Scalable partitioning and exploration of chemical spaces using geometric hashing

Virtual screening (VS) has become a preferred tool to augment high-throughput screening1 and determine new leads in the drug discovery process. The core of a VS informatics pipeline includes several data mining algorithms that work on huge databases of chemical compounds containing millions of molecular structures and their associated data. Thus, scaling traditional applications such as classification, partitioning, and outlier detection for huge chemical data sets without a significant loss in accuracy is very important. In this paper, we introduce a data mining framework built on top of a recently developed fast approximate nearest-neighbor-finding algorithm2 called locality-sensitive hashing (LSH) that can be used to mine huge chemical spaces in a scalable fashion using very modest computational resources. The core LSH algorithm hashes chemical descriptors so that points close to each other in the descriptor space are also close to each other in the hashed space. Using this data structure, one can perf...