The hybrid CLM network contained the weights of the pretrained E-CLM plus an additional feedforward layer with three sigmoidal neurons. The model was trained to solve an ordinal classification task, where each of the three output neurons corresponded to one class. k-Means clustering (k = 5, Scikit-learn; ) was performed to group the fine-tuning molecules according to their similarity based on Morgan fingerprints. Four groups were used for cross-validation and one for classifier testing. The output threshold values, the number of transfer learning epochs, and the oversampling values of the less represented classes were defined by cross-validation. The best settings were selected on the basis of the performance on the test set, which was used once (oversampling: +40 molecules for the two less represented classes, sigmoid threshold: 0.4, number of transfer learning epochs: 200). Each of the 100 CLM models of the final ensemble was trained with the best settings on all available data. The neural network architecture was composed of six layers with a total of 5,646,982 parameters (layer 1: BatchNormalization, layer 2: 1024 LSTM cells, layer 3: 256 LSTM cells, layer 4: BatchNormalization, layer 5: Dropout, and layer 6: Dense, with three units, each with a sigmoid activation function) and was trained with SMILES encoded as one-hot vectors. The models were trained with the Adam optimizer and the binary cross-entropy loss function (learning rate = 10 -4 , 200 epochs).