Electron density-based GPT for optimization and suggestion of host-guest binders

Juan M Parrilla-Gutiérrez, Jarosław M. Granda, Jean-François Ayme, Michał D. Bajczyk, Liam Wilbraham, Leroy Cronin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

Here we present a machine learning model trained on electron density for the production of host-guest binders. These are read out as simplified molecular-input line-entry system (SMILES) format with >98% accuracy, enabling a complete characterization of the molecules in two dimensions. Our model generates three-dimensional representations of the electron density and electrostatic potentials of host-guest systems using a variational autoencoder, and then utilizes these representations to optimize the generation of guests via gradient descent. Finally the guests are converted to SMILES using a transformer. The successful practical application of our model to established molecular host systems, cucurbit[n]uril and metal-organic cages, resulted in the discovery of 9 previously validated guests for CB[6] and 7 unreported guests (with association constant K a ranging from 13.5 M -1 to 5,470 M -1) and the discovery of 4 unreported guests for [Pd 21 4] 4+ (with K a ranging from 44 M -1 to 529 M -1).

Original languageEnglish
Pages (from-to)200-209
Number of pages10
JournalNature computational science
Volume4
Early online date8 Mar 2024
DOIs
Publication statusPublished - Mar 2024

Keywords

  • cheminformatics
  • computational science
  • supramolecular chemistry

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