Efficient exploration of large combinatorial chemistry spaces by monomer-based similarity searching

In modern drug discovery, 2-D similarity searching is widely employed as a cost-effective way to screen large compound collections and select subsets of molecules that may have interesting biological activity prior to experimental screening. Nowadays, there is a growing interest in applying the existing 2-D similarity searching methods to combinatorial chemistry libraries to search for novel hits or to evolve lead series. A dilemma thus arises when many identical substructures recur in library products and they have to be considered repeatedly in descriptor calculations. The dilemma is exacerbated by the astronomical number of combinatorial products. This problem imposes a major barrier to similarity searching of large combinatorial chemistry spaces. An efficient approach, termed Monomer-based Similarity Searching (MoBSS), is proposed to remedy the problem. MoBSS calculates atom pair (AP) descriptors based on interatomic topological distances, which lend themselves to pair additivity. A fast algorithm is employed in MoBSS to rapidly compute product atom pairs from those of the constituent fragments. The details of the algorithm are presented along with a series of proof-of-concept studies, which demonstrate the speed, accuracy, and utility of the MoBSS approach.