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Combining Generative Artificial Intelligence and On-Chip Synthesis for De Novo Drug Design

submitted on 29.12.2020, 11:30 and posted on 30.12.2020, 06:51 by Francesca Grisoni, Berend Huisman, Alexander Button, Michael Moret, Kenneth Atz, Daniel Merk, Gisbert Schneider

Automation of the molecular design-make-test-analyze cycle speeds up the identification of hit and lead compounds for drug discovery. Using deep learning for computational molecular design and a customized microfluidics platform for on-chip compound synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space defined by known LXRα agonists, and to suggest structural analogs of known ligands and novel molecular cores. To further the design of lead-like molecules and ensure compatibility with automated on-chip synthesis, this chemical space was confined to the set of virtual products obtainable from 17 different one-step reactions. Overall, 25 de novo generated compounds were successfully synthesized in flow via formation of sulfonamide, amide bond, and ester bond. First-pass in vitro activity screening of the crude reaction products in hybrid Gal4 reporter gene assays revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch re-synthesis, purification, and re-testing of 14 of these compounds confirmed that 12 of them were potent LXRα or LXRβ agonists. These results support the utilization of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.


Swiss National Science Foundation (grant no. 205321_182176 to G.S.)

ETH RETHINK initiative


Email Address of Submitting Author


ETH Zurich



ORCID For Submitting Author

Declaration of Conflict of Interest

G.S. declares a potential financial conflict of interest as a co-founder of LLC, Zurich, and in his role as a consultant to the pharmaceutical industry. All other authors declare no conflict of interest.

Version Notes

Version 1.