabled
all the end points to have R2 > 0.9,
which
could hardly be achieved previously. The models were employed to screen
persistent, mobile, and/or bioaccumulative chemicals from inventories
consisting of about 106 chemicals. Given the current state-of-the-art
model performance and coverage of the various environmental end points,
the constructed models with ADSAL{ρs, IA} may serve as benchmarks for future efforts
to improve modeling efficacy.