Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors

This paper aims to identify structural
motifs within a molecule
that contribute the most toward a chemical being an endocrine disruptor.
We have developed a deep neural network-based toolkit toward this
aim. The trained model can virtually assess a synthetic chemical’s
potential to be an endocrine disruptor using machine-readable molecular
representation, simplified molecular input line entry system (SMILES).
Our proposed toolkit is a multilabel or multioutput classification
model that combines both convolution and long short-term memory (LSTM)
architectures. The toolkit leverages the advantages of an active learning-based
framework that combines multiple sources of data. Class activation
maps (CAMs) generated from the feature-extraction layers can identify
the structural alerts and the chemical environment that determines
the specificity of the structural alerts.