Leveraging bioactivity data for molecule selection. The availability of bioactivity data for the finetuning molecules permitted the training of a bioactivity prediction model to select the most promising de novo designs . Classical chemoinformatics methods often rely on precomputed features (molecular descriptors), combined with a machine learning algorithm for molecular property prediction. In this study, we aimed to explore the potential of a SMILES string-based hybrid CLM to predict the bioactivity. This neural network model combines a generative CLM with a classifier network. Given that (i) inactive molecules were annotated with PI3Kγ pIC50 = 4.0 (Fig. ) and (ii) there is a natural ordering of the PI3Kγ ligands according to their pIC50 values, the bioactivity prediction task was framed as an ordinal classification task, i.e., classification with a class order . Such a model considers both the active and inactive compounds for training and preserves both the class labels and the class order. For model training, we defined three class labels: "inactive" (pIC50 ≤ 4.0, 34 molecules), "moderately active" (4.0 < pIC50 ≤ 6.5, 121 molecules), and "highly active" (pIC50 > 6.5, 43 molecules). The CLM generated a focused virtual chemical library by leveraging the structural information of the molecules used for fine-tuning, while the classifier layer factored their activity labels into the model (Fig. ).