- Title
- AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis
- Creator
- Isamura, Bienfait K, Lobb, Kevin A
- Subject
- To be catalogued
- Date
- 2022
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/453143
- Identifier
- vital:75226
- Identifier
- xlink:href="https://link.springer.com/article/10.1186/s13321-022-00618-3"
- Description
- Predicting transition state geometries is one of the most challenging tasks in computational chemistry, which often requires expert-based knowledge and permanent human intervention. This short communication reports technical details and preliminary results of a python-based tool (AMADAR) designed to generate any Diels–Alder (DA) transition state geometry (TS) and analyze determined IRC paths in a (quasi-)automated fashion, given the product SMILES. Two modules of the package are devoted to performing, from IRC paths, reaction force analyses (RFA) and atomic (fragment) decompositions of the reaction force F and reaction force constant κ. The performance of the protocol has been assessed using a dataset of 2000 DA cycloadducts retrieved from the ZINC database. The sequential location of the corresponding TSs was achieved with a success rate of 95%. RFA plots confrmed the reaction force constant κ to be a good indicator of the (non)synchronicity of the associated DA reactions. Moreover, the atomic decomposition of κ allows for the rationalization of the (a)synchronicity of each DA reaction in terms of contributions stemming from pairs of interacting atoms. The source code of the AMADAR tool is available on GitHub [CMCDD/AMADAR(github. com)] and can be used directly with minor customizations, mostly regarding the local working environment of the user.
- Format
- computer, online resource, application/pdf, 1 online resource (8 pages), pdf
- Publisher
- SpringerLink
- Language
- English
- Relation
- Journal of Cheminformatics, Isamura, B.K. and Lobb, K.A., 2022. AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis. Journal of Cheminformatics, 14(1), p.39, Journal of Cheminformatics volume 14 number 1 p. 39 2022 1758-2946
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
- Rights
- Open Access
- Hits: 6
- Visitors: 3
- Downloads: 0
Collections
RU Research Unit in Bioinformatics (RUBi)
Lobb, Kevin Allan (Assoc Prof)
RU Department of Chemistry
SDG 03. Good Health and Well-Being
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | AMADAR.pdf | 1 MB | Adobe Acrobat PDF | View Details |