Virtual screening, or in silico screening, is a new approach attracting increasing levels of interest in the pharmaceutical industry as a productive and cost-effective technology in the search for novel lead compounds. Although the principles involved-the computational analysis of chemical databases to identify compounds appropriate for a given biological receptor-have been pursued for several years in molecular modeling groups, the availability of inexpensive high-performance computing platforms has transformed the process so that increasingly complex and more accurate analyses can be performed on very large data sets. The virtual screening technology of Protherics Molecular Design Ltd. is based on its integrated software environment for receptorbased drug design, called Prometheus. In particular, molecular docking is used to predict the binding modes and binding affinities of every compound in the data set to a given biological receptor. This method represents a very detailed and relevant basis for prioritizing compounds for biological screening. This paper discusses the broader scope of virtual screening and, as an example, describes our recent work in docking one million compounds into the estrogen hormone receptor in order to highlight the technical feasibility of performing very largescale virtual screening as a route to identifying novel drug leads.

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.

Steric complementarity is a prerequisite for ligand-receptor recognition; this implies that drugs with a common receptor binding site should possess sterically similar binding surfaces. This principle is used as the basis for an automatic and unbiased method that superposes molecules. One molecule is rotated and translated to maximize the overlap between the two molecular surface volumes. A fast grid-based method is used to determine the extent of this overlap, and this is optimized using simulated annealing. Matches with high steric similarity scores are then sorted on the basis of both hydrogen-bond and electrostatic similarity between the matched molecules. Flexible molecules are treated as a set of rigid representative conformers. The algorithm has correctly predicted superpositions between a number of paris of molecules, according to crystallographic data from ligands that have been co-crystallized at common enzyme binding sites.

Structure-based virtual screening was performed against the target dipeptidyl peptidase IV (DPP-IV) to identify good chemical starting points for medicinal chemistry. A database of available compounds was filtered by calculated physical properties and undesired chemistry. This database was matched against two in-house designed DPP-IV pharmacophores, and the hits from these pharmacophore searches were docked into a DPP-IV crystal structure. Compounds were then selected for testing and 51 active compounds were identified from a list of 4000 compounds tested. These had activities ranging from 30% to 82% when tested at a concentration of 30 microM in an enzyme inhibition assay.

In order to assess the potential of sPLA-X as a therapeutic target for atherosclerosis, novel sPLA inhibitors with improved type X selectivity are required. To achieve the objective of identifying such compounds, we embarked on a lead generation effort that resulted in the identification of a novel series of indole-2-carboxamides as selective sPLA2-X inhibitors with excellent potential for further optimization.