Computational library design dissimilarity-based

Cluster-based and dissimilarity-based methods for compound selection were first discussed in the Eighties but it is only in the last few years that the area has attracted substantial attention as a result of the need to provide a rational basis for the design of combinatorial libraries. The four previous sections have provided an overview of the main types of selection method that are already available, with further approaches continuing to appear in the literature. Given this array of possible techniques, it is appropriate to consider ways in which the various methods can be evaluated, both in absolute terms and when compared with each other. A method can be evaluated in terms of its efficiency, /.< ., the computational costs associated with its use, and its effectiveness, /.< ., the extent to which it achieves its aims. As we shall see, it is not immediately obvious how effectiveness should be quantified and we shall thus consider the question of efficiency first, focusing upon the normal algorithmic criteria of CPU time and storage requirements. 

Tanimoto index > 0.9 but may be very different in terms of activity (chemically similar, biologically diverse), while completely different strucmres are known to have the same biological activity (chemically diverse, biologically similar). This intrinsic drawback to the computational screening of virtual libraries should always be considered when interpreting screening results of a computationally designed library, and real data should be used to refine any virtual SAR information based on chemical similarity or dissimilarity. 

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