Target prediction and molecular descriptors. Target prediction was performed with the in-house SPiDER software, Model architecture and settings. The chemical language model was implemented in Python (v. 3.6.5) using Keras (, v2.2.0) with the TensorFlow GPU backend (www.tensorflow.org, v1.9.0) as a recurrent neural network with LSTM cells, 30 as previously published. The neural network used consisted of four layers, for a total of 5,820,515 Temperature and sampling epoch choice. Sampling temperature and fine-tuning epochs were automatically determined to optimize three parameters simultaneously: (i) predicted LXR activity by SPiDER, (ii) scaffold diversity, 37 and (iii) distance to the fine-tuning compounds (as encoded by CATS2 38 descriptors with Euclidean distance). We tested three sampling temperatures (T = 0.2, T = 0.7, T = 1.2, Supporting Figure ) and fine-tuning epochs 1 to 40 (Supporting Figure ). Sampling temperature T = 0.70 and fine-tuning epochs in the range 15 -20 were chosen to generate the final designs, as they resulted in the best compromise between scaffold diversity, number of compounds predicted as LXR modulators (p < 0.05), and CATS2 similarity between the fine-tuning set and de novo design.