Graph Attention Network Model with Defined Applicability Domains for Screening PBT Chemicals

In
silico models for screening environmentally persistent, bio-accumulative,
and toxic (PBT) substances are necessary for sound management of chemicals.
Due to the complex structure–activity landscapes (SALs) on
the PBT attributes, previous models for screening PBT chemicals lack
either applicability domain (AD) characterizations or interpretability,
restricting their applications. Herein, graph attention networks (GATs),
a novel neural network architecture, were introduced to construct
models for screening PBT chemicals. Results show that the GAT model
not only outperformed those in previous studies but also exhibited
interpretability since it optimizes attention weight parameters (PAW) that indicate contributions of each atom
to the PBT attributes. An AD characterization termed ADFP–AC, which considers both molecular fingerprint (FP) similarities and
compounds at activity cliffs (ACs) of SALs, was proposed to describe
the ADs, which further assured the performance of the GAT model. Eight
previously unidentified classes of compounds were identified as PBT
chemicals from the Inventory of Existing Chemical Substances in China.
The GAT model together with the ADFP–AC characterization
may serve as efficient tools for screening PBT chemicals, and the
modeling methodology can be applied to other physicochemical, environmental,
behavioral, and toxicological parameters of chemicals that are necessary
for their risk assessment and management.