In order to design a drug curing a given pathology, an approach which is commonly used consists in first selecting those that work best among molecules known to treat similar symptoms. In view of the number of known molecules, an exhaustive approach is often impossible. It is therefore important to be able to find those molecules in such a database. The aim of this work is to present a probabilistic algorithm for this task.
Molecules are close when they differ by few molecular fragments.
Other measures of similarity might also be of interest in practice. A fragment is selected at random and the database is partitioned into two subsets according to the presence or absence of this fragment. Thus a few iterations of this idea yield a very large proportion of N . K plays an important part in the efficiency of the algorithm. When K is small the search is faster but finds less pairs, so that the number of times it has to be repeated to obtain the same number of pairs can be larger than for higher values of K .