Overall, MorganFP performed the best among all numerical descriptors. The PCA plots in Figure indicates that the blockers could be better discriminated from the non-blockers by MorganFP. The continuous distribution of compounds in the latent space explains the poor ability of simple classifiers such as RF and XGBoost to distinguish blockers from nonblockers. Previously, these representations have been shown to provide improvements over baseline models based on molecular fingerprints. It is also worth noting that these representations are not only useful in reconstruction of molecules but also in capturing properties of molecules that include biological activity. Generating compounds without hERG liabilities. The recently introduced sequence-tosequence based models that rely on the encoded representation (i.e. latent space) of molecules facilitates exploration of new chemical space. Apart from its novel applicability in QSAR modeling and virtual screening , the encoded representation has been explored to generate focused chemical libraries with molecular properties of interest. A number of key factors such as validity, novelty, diversity and synthetic feasibility of the sampled molecules have been addressed. Such models have been recently reported to identify promising drug candidates. In this context, the AAE architecture was used to sample new compounds using hERG blockers and non-blockers as separate starting points. Distribution of the prediction probabilities for the newly generated compounds (using the consensus model) revealed that most compounds generated around non-blockers were predicted as non-blockers by the consensus model (Figure ). Similarly, a majority of new structures sampled from the blockers were predicted as blockers (Figure and Figure ). The activities of new compounds were predicted using the consensus model.