Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators

Instances of artificial intelligence equip medicinal chemistry with innovative tools for molecular design and lead discovery. Here we describe a deep recurrent neural network for de novo design of new chemical entities that are inspired by pharmacologically active natural products. Natural product characteristics are incorporated into a deep neural network that has been trained on synthetic low molecular weight compounds. This machine-learning model successfully generates readily synthesizable mimetics of the natural product templates. Synthesis and in vitro pharmacological characterization of four de novo designed mimetics of retinoid X receptor modulating natural products confirms isofunctional activity of two computer-generated molecules. These results positively advocate generative neural networks for natural-product-inspired drug discovery, reveal both opportunities and certain limitations of the current approach, and point to potential future developments. Artificial intelligence approaches to medicinal chemistry are increasingly powerful but struggle to predict bioactive molecules. Here a machine learning model generates synthetically accessible mimetics of natural products, which are shown to be bioactive against the retinoid X receptor.