Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low-micromolar receptor modulatory activity in cell-based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry.
Bibliographical noteFunding Information:
We thank P. Schneider for compiling the subsets of the ChEMBL database and A. T. Müller for technical support. This research was financially supported by the Swiss National Science Foundation (grant no. IZSEZ0_177477). D. M. was supported by an ETH Zurich Postdoctoral Fellowship (grant no. 16-2 FEL-07).
© 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.
- drug discovery
- machine learning
- medicinal chemistry
- nuclear receptor
- Small Molecule Libraries/chemical synthesis
- Retinoid X Receptors/agonists
- Deep Learning
- Peroxisome Proliferator-Activated Receptors/agonists
- Drug Design
- HEK293 Cells
- Molecular Docking Simulation
- Quantitative Structure-Activity Relationship