Hit-directed nearest-neighbor searching.
This work describes a practical strategy used at Pharmacia for identifying compounds for follow-up screening following an initial HTS campaign against targets where no 3-D structural information is available and preliminary SAR models do not exist. The approach explicitly takes into account different representations of chemistry space and identifies compounds for follow-up screening that are likely to provide the best overall coverage of the chemistry spaces considered. Specifically, the work employs hit-directed nearest-neighbor (HDNN) searching of compound databases based upon a set of "probe compounds" obtained as hits in the preliminary high-throughput screens. Four different molecular representations that generate nearly unique chemistry spaces are used. The representations include 3-D, 2-D, 2-D topological BCUTs (2-DT) and molecular fingerprints derived from substructural fragments. In the case of the BCUT representations the NN searching is distance based, while in the case of molecular fingerprints a similarity-based measure is used. Generally, the results obtained differ significantly among all four methods, that is, the sets of NN compounds have surprisingly little overlap. Moreover, in all of the four chemistry space representations, a minimum of 3- to 4-fold enrichment in actives over random screening is observed even though the actives identified in each of the sets of NNs are in large measure unique. These results suggest that use of multiple searches based upon a variety of molecular representations provides an effective way of identifying more hits in HDNN searches of chemistry spaces than can be realized with single searches.
Shanmugasundaram
Veerabahu
V
Structural & Computational Chemistry, Pharmacia Corporation, Kalamazoo, Michigan 49007, USA. Veerabahu.Shanmugasundaram@pfizer.com
Maggiora
Gerald M
GM
Lajiness
Michael S
MS
D001419:Bacteria / Q000201:enzymology; D020650:Combinatorial Chemistry Techniques; D016208:Databases, Factual; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D004798:Enzymes / Q000187:drug effects / Q000378:metabolism; D008433:Mathematics; D008956:Models, Chemical; D018160:Receptors, Cytoplasmic and Nuclear / Q000819:agonists / Q000037:antagonists & inhibitors / Q000378:metabolism; D012984:Software
D004798:Enzymes; D018160:Receptors, Cytoplasmic and Nuclear