Algorithmic similarity analysis

The Principles of Nonvisual, Algorithmic Similarity Analysis Automated Similarity Assessment by Computer... 

Energy minimisation and normal mode analysis have an important role to play in the study of the solid state. Algorithms similar to those discussed above are employed but an extra feature of such systems, at least when they form a perfect lattice, is that it is can be possible to exploit the space group symmetry of the lattice to speed up the calculations. It is also important to properly take the interactions with atoms in neighbouring cells into account. 

FIGURE 4.9 Molecular models of the nine potent lead variants derived by similarity analysis of the Analog 5-1-9-3-4, generated by the ligand fit docking algorithm of Cerius2 [20]. 

Durand, P.J., Pasari, R., Baker, J.W. and Tsai, C. (1999). An Efficient Algorithm for Similarity Analysis of Molecules. Internet Journal of Chemistry, 2 - Article 17. 

Eckert, H., Vogt, I. and Bajorath, J. (2006) Mapping algorithms for molecular similarity analysis and ligand-based virtual screening design of DynaMAD and comparison with MAD and DMC. /. Chem. Inf. Model, 46, 1623-1634. 

In dissimilarity-based compound selection the required subset of molecules is identified directly, using an appropriate measure of dissimilarity (often taken to be the complement of the similarity). This contrasts with the two-stage procedure in cluster analysis, where it is first necessary to group together the molecules and then decide which to select. Most methods for dissimilarity-based selection fall into one of two categories maximum dissimilarity algorithms and sphere exclusion algorithms [Snarey et al. 1997]. 

The field points must then be fitted to predict the activity. There are generally far more field points than known compound activities to be fitted. The least-squares algorithms used in QSAR studies do not function for such an underdetermined system. A partial least squares (PLS) algorithm is used for this type of fitting. This method starts with matrices of field data and activity data. These matrices are then used to derive two new matrices containing a description of the system and the residual noise in the data. Earlier studies used a similar technique, called principal component analysis (PCA). PLS is generally considered to be superior. 

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