Individual machine learning models were analyzed using an initial descriptor set of AlogPS and OEstate. Internal five-fold cross-validation results and external test QWK scores from models available in openOCHEM are shown in table . Post hoc analysis showed that descriptor-based models included some of the best performing models in both leaderboard and private QWK scores. An exception was ASNN, which contributed the highest score for the Leaderboard dataset but had a lower score for the private set. Overall the RMSE correlated with the QWK scores. Based on Table data we calculated R=-0.38 and R=-0.46 for leaderboard and private scores, respectively. Thus, indeed, RMSE could be a proxy to identify models with high QWK score in the absence of a direct loss function for the latter one. The final model design Experience from previous competitions in the ToxCast toxicity prediction challenge and Tox21 challenge introduced the idea of consensus modeling as a winning strategy. Descriptor-based machine learning methods with the two smallest RMSE scores, i.e. DNN and CatBoost, were combined into a consensus model. Selecting these two principally different descriptor-based machine learning approaches, neural networks and decision trees, we hoped to eliminate variances and biases of each specific approach.