The prospective validation set containing 839 compounds was used to evaluate the models. A nearest-neighbor analysis with the training set revealed that a majority (>80%) of these compounds are below a Tanimoto similarity threshold of 0.6 (supporting information, S5). The optimal settings from cross-validation were retained for DNN and LSTM models. A performance trend similar to cross-validation was observed ( In particular, the latent descriptors derived from encoder-decoder architectures performed very well on validation set and emphasize their applicability in prediction of molecular properties and biological activity. However, classical ML methods such as XGBoost and RF are still in the league of best performing models, in agreement with previous studies. The original implementation of this VAE model, built on a subset of ZINC database, was used to generate latent descriptors of length 192 bits for both training and validation set compounds. DNNs were used to evaluate the utility of these descriptors for prediction of hERG channel blockade. Hyperparameter optimization was performed similar to other descriptors. This model did not perform as well as the DNN models based on our latent descriptors (Table ). This could be due to the fact that the latent space of the VAE model was originally shaped for predicting specific molecular properties such as the water-octanol partition coefficient. We also noticed that the reconstruction rate of the encoder-decoder model can influence the QSAR model performance. An inverse correlation was observed between the reconstruction rate of the encoder-decoder models and the improvement in performance of QSAR models using the latent descriptors. Considering this into account, we trained our AE and AAE models in a small number of epochs to limit the reconstruction rate and obtain optimal performance using the latent descriptors. However, a detailed investigation to arrive at the best reconstruction rate could not be performed due to the huge computational costs involved development of these models. DeepSMILES that could be used instead of the conventional SMILES representations in building generative neural networks. They tried to address the syntactical limitations of SMILES that could be a reason behind the poor validity of the newly generated structures. In another benchmark, canonical SMILES and DeepSMILES were compared to 'Randomized SMILES' for the development of generative RNN models. Randomized SMILES were earlier proposed as a data augmentation technique to improve the performance of QSAR models. Further, they were also shown to improve the relevance of latent descriptors for QSAR when used in generation of autoencoder models. In this study, we developed LSTM models using these two SMILES adaptations and compared the performance with our best LSTM model based on canonical SMILES. In the case of Randomized SMILES, different enumeration factors (e = 2, 3, 4, 5) were considered i.e. in case of e = 5, five unique randomized SMILES were generated for each molecule in the training set. In all cases, the LSTM model started to provide higher Sensitivity although the overall performance declined. Similarly, DeepSMILES did not perform as well as the canonical SMILES (see Table ). In order to evaluate DeepSMILES on a larger dataset, the AE model developed in this study was rebuilt using the same ChEMBL data but this time using DeepSMILES. Again, the AE model based on canonical SMILES resulted in a higher reconstruction performance and the latent descriptors derived from the same model provided better QSAR performance. . With more stringent activity criteria, the consensus model achieved a BACC of 0.79. Similar to the validation set, a majority (>80%) of these drugs were found to be below a Tanimoto similarity threshold of 0.6 (supporting information, S5). Thus, we demonstrated the ability of our models to provide robust predictions on unseen chemical space. At the same time, it is clear that the activity threshold used to separate blockers from non-blockers can result in a completely different dataset and model performance. While 10𝜇M is the generally accepted threshold, in the case of this dataset, we believe that 1𝜇M offers a realistic composition with more non-blockers than blockers. Furthermore, no clear trend was noticed in the evaluated time period (2012 to 2018) for the potential of newly approved drugs to inhibit hERG (see Figure ), while the expectation was to observe a decrease in the inhibitory potential over the time. This should draw the attention of the community to the question -is hERG blockade still a concern for drug discovery? and Latent2). The performance obtained using latent descriptors from AE and AAE models was comparable to that obtained using fingerprints and other descriptors only when employed with DNNs. The poor performance of RF and XGBoost models with latent descriptors could be attributed to the continuous distribution of the compounds in the low-dimensional space.