## 2 Accuracy of Predictive Methods and What Is Required
### Why do Methods often not Perform up to the Required Level of Accuracy and Reliability? The question that is now pertinent is why property prediction methods, sufficiently often, do not yet lead to the required accuracy and reliability of prediction. We recall that the required accuracy and reliability is what bench chemists, process engineers and toxicologists need to make these predictions preferably first time right. We believe that, by experience, the following factors are relevant  
1. the preferred or exclusive use of a specific class of methods, for example quantum mechanics or statistical analysis using molecular descriptors \begin{table}
\begin{tabular}{c c} \hline \hline
**Tool** & **Concordance (\%) = Agreement with Experiment** \\ \hline TOPKAT & 58 \\ \hline DEREK & 59 \\ \hline CASE & 49 \\ \hline COMPACT & 54 \\ \hline Human Experts & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance of some well-known in silico tools for the prediction. Table taken from Reference [35]. 2. no optimal parametrization of statistical models or GC methods
3. quality of data
4. inappropriate account of the physics involved
5. insufficient transfer of knowledge between different fields of science  
Ad _(i) preferred or exclusive use of particular methods._ Scientist are experts in their field of expertise, and, generally speaking, we tend to apply this expertise to new upcoming problems. Theoretical chemists mostly apply ab initio calculations and molecular simulations to evaluate molecular properties but even after many years and high-level calculations the desired chemical accuracy of \"1 kcal/mole\" for heats of formation and limited number of outliers has not been achieved for a broader class of molecules using one methodology [5]. Here the recent results obtained by applying the Group Contribution method by [PERSON] et al. [32] has delivered an appropriate solution that serves the need of both accuracy and reliability for heat of formation of several classes of organic molecules. Moreover, there is a straightforward and natural way to make that GC method better or extend it to new classes of molecules and results are produced 'on the fly' (this will be further elaborated in one of the next sections), a promise _ab initio_ approaches are far away from. This all applies to organic molecules. However, as Group Contribution methods rely on the summation of individual group contributions with some higher order contribution corrections, generally speaking, organometallic compounds with their much more complicated electronic structure and diffuse molecular orbitals cannot be realistically treated with such method as the overall property cannot be split into molecular group contributions in a simple and uniform way which is transferrable over the entire class of similar species. Here we thus still rely on, for example, quantum mechanical calculations even though the required accuracy will be difficult to achieve and the calculations are still expensive and not 'on the fly.' The difficulty with ab initio calculations remains that to arrive at the required accuracy and reliability extremely demanding and computationally intense calculations are required that also require the involvement of a true specialist. This will not facilitate the use of predictive methods by practitioners. An alternative for, for example, organometallics is the use of QSPR based on molecular descriptors rather than molecular group. See Section 3.3 for further reference. Finally, thus far GC methods were not really developed for charged or radical type organic molecules but the available methodologies can be applied without any restriction to such classes of molecules. Also here ab initio calculations can be very useful to provide input, that is not available experimentally, to construct GC methods.