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GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks
In this study, we introduce a new robust architecture, Generative Examination Networks GEN, based on bidirectional RNNs with concatenated sub-models to learn and generate molecular SMILES with a trained target space.
In silico generation of novel, drug-like chemical matter using the LSTM neural network
In this article we present a method to generate molecules using a long short-term memory (LSTM) neural network and provide an analysis of the results, including a virtual screening test.
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