GESSE : The “magic triangle”
The “magic triangle” of “drugs, targets, side effects” (SEs) is the new “holy grail” of the pharmaceutical industry. This figure shows a subset of a triangular matrix associating the most significant drug–target relationships predicted by the authors’ GES algorithm with the SEs for those drugs predicted by the same authors’ GESSE approach. Combining GES with GESSE allows the physicochemical space of drugs, the polypharmacologically relevant biological subspace of drug targets, and the phenotypic space of SEs to be related computationally.
GES Polypharmacology Fingerprints: A Novel Approach for Drug Repositioning
Polypharmacology is now recognized as an increasingly important aspect of drug design. We previously introduced the Gaussian ensemble screening (GES) approach to predict relationships between drug classes rapidly without requiring thousands of bootstrap comparisons as in current promiscuity prediction approaches. Here we present the GES “computational polypharmacology fingerprint” (CPF), the first target fingerprint to encode drug promiscuity information. The similarity between the 3D shapes and chemical properties of ligands is calculated using PARAFIT and our HPCC programs to give a consensus shape-plus-chemistry ligand similarity score, and ligand promiscuity for a given set of targets is quantified using the GES fingerprints. To demonstrate our approach, we calculated the CPFs for a set of ligands from DrugBank that are related to some 800 targets. The performance of the approach was measured by comparing our CPF with an in-house “experimental polypharmacology fingerprint” (EPF) built using publicly available experimental data for the targets that comprise the fingerprint. Overall, the GES CPF gives very low fall-out while still giving high precision. We present examples of polypharmacology relationships predicted by our approach that have been experimentally validated. This demonstrates that our CPF approach can successfully describe drug–target relationships and can serve as a novel drug repurposing method for proposing new targets for preclinical compounds and clinical drug candidates.
A highly specific and sensitive pharmacophore model for identifying CXCR4 antagonists. Comparison with docking and shape-matching virtual screening performance
HIV infection is initiated by fusion of the virus with the target cell through binding of the viral gp120 protein with the CD4 cell surface receptor protein and the CXCR4 or CCR5 coreceptors. There is currently considerable interest in developing novel ligands that can modulate the conformations of these coreceptors and, hence, ultimately block virus–cell fusion. Herein, we present a highly specific and sensitive pharmacophore model for identifying CXCR4 antagonists that could potentially serve as HIV entry inhibitors. Its performance was compared with docking and shape-matching virtual screening approaches using 3OE6 CXCR4 crystal structure and high-affinity ligands as query molecules, respectively. The performance of these methods was compared by virtually screening a library assembled by us, consisting of 228 high affinity known CXCR4 inhibitors from 20 different chemotype families and 4696 similar presumed inactive molecules. The area under the ROC plot (AUC), enrichment factors, and diversity of the resulting virtual hit lists was analyzed. Results show that our pharmacophore model achieves the highest VS performance among all the docking and shape-based scoring functions used. Its high selectivity and sensitivity makes our pharmacophore a very good filter for identifying CXCR4 antagonists.
Benchmarking of HPCC: A novel 3D molecular representation combining shape and pharmacophoric descriptors for efficient molecular similarity assessments
Since 3D molecular shape is an important determinant of biological activity, designing accurate 3D molecular representations is still of high interest. Several chemoinformatic approaches have been developed to try to describe accurate molecular shapes.
Here, we present a novel 3D molecular description, namely harmonic pharma chemistry coefficient (HPCC), combining a ligand-centric pharmacophoric description projected onto a spherical harmonic based shape of a ligand. The performance of HPCC was evaluated by comparison to the standard ROCS software in a ligand-based virtual screening (VS) approach using the publicly available directory of useful decoys (DUD) data set comprising over 100,000 compounds distributed across 40 protein targets.
Our results were analyzed using commonly reported statistics such as the area under the curve (AUC) and normalized sum of logarithms of ranks (NSLR) metrics. Overall, our HPCC 3D method is globally as efficient as the state-of-the-art ROCS software in terms of enrichment and slightly better for more than half of the DUD targets. Since it is largely admitted that VS results depend strongly on the nature of the protein families, we believe that the present HPCC solution is of interest over the current ligand-based VS methods.
Recent trends and future prospects in computational GPCR drug discovery: from virtual screening to polypharmacology
Extending virtual screening approaches to deal with multi-target drug design and polypharmacology is an increasingly important aspect in drug design. In light of this, the concept of accessible chemical space and its exploration should be reviewed. The great advantages of re-using drugs with safe pharmacological profiles with favourable pharmacokinetic properties highlights drug repositioning as a valid alternative to rational drug design, massive drug development efforts, and high-throughput screening, especially when supported by in silico techniques. Here, we discuss some of the advantages of multi-target approaches, and we review some significant examples of their application in the last decade to that well known class of pharmaceutical targets, the G-protein coupled receptors.