Iterative Stochastic Elimination for Solving Complex Combinatorial Problems in Drug Discovery

Iterative Stochastic Elimination (ISE) is a novel algorithm that was originally developed to solve extremely complex problems in protein structure and interactions, and has since been applied to diverse topics that share a few general “ingredients”: they are extremely complex, of combinatorial nature, may be presented as large sets of variables that can each have many alternative values, there is some interdependence of the variables on each other, and there is a scoring function that can evaluate each choice of the problems “configuration”; this is the set of single values of each of the variables that constitute its full presentation. Those are picked randomly in a large sample, the analysis of which allows decisions to be made for rejecting some values for each of the variables; thus resulting in a smaller set of potential combinations. This continues in iterations until the number of combinations allows all the remaining options to be computed exhaustively and to order them by their scores. ISE has been mainly applied to problems that are relevant to drug design and discovery. We demonstrate, among others, the use of ISE to determine the properties of molecular ensembles and to pick the best molecules (“focused libraries”) for hitting a specific target. Future ideas for using ISE are discussed, as well as mentioning its contributions to the construction of two start-up companies.