Chemical language model. A CLM based on a long short-term memory (LSTM) model and SMILES strings as input was developed for the de novo generation of a focused virtual chemical library for PI3Kγ 28 . To learn from unlabeled data, CLMs leverage "self-supervised" learning . Specifically, the CLM was trained with an autoregressive approach, i.e., the process of iteratively predicting the next character in a SMILES string given all the previous characters in the string (Fig. ) . In previous studies, CLMs were pretrained on molecules with known biological activity (IC50, EC50, Kd, and Ki) <1 µM retrieved from the ChEMBL database . Although the training set can capture the general features of bioactive compounds, it does not necessarily represent the physicochemical properties of approved drugs. Here, to enable the CLM to capture features more related to approved drugs, we used 839,674 molecules from the US patent database for the CLM pretraining . We hypothesized that patented compounds are more likely to become marketed drugs than the molecules deposited in ChEMBL. Transfer learning was performed to properly focus the pretrained CLM toward the target space of PI3Kγ ligands. For transfer learning, 46 PI3Kγ inhibitors with IC50 ≤100 nM were selected from the Drug Target Commons (DTC) database .