|
Hokkaido University Collection of Scholarly and Academic Papers >
Institute for Chemical Reaction Design and Discovery : ICReDD >
Peer-reviewed Journal Articles, etc >
Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions
This item is licensed under:Creative Commons Attribution 4.0 International
| Title: | Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions |
| Authors: | Rakhimbekova, Assima Browse this author | | Madzhidov, Timur I. Browse this author | | Nugmanov, Ramil I. Browse this author | | Gimadiev, Timur R. Browse this author | | Baskin, Igor I. Browse this author | | Varnek, Alexandre Browse this author |
| Keywords: | applicability domain | | Quantitative Reaction-Property Relationship | | QSAR | | QSPR | | chemical reactions | | chemoinformatics | | machine learning | | reaction mining |
| Issue Date: | Aug-2020 |
| Publisher: | MDPI |
| Journal Title: | International Journal of Molecular Sciences |
| Volume: | 21 |
| Issue: | 15 |
| Start Page: | 5542 |
| Publisher DOI: | 10.3390/ijms21155542 |
| Abstract: | Nowadays, the problem of the model's applicability domain (AD) definition is an active research topic in chemoinformatics. Although many various AD definitions for the models predicting properties of molecules (Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) models) were described in the literature, no one for chemical reactions (Quantitative Reaction-Property Relationships (QRPR)) has been reported to date. The point is that a chemical reaction is a much more complex object than an individual molecule, and its yield, thermodynamic and kinetic characteristics depend not only on the structures of reactants and products but also on experimental conditions. The QRPR models' performance largely depends on the way that chemical transformation is encoded. In this study, various AD definition methods extensively used in QSAR/QSPR studies of individual molecules, as well as several novel approaches suggested in this work for reactions, were benchmarked on several reaction datasets. The ability to exclude wrong reaction types, increase coverage, improve the model performance and detect Y-outliers were tested. As a result, several "best" AD definitions for the QRPR models predicting reaction characteristics have been revealed and tested on a previously published external dataset with a clear AD definition problem. |
| Rights: | https://creativecommons.org/licenses/by/4.0/ |
| Type: | article |
| URI: | http://hdl.handle.net/2115/79296 |
| Appears in Collections: | 化学反応創成研究拠点：ICReDD (Institute for Chemical Reaction Design and Discovery : ICReDD) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
|
|
