We present the global mapping of pharmacological space by the integration of several vast sources of medicinal chemistry structure-activity relationships (SAR) data. Our comprehensive mapping of pharmacological space enables us to identify confidently the human targets for which chemical tools and drugs have been discovered to date. The integration of SAR data from diverse sources by unique canonical chemical structure, protein sequence and disease indication enables the construction of a ligand-target matrix to explore the global relationships between chemical structure and biological targets. Using the data matrix, we are able to catalog the links between proteins in chemical space as a polypharmacology interaction network. We demonstrate that probabilistic models can be used to predict pharmacology from a large knowledge base. The relationships between proteins, chemical structures and drug-like properties provide a framework for developing a probabilistic approach to drug discovery that can be exploited to increase research productivity.

A fast new algorithm (Fingerprints for Ligands And Proteins or FLAP) able to describe small molecules and protein structures using a common reference framework of four-point pharmacophore fingerprints and a molecular-cavity shape is described in detail. The procedure starts by using the GRID force field to calculate molecular interaction fields, which are then used to identify particular target locations where an energetic interaction with small molecular features would be very favorable. The target points thus calculated are then used by FLAP to build all possible four-point pharmacophores present in the given target site. A related approach can be applied to small molecules, using directly the GRID atom types to identify pharmacophoric features, and this complementary description of the target and ligand then leads to several novel applications. FLAP can be used for selectivity studies or similarity analyses in order to compare macromolecules without superposing them. Protein families can be compared and clustered into target classes, without bias from previous knowledge and without requiring protein superposition, alignment, or knowledge-based comparison. FLAP can be used effectively for ligand-based virtual screening and structure-based virtual screening, with the pharmacophore molecular recognition. Finally, the new method can calculate descriptors for chemometric analysis and can initiate a docking procedure. This paper presents the background to the new procedure and includes case studies illustrating several relevant applications of the new approach.

A new 4-point pharmacophore method for molecular similarity and diversity that rapidly calculates all potential pharmacophores/pharmacophoric shapes for a molecule or a protein site is described. The method, an extension to the ChemDiverse/Chem-X software (Oxford Molecular, Oxford, England), has also been customized to enable a new internally referenced measure of pharmacophore diversity. The "privileged" substructure concept for the design of high-affinity ligands is presented, and an example of this new method is described for the design of combinatorial libraries for 7-transmembrane G-protein-coupled receptor targets, where "privileged" substructures are used as special features to internally reference the pharmacophoric shapes. Up to 7 features and 15 distance ranges are considered, giving up to 350 million potential 4-point 3D pharmacophores/molecule. The resultant pharmacophore "key" ("fingerprint") serves as a powerful measure for diversity or similarity, calculable for both a ligand and a protein site, and provides a consistent frame of reference for comparing molecules, sets of molecules, and protein sites. Explicit "on-the-fly" conformational sampling is performed for a molecule to enable the calculation of all geometries accessible for all combinations of four features (i.e., 4-point pharmacophores) at any desired sampling resolution. For a protein site, complementary site points to groups displayed in the site are generated and all combinations of four site points are considered. In this paper we report (i) the details of our customized implementation of the method and its modification to systematically measure 4-point pharmacophores relative to a "special" substructure of interest present in the molecules under study; (ii) comparisons of 3- and 4-point pharmacophore methods, highlighting the much increased resolution of the 4-point method; (iii) applications of the 4-point potential pharmacophore descriptors as a new measure of molecular similarity and diversity and for the design of focused/biased combinatorial libraries.

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