## 1 Predictive Methods for Physicochemical Properties of Molecules
### Current Methods
The observation though is that accuracy and reliability are often still too limited, something we will further comment on after the next paragraph on the Group Contribution methodology. The third well-known method is the _Group Contribution (GC) method_. The GC methods (see [19; 20; 21; 22]) belong to the class of empirical property models, also known as additive methods, where the molecular structure of a compound is broken down into _building blocks_ and the property of a compound is estimated by the summation of the contributions of each building block as schematically illustrated in Figure 1. In GC-methods, the building blocks are functional groups and these methods are based on the assumption that a property value of any group has the same contribution in all the compounds where it appears and that the property value of the compound is a function of the contributions of all the groups needed for a unique representation of the molecular structure of the compound. In this way, the GC-methods are similar to methods based on topological indices [23], where the building blocks are topological indices [24; 25], bond contributions [25; 26], conjugates [27; 28] or a combination of them. The GC-methods could also be considered a special class of QSAR methods [29; 30] where the molecular descriptors are the building blocks, that is, the functional groups. Group or Fragment based QSAR is also known as GQSAR. The simplest form of a GC approach has a large set of simple groups that allows description of the molecular structures of a wide variety of organic compounds, for example  
\[\text{Property P}=\text{constant}+\sum\
olimits_{i}\text{C}_{i} \tag{2}\]  
in which the \(\text{C}_{i}\) are values characteristic of a chemical group. So, for instance for alkanes one can well imagine that some properties depend on the \(\text{CH}_{3}\) end-groups and the number of \(\text{CH}_{2}\) groups only. In such a case, very few parameters can make a very good method. Obviously, the entire idea originates from the empirical knowledge that individual chemical groups have an individual and additive contribution to many properties. However, these groups capture only partially the proximity effects and are unable to distinguish among isomers. For this reason, the first level of estimation is intended to deal with simple and mono-functional compounds. To accommodate this deficiency, [PERSON] and [PERSON] [23] and, before them, [PERSON] and [PERSON] [21] and [PERSON] et al. [20] (who also used non-linear  
Figure 1: Schematic representation of the Group Contribution concept where the molecule is broken into building blocks. group contributions) introduced higher-order groups, the purpose of which was to add molecular structural information (interactions between groups) of the compound as a higher-level contribution or correction to the value from the first level. The ultimate objective of the proposed multilevel scheme is to enhance the accuracy, reliability and the range of application for a number of important pure component properties. Also for the Group Contribution approach there exist many references for many properties. The Gani group has probably the currently most extensive set of properties combined with, at least on average, the best accuracy ([[http://www.capec.kt.dtu.dk/Software/ICAS-and-its-Tools/](http://www.capec.kt.dtu.dk/Software/ICAS-and-its-Tools/)]([http://www.capec.kt.dtu.dk/Software/ICAS-and-its-Tools/](http://www.capec.kt.dtu.dk/Software/ICAS-and-its-Tools/))). Among the properties we find vapour pressure and heat of vaporization [31], enthalpy of formation with the required chemical accuracy [32] and environment related properties [33].