The automated molecular design pipeline was composed of three modules (Figure ): Module 1: A generative deep learning model based on a recurrent neural network with Long-Short Term Memory (LSTM) cells. LSTM models were employed for the design of new molecules represented as Simplified Molecular Input Line Entry Systems (SMILES) strings. This LSTM-based 'chemical language model' served as the de novo structure generator (Figure ). Module 2: A virtual reaction filter that captured 17 one-step reactions that were compatible with the microfluidics system (module 3). These reactions were encoded as SMILES ARbitrary Target Specification (SMARTS) strings. This filtering module selected those generated molecules that were synthetically compatible within the microfluidics platform (Figure ). Module 3: A microfluidics platform designed to minimize the amount of manual labor needed to optimize reaction conditions and synthesize small compound libraries. This compact bench-top system combined the automated retrieval of required reagents with the optimization of reaction conditions, online reaction product monitoring by highperformance liquid chromatography-mass spectrometry (HPLC-MS), and the collection of the reaction mixtures (Figure ). based on a long-short term memory network was used to generate putative liver X receptor-α agonists. The two-step network training procedure first trained the model on 656,689 compounds predicted as compatible with the microfluidics system, and then fine-tuned this pre-trained model with 41 known LXRα agonists. (b) The de novo generated molecules were filtered on the basis of their predicted synthesis route, using a set of 17 reactions specified in the SMARTS notation ('virtual reaction filter'). Molecular building blocks for synthesis were automatically retrieved from a commercial supplier catalog. (c) A total of 41 de novo designs were retained for synthesis on a microfluidics platform, which contained two syringes for the handling of reagents R1 and R2, a Cetoni Qmix element equipped with a Dean Flow microfluidic reactor chip, and a Rheodyne MRA splitter for automated sample transfer to an HPLC-MS system.