The deep learning model (Figure ) was pre-trained on the SMILES strings of 656,689 commercially available molecules from four compound vendors (for details, see Materials and Methods), which were predicted by the virtual reaction system to be suitable for the on-chip synthesis within our microfluidics platform. This pre-training step enabled the model to capture the syntax of the SMILES strings. After pre-training, 83 ± 2% of the generated SMILES strings were unique and chemically valid (average of three repetitions, 3000 SMILES strings sampled each time), and corresponded to novel molecules that were not included in the pre-training set. Compared to model pre-training performed in previous studies using bioactive molecules from the ChEMBL database, the approach used here resulted in a significantly higher proportion of de novo molecules that were considered synthesizable in flow (p < 0.001, Kruskal-Wallis test, Figure ). This result highlights the capability of the deep learning model to implicitly learn the desired molecular features (here: compound synthesizability as defined by the virtual reaction filter), without the need for explicit, rule-based design constraints. (c) Analysis of 67 de novo designs retained for potential synthesis: 14 compounds were patented LXR modulators annotated in SureChEMBL or Reaxys (22%); 15 compounds existed in PubChem, of which 10 compounds are annotated as commercially available (15% of the total); 4 (6%) compounds lack vendor information; and 2 compounds (3%) were known LXR modulators annotated in ChEMBL27 (IC/EC50 ≤ 2 µM). 37 compounds (55%) were not found in either PubChem, ChEMBL27, SciFinder, SureChEMBL, or Reaxys databases.