2D descriptors

D descriptors), the 3D structure, or the molecular surface (3D descriptors) of a structure. Which kind of descriptors should or can be used is primarily dependent on the si2e of the data set to be studied and the required accuracy for example, if a QSPR model is intended to be used for hundreds of thousands of compounds, a somehow reduced accuracy will probably be acceptable for the benefit of short processing times. Chapter 8 gives a detailed introduction to the calculation methods for molecular descriptors. 

This study was done because we wanted to see whether 3D descriptors can improve on the models obtained by 2D descriptors. Futhermore, we wanted to use the descriptor set as initially chosen, without any tedious selection of descriptor as reported in the Tutorial in Section 10.1.5.2. 

D descriptors are simple and easy to imderstand, while the 3D descriptors contain more information. The descriptors of both methods were derived from the molecular structures. Thus the models that were developed here are suitable for in-sdico data screening and library design. 

Fig. 18.4 An ideal compound library. Compounds (black dots) are uniformly distributed in 2D descriptor space, a plane defined by two molecular descriptors that correlate with biological activity but not with one another. The compounds are surrounded by nonoverlapping neighborhoods (circles)...

Fig. 2. Cells and selected molecules in a 2D descriptor space formed by variables Xj and x2. There are four bins for each ID descriptor (solid or dashed lines) and four cells in a 2 x 2 arrangement in two dimensions (solid lines). Solid and open circles represent four molecules selected from 20 and the remaining unselected molecules, respectively. Panels A and B show poor and good selections, respectively, according to the UCC criterion.

Already at this early stage, using simple 2D descriptors, the model yields important mechanistic information The correlation for the TON and the TOE depends strongly on the reaction temperature, with a cut-off point at 120 C (Table 2), The chemical reason for this is that Pd nanoclusters form much faster above 120 °C (25), and the reaction follows a pathway that is independent of the ligand. 

Figure 6.16 Observed and predicted bite angle and flexibility values for a set of biphosphine and biphosphite ligands. The empty dots and gray lines represent respectivelythe bite angle and flexibility values calculated on a set of 80 ligand-metal complexes retrieved from the Cambridge Crystallographic Database. Black dots and lines represent the same values predicted using a 2D descriptor QSAR model.

Selecting the right variables often improves the models and makes interpretation easier. When there are too many descriptors, and especially when these descriptors do not have a clear physico-chemical meaning (e.g., connectivity indices and other 2D descriptors), stochastic methods such as genetic algorithms and evolutionary strategies can be used for finding an optimal subset of descriptors [91,92]. 

These indices are also 2D descriptors because they require a molecular drawing (or graph) in order to determine the number of bonds. 

In 2D-QSAR, model building is based on 2D representation of molecules and 2D descriptors. However, it has become very common to generate 3D-QSAR models. 

Isoniazid derivatives 91 HQSAR and DRAGON descriptors Used HQSAR and generated a test set (R2 = 0.87) for 24 compounds. The results were better than for PLS-QSAR with 2D descriptors from DRAGON (R2 = 0.72) Andrade et al. (34)... 

Topological (2D) descriptors Zagreb, the Zagreb index, is defined as the sum of the squares of vertex valencies [42] Balaban, JX and JY, is a highly discriminating descriptor, whose values do not substantially increase with molecule size and the number of rings present [43] Kappa indices PHI SubgraphCount Chi indices Hosoya, the Hosoya... 

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