Although synthesizability of the generated structures is a bottleneck for generative models, it was recently demonstrated that the fraction of synthesizable molecules is comparable to that of training set used to derive the new compounds. Since our training set compounds originate from ChEMBL database that reports bioactivities for already synthesized compounds and in-house high-throughput assay, it is our expectation that the newly generated compounds have similar rate of synthesizability. Furthermore, the generated chemical structures are fairly diverse and not completely similar to the original training set subsets used for sampling (Figure ; supporting information, S8). These findings emphasize the potential of generative models in designing new chemical libraries with desired properties (or poor toxic liabilities), particularly in combination with predictive models. CONCLUSIONS. Modeling hERG channel inhibition has been important ever since the recall of marketed drugs due to fatal cardiac arrhythmias. To date, several computational modeling approaches have been proposed for early assessment of hERG liability and several in silico models have been reported in the recent years. In this study, both classical and modern learning approaches were evaluated and compared for their ability to predict hERG liabilities of small molecules. Both feed-forward neural networks (DNN models) and recurrent neural networks (LSTM models) performed on par with classical machine learning methods. It was also demonstrated that novel representations derived from the latent space of chemical autoencoders offer an alternative to traditional descriptors in structure-activity and structure-property modeling. Particularly, the DNNs provided a significantly better performance using these novel descriptors. Further, the utility of generative models to derive a new chemical space with a property of interest has been demonstrated. In addition, we also provide a high-quality reference dataset obtained by combining public domain hERG activity data with experimental data generated in a high-throughput thallium flux assay as well as hERG activity data for small molecules approved between 2012 and 2018. The validation data from this study can be used to evaluate hERG models proposed in future studies.