| Title | Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors |
|---|---|
| ID_Doc | 59 |
| Authors | Mukherjee, A; Su, A; Rajan, K |
| Title | Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors |
| Year | 2021 |
| Published | Journal Of Chemical Information And Modeling, 61, 5 |
| Abstract | 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. |
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