Editing Quantitative structure–activity relationship (section)

=== Fragment based (group contribution) ===

Analogously, the "[[partition coefficient]]"—a measurement of differential solubility and itself a component of QSAR predictions—can be predicted either by atomic methods (known as "XLogP" or "ALogP") or by [[group contribution method|chemical fragment methods]] (known as "CLogP" and other variations). It has been shown that the [[partition coefficient|logP]] of compound can be determined by the sum of its fragments; fragment-based methods are generally accepted as better predictors than atomic-based methods.<ref name="pmid17597897">{{cite journal | vauthors = Thompson SJ, Hattotuwagama CK, Holliday JD, Flower DR | title = On the hydrophobicity of peptides: Comparing empirical predictions of peptide log P values | journal = Bioinformation | volume = 1 | issue = 7 | pages = 237–41 | year = 2006 | pmid = 17597897 | pmc = 1891704 | doi = 10.6026/97320630001237 }}</ref> Fragmentary values have been determined statistically, based on empirical data for known logP values. This method gives mixed results and is generally not trusted to have accuracy of more than ±0.1 units.<ref>{{Cite journal | title = Prediction of physicochemical parameters by atomic contributions |vauthors=Wildman SA, Crippen GM | doi = 10.1021/ci990307l | year = 1999 | journal = J. Chem. Inf. Comput. Sci. | pages = 868–873 | volume = 39 | issue = 5 }}</ref>

Group or fragment-based QSAR is also known as GQSAR.<ref name="Ajmani_2008"/> GQSAR allows flexibility to study various molecular fragments of interest in relation to the variation in biological response. The molecular fragments could be substituents at various substitution sites in congeneric set of molecules or could be on the basis of pre-defined chemical rules in case of non-congeneric sets.  GQSAR also considers cross-terms fragment descriptors, which could be helpful in identification of key fragment interactions in determining variation of activity.<ref name="Ajmani_2008">{{citation | vauthors = Ajmani S, Jadhav K, Kulkarni SA | title = Group-Based QSAR (G-QSAR)}}</ref>
Lead discovery using fragnomics is an emerging paradigm. In this context FB-QSAR proves to be a promising strategy for fragment library design and in fragment-to-lead identification endeavours.<ref>{{cite journal | vauthors = Manoharan P, Vijayan RS, Ghoshal N | title = Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies | journal = Journal of Computer-Aided Molecular Design | volume = 24 | issue = 10 | pages = 843–64 | date = Oct 2010 | pmid = 20740315 | doi = 10.1007/s10822-010-9378-9 | bibcode = 2010JCAMD..24..843M | s2cid = 1171860 }}</ref>

An advanced approach on fragment or group-based QSAR based on the concept of pharmacophore-similarity is developed.<ref name ="Kumar_2013"/> This method, pharmacophore-similarity-based QSAR (PS-QSAR) uses topological pharmacophoric descriptors to develop QSAR models. This activity prediction may assist the contribution of certain pharmacophore features encoded by respective fragments toward activity improvement and/or detrimental effects.<ref name="Kumar_2013">{{cite journal | vauthors = Prasanth Kumar S, Jasrai YT, Pandya HA, Rawal RM | title = Pharmacophore-similarity-based QSAR (PS-QSAR) for group-specific biological activity predictions | journal = Journal of Biomolecular Structure & Dynamics | volume = 33 | issue = 1 | pages = 56–69 | date = November 2013 | pmid = 24266725 | doi = 10.1080/07391102.2013.849618 | s2cid = 45364247 | url = https://figshare.com/articles/dataset/Pharmacophore_similarity_based_QSAR_PS_QSAR_for_group_specific_biological_activity_predictions/861021 | url-access = subscription }}</ref>