BROWSE

Related Scientist

Grzybowski, Bartosz Andrzej's photo.

Grzybowski, Bartosz Andrzej
첨단연성물질 연구단
more info

ITEM VIEW & DOWNLOAD

Computational prediction of complex cationic rearrangement outcomes

Cited 0 time in webofscience Cited 0 time in scopus
39 Viewed 0 Downloaded
Title
Computational prediction of complex cationic rearrangement outcomes
Author(s)
Klucznik, Tomasz; Syntrivanis, Leonidas-Dimitrios; Baś, Sebastian; Mikulak-Klucznik, Barbara; Moskal, Martyna; Szymkuć, Sara; Mlynarski, Jacek; Gadina, Louis; Beker, Wiktor; Burke, Martin D.; Tiefenbacher, Konrad; Bartosz A. Grzybowski
Publication Date
2024-01
Journal
Nature, v.625, pp.508 - 515
Publisher
Nature Research
Abstract
Recent years have seen revived interest in computer-assisted organic synthesis 1,2. The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field 1,3–7, including examples leading to advanced natural products 6,7. Such methods typically operate on full, literature-derived ‘substrate(s)-to-product’ reaction rules and cannot be easily extended to the analysis of reaction mechanisms. Here we show that computers equipped with a comprehensive knowledge-base of mechanistic steps augmented by physical-organic chemistry rules, as well as quantum mechanical and kinetic calculations, can use a reaction-network approach to analyse the mechanisms of some of the most complex organic transformations: namely, cationic rearrangements. Such rearrangements are a cornerstone of organic chemistry textbooks and entail notable changes in the molecule’s carbon skeleton 8–12. The algorithm we describe and deploy at https://HopCat.allchemy.net/ generates, within minutes, networks of possible mechanistic steps, traces plausible step sequences and calculates expected product distributions. We validate this algorithm by three sets of experiments whose analysis would probably prove challenging even to highly trained chemists: (1) predicting the outcomes of tail-to-head terpene (THT) cyclizations in which substantially different outcomes are encoded in modular precursors differing in minute structural details; (2) comparing the outcome of THT cyclizations in solution or in a supramolecular capsule; and (3) analysing complex reaction mixtures. Our results support a vision in which computers no longer just manipulate known reaction types 1–7 but will help rationalize and discover new, mechanistically complex transformations.
URI
https://pr.ibs.re.kr/handle/8788114/14716
DOI
10.1038/s41586-023-06854-3
ISSN
0028-0836
Appears in Collections:
Center for Soft and Living Matter(첨단연성물질 연구단) > 1. Journal Papers (저널논문)
Files in This Item:
There are no files associated with this item.

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse