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

By D. Merk, Francesca Grisoni, Lukas Friedrich, Gisbert Schneider. 2018

117 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Pharmacology, Toxicology

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Citations in the corpus

Geometric deep learning on molecular representations (2021, unknown): “In this context, RNNs have shown remarkable capability to learn the SMILES syntax [19, 128], and capture high-level molecular features ("semantics"), such as physicochemical [19, 128] and biological properties [45, 126, 129, 132].”
Rethinking drug design in the artificial intelligence era (2019, background): “Nevertheless, pioneering prospective applications have shown that generative de novo design produces synthetically accessible molecules with desired properties and activities 75,76 .”
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models (2018, background): “Recent works demonstrated that machine learning methods can produce new small molecules (Merk et al., 2018a; Merk et al., 2018b; Polykovskiy et al., 2018b; Zhavoronkov et al., 2019a) and peptides (Grisoni et al.”