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Table 3 Metrics obtained from a 50,000 SMILES sample of all the models trained

From: A de novo molecular generation method using latent vector based generative adversarial network

DatasetArch.Valid (%)Unique (%)Novel (%)Active (%)Recovered actives/total actives (%)Recovered neighbors
EGFRGAN865697715.26196
RNN964695657.74238
HTR1AGAN866695715.05284
RNN965090817.28384
S1PR1GAN893198440.9324
RNN973597653.7243
  1. Dataset used (Dataset), Architecture used (Arch.), Percent of valid molecules in the sampled set (Valid), Percent of valid unique compounds (Unique), Percent of unique novel (not present in the training set) compounds (Novel), Percent of unique active compounds (Active), Recovered actives from the test set given the entire number of actives in the test set (Recovered actives/Total Actives), Recovered neighbors of active compounds using FCFP6 fingerprint with 2048 bits and a threshold Tanimoto similarity of 0.7