The authors report that adding a classifier-based range-loss to a WGAN makes it generate molecules labeled as odorants in 99.2% of trials, but the labeler is both the training signal and the evaluation metric.
”Automatic chemical design using a data-driven continuous representation of molecules.” ACS central science 4.2 (2018): 268-276
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Improved Molecular Generation through Attribute-Driven Integrative Embeddings and GAN Selectivity
The authors report that adding a classifier-based range-loss to a WGAN makes it generate molecules labeled as odorants in 99.2% of trials, but the labeler is both the training signal and the evaluation metric.