Predicting drug polypharmacology using a novel surface property similarity-based approach
Violeta I. Pérez‐Nueno, Vishwesh Venkatraman, Lazaros Mavridis, David W. Ritchie · Journal of Cheminformatics · 2011
In recent years, polypharmacology is becoming anincreasingly important aspect in drug design. For exam-ple, pharmaceutical companies are discovering more andmore cases in which multiple drugs bind to a given tar-get (promiscuous targets) and in which a given drugbinds to more than one target (promiscuous ligands).Both of these phenomena are clearly of great impor-tance when considering drug side-effects. Given thatscreening drugs against all the proteins expressed by thehuman genome is infeasible, several computational tech-niques for predicting the pharmacological profiles ofdrugs have been developed, ranging from statistical ana-lyses of chemical fingerprints and biological activities [1]to 3D docking of ligand structures into protein pockets.Here we present a novel shape-based approach whichuses spherical harmonic (SH) representations [2,3] tocompare molecular surfaces and key surface propertiesvery efficiently. This approach compares targets by theSH similarity of their ligands and also of their bindingpockets. This allows promiscuous ligands and targets tobe identified and characterized.In this contribution, we present details of ourapproach applied to a subset of the MDL Drug DataReport (MDDR) database containing 65367 compoundsdistributed over 249 diverse pharmacological targets forwhich experimental binding information is known. Thesimilarity of each ligand to each target’s ligand set isquantified and used to predict promiscuity. To ourknowledge, this is the largest all-against-all polypharma-cological study to have been carried out using shape-based techniques. We compare our promiscuity predic-tions with computational and experimental resultsobtained by Keiser et al.[4]. We also analyse thecorrelation between binding pocket shapes and ligand-based promiscuity predictions using the ligand andpocket shape similarity matrices.