Perhaps the most fundamental task related to chemical diversity is that of selecting a diverse subset of compounds from a much larger population of compounds. The obvious objective of that task is to identify a subset which best represents the full range of chemical diversity present in the larger population. This presentation will address those questions and the solutions we have implemented in our DiverseSelector package for managing chemical diversity. Whereas the dimensionality of our physical world is predefined as 3, the dimensionality of a chemistry space can be chosen to best suit our particular needs. Although not often discussed in such terms, each bit represents an axis in a multi-dimensional chemistry space.
Fingerprints typically consist of hundreds or even thousands of bits.
However, we have shown that the Tanimoto dissimilarity index is non-Euclidean and that another method for computing such distances may be advantageous. This, however, is not the focus of this presentation. However, distance-based algorithms are poorly suited to the tasks of identifying and filling in diversity voids. Clearly, identifying and filling in diversity voids would be trivially simple if we knew which and how many objects were located in each square. Each compound occupies some particular cell.