寄主(生物学)
电子
计算机科学
计算生物学
生物
物理
遗传学
核物理学
作者
Juan M. Parrilla-Gutiérrez,Jarosław M. Granda,Jean‐François Ayme,Michał D. Bajczyk,Liam Wilbraham,Leroy Cronin
标识
DOI:10.1038/s43588-024-00602-x
摘要
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 2 1 4 ] 4+ (with K a ranging from 44 M −1 to 529 M −1 ).
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