| 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 |
| DOI |
10.1021/acs.jcim.0c01409 |
| 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. |
| Author Keywords |
|
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000656118800008 |
| WoS Category |
Chemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications |
| Research Area |
Pharmacology & Pharmacy; Chemistry; Computer Science |
| PDF |
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