Literature DB >> 11328715

The use of Hasse diagrams as a potential approach for inverse QSAR.

R Brüggemann1, S Pudenz, L Carlsen, P B Sørensen, M Thomsen, R K Mishra.   

Abstract

Quantitative structure-activity relationships are often based on standard multidimensional statistical analyses and sophisticated local and global molecular descriptors. Here, the aim is to develop a tool helpful to define a molecule or a class of molecules which fulfills pre-described properties, i.e., an Inverse QSAR approach. If highly sophisticated descriptors are used in QSAR, the structure and then the synthesis recipe may be hard to derive. Thus, descriptors, from which the synthesis recipe can be easily derived, seem appropriate to be included within this study. However, if descriptors simple enough to be useful for defining syntheses recipes of chemicals were used, the accuracy of a numeric expression may fail. This paper suggests a method, based on very simple elements of the theory of partially ordered sets, to find a qualitative basis for the relationship between such fairly simple descriptors on the one side and a series of ecotoxicological properties, on the other side. The partial order ranking method assumes neither linearity nor certain statistical distribution properties. Therefore the method may be more general compared to many standard statistical techniques. A series of chlorinated aliphatic compounds has been used as an illustrative example and a comparison with more sophisticated descriptors derived from quantum chemistry and graph theory is given. Among the results, it was disclosed that only for algae lethal concentration, as one of the four ecotoxicological properties, the synthesis specific predictors seem to be good estimators. For all other ecotoxicological properties quantum chemical descriptors appear as the more suitable estimators.

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Year:  2001        PMID: 11328715     DOI: 10.1080/10629360108035364

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  4 in total

1.  QSAR of ecotoxicological data on the basis of data-driven if-then-rules.

Authors:  Stefan Pudenz; Rainer Brüggemann; Hans-Georg Bartel
Journal:  Ecotoxicology       Date:  2002-10       Impact factor: 2.823

2.  Giving molecules an identity. On the interplay between QSARs and partial order ranking.

Authors:  Lars Carlsen
Journal:  Molecules       Date:  2004-12-31       Impact factor: 4.411

3.  Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quantitative super-structure/activity relationships (QSSAR).

Authors:  Teodora Ivanciuc; Ovidiu Ivanciuc; Douglas J Klein
Journal:  Mol Divers       Date:  2006-05-19       Impact factor: 2.943

4.  Deep reinforcement learning for de novo drug design.

Authors:  Mariya Popova; Olexandr Isayev; Alexander Tropsha
Journal:  Sci Adv       Date:  2018-07-25       Impact factor: 14.136

  4 in total

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