Nucleosides are fundamental building blocks of DNA and RNA in all life forms and viruses. In addition, natural nucleosides and their analogs are critical in prebiotic chemistry, innate immunity, signaling, antiviral drug discovery and artificial synthesis of DNA / RNA sequences. Combined with the fact that quantitative structure activity relationships (QSAR) have been widely performed to understand their antiviral activity, nucleoside analogs could be used to benchmark generative molecular design. Here, we undertake the first generative design of nucleoside analogs using an approach that we refer to as the Conditional Randomized Transformer (CRT). We also benchmark our model against five previously published molecular generative models. We demonstrate that AI-generated molecules include nucleoside analogs that are of significance in a wide range of areas including prebiotic chemistry, antiviral drug discovery and synthesis of oligonucleotides. Our results show that CRT explores distinct molecular spaces and chemical transformations, some of which are similar to those undertaken by nature and medicinal chemists. Finally, we demonstrate the potential application of the CRT model in the generative design of molecules conditioned on Remdesivir and Molnupiravir as well as other nucleoside analogs with in vitro activity against SARS-CoV-2. One-Sentence Summary: Generative design of nucleoside analogs relevant to antiviral drug discovery, prebiotic chemistry and synthetic biology.
Generative AI Design and Exploration of Nucleoside Analogs
02 November 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.