## 2 Accuracy of Predictive Methods and What Is Required
### Organic Molecules: the Virtue of Group Contribution Methods  
For the chemist and the chemical engineer, as well as the corresponding environmental scientists, a significant and very important part of the total number of possible molecules for which we need property data is the class of organic molecules. For these one can argue, as we believe, there is a preferred approach. There is a unique notation for each organic molecule, known as the Simplified Molecular Input Line Entry Specification (SMILES) [43]. It is a kind of linguistic construct, for example acetic acid reads CC(=O)) and it is _a unique notation for each unique molecule_. It is the most concise way in writing down the structure of a molecule whilst still retaining all necessary information. A SMILES actually only contains the atom types and the entire connectivity within the molecule. This suggests that it should be possible to establish a QSPR relation, linking the unique property value of a specific molecule to the unique structure of that molecule, on the basis of the SMILES notation only. At least for small molecules this implies only relatively few elements and thus few parameters to be fitted. The SMILES notation does not contain the hydrogen atoms, as these are implicit when the molecular structure is drawn. The next logical step is the recognition that we are now close to the group contribution approach: a C in the SMILES notation within is a CH\({}_{3}\) or CH\({}_{2}\) are the groups in a GC approach. Furthermore, a combination of atoms in a SMILES might form a group, for example C(=O) or C(=O)O. As we only have atom types and atom-atom connectivity in a SMILES notation, to describe a property of a molecule could still imply we would need the entire connectivity table of a molecule to arrive at proper description of the physicochemical property. Although this still means the SMILES and therefore the molecular structure can be uniquely connected to properties, this would possibly be at the cost of many adjustable parameters and possibly non-linear relations. We now come back to what we know as chemists, namely the fact that it is well-known that in organic chemistry many physicochemical properties can be based on additivity based on molecular groups for instance a CH\({}_{3}\), an acid group COOH and so forth. Thus, we need only limited contribution of groups that are connected at further distance. This is precisely what the first order Group Contribution (GC) methods entail. It is based upon the recognition that a molecular group sufficiently far away from another does not influence the contribution of that other group to the physicochemical property of interest. This will not always be the case but we have seen before that such effects can be incorporated by introducing higher order effect [22]. Thus, the Group Contribution method is the most logical and efficient approach to start developing predictive methods for physicochemical properties, requiring the minimum on input (only the SMILES in effect) and the lowest number of adjustable parameters. Using the GC approach also circumvents the potential problem of overfitting which may readily occur when using a larger number of molecular descriptors in a QSPR approach (see next section). One could see the GC approach as a QSPR approach with only relatively few specific descriptors, namely atom type and connectivity. The downside is of course that the approach will work for organics but not for systems like many of the organometallics. As mentioned earlier, an alternative for organometallics is the use of QSPR based on molecular descriptors rather than molecular group. See Section 3.3 for further reference. Another advantage in using GC-based methods is illustrated by the following example.