Editing Partition coefficient (section)

===Knowledge-based===
A typical [[data-mining]]-based prediction uses [[support-vector machine]]s,<ref name="pmid17031534">{{cite journal | vauthors = Liao Q, Yao J, Yuan S | title = SVM approach for predicting LogP | journal = Molecular Diversity | volume = 10 | issue = 3 | pages = 301–9 | date = August 2006 | pmid = 17031534 | doi = 10.1007/s11030-006-9036-2 | s2cid = 1196330 }}</ref> [[Decision tree learning|decision trees]], or [[Artificial neural network|neural networks]].<ref name="pmid15012980">{{cite journal | vauthors = Molnár L, Keseru GM, Papp A, Gulyás Z, Darvas F | title = A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors | journal = Bioorganic & Medicinal Chemistry Letters | volume = 14 | issue = 4 | pages = 851–3 | date = February 2004 | pmid = 15012980 | doi = 10.1016/j.bmcl.2003.12.024 }}</ref> This method is usually very successful for calculating log ''P'' values when used with compounds that have similar chemical structures and known log ''P'' values. [[Molecule mining]] approaches apply a similarity-matrix-based prediction or an automatic fragmentation scheme into molecular substructures. Furthermore, there exist also approaches using [[Maximum common subgraph isomorphism problem|maximum common subgraph]] searches or [[Molecule mining|molecule kernels]].