Hit finding: towards 'smarter' approaches.

Drug discovery is complex and risky, and the chances of success are low. One starting point to discover a new drug is the selective screening of a collection of high value and good quality compounds. Selection of compounds for screening is one of the challenging initial steps in the drug discovery process and is crucial for the success of the project. Optimal selection will enhance the chances of successful hit finding with regard to both number and quality of hits. Several scenarios for compound selection can be envisaged, and are primarily driven by knowledge of the target. Deciding the most appropriate scenario is important and appropriate software packages and chemoinformatics tools are available for these purposes. After screening, researchers may face challenges in selecting the best hits for further optimization. Numerous chemoinformatics tools have emerged recently to address challenges in hit analysis, prioritization and optimization.

High-potency olfactory receptor agonists discovered by virtual high-throughput screening: molecular probes for receptor structure and olfactory function.

The detection of diverse chemical structures by the vertebrate olfactory system is accomplished by the recognition of odorous ligands by their cognate receptors. In the present study, we used computational screening to discover novel high-affinity agonists of an olfactory G protein-coupled receptor that recognizes amino acid ligands. Functional testing of the top candidates validated several agonists with potencies higher than any of the receptor's known natural ligands. Computational modeling revealed molecular interactions involved in ligand binding and further highlighted interactions that have been conserved in evolutionarily divergent amino acid receptors. Significantly, the top compounds display robust activities as odorants in vivo and include a natural product that may be used to signal the presence of bacteria in the environment. Our virtual screening approach should be applicable to the identification of new bioactive molecules for probing the structure of chemosensory receptors and the function of chemosensory systems in vivo.

Parallel screening and activity profiling with HIV protease inhibitor pharmacophore models.

Parallel Screening has been introduced as an in silico method to predict the potential biological activities of compounds by screening them with a multitude of pharmacophore models. This study presents an early application example employing a Pipeline Pilot-based screening platform for automatic large-scale virtual activity profiling. An extensive set of HIV protease inhibitor pharmacophore models was used to screen a selection of active and inactive compounds. Furthermore, we aimed to address the usually critically eyed point, whether it is possible in a parallel screening system to differentiate between similar molecules/molecules acting on closely related proteins, and therefore we incorporated a collection of other protease inhibitors including aspartic protease inhibitors. The results of the screening experiments show a clear trend toward most extensive retrieval of known active ligands, followed by the general protease inhibitors and lowest recovery of inactive compounds.

Pharmacophore modelling: applications in drug discovery.

This review highlights the concept of using pharmacophore models in modern drug research and reviews some important examples as well as success stories. This includes papers from both method-development and application areas. As indicated by the number of publications available, the pharmacophore approach has proven to be extremely useful not only in virtual screening and library design for efficient hit discovery, but also in the optimisation of lead compounds to clinical candidates. Recent studies focus on the use of parallel screening using pharmacophore models for bioactivity profiling and early stage risk assessment of potential side effects and toxicity, due to the interaction of drug candidates with antitargets.

Discovery of high-affinity ligands of sigma1 receptor, ERG2, and emopamil binding protein by pharmacophore modeling and virtual screening.

ERG2, emopamil binding protein (EBP), and sigma-1 receptor (sigma(1)) are enzymes of sterol metabolism and an enzyme-related protein, respectively, that share high affinity for various structurally diverse compounds. To discover novel high-affinity ligands, pharmacophore models were built with Catalyst based upon a series of 23 structurally diverse chemicals exhibiting K(i) values from 10 pM to 100 microM for all three proteins. In virtual screening experiments, we retrieved drugs that were previously reported to bind to one or several of these proteins and also tested 11 new hits experimentally, of which three, among them raloxifene, had affinities for sigma(1) or EBP of <60 nM. When used to search a database of 3525 biochemicals of intermediary metabolism, a slightly modified ERG2 pharmacophore model successfully retrieved 10 substrate candidates among the top 28 hits. Our results indicate that inhibitor-based pharmacophore models for sigma(1), ERG2, and EBP can be used to screen drug and metabolite databases for chemically diverse compounds and putative endogenous ligands.

Structure-based virtual screening: an application to human topoisomerase II alpha.

The eukaryotic topoisomerase II is involved in several vital processes, such as replication, transcription, and recombination. Many compounds interfering with the catalytic action of this enzyme are efficient in human cancer chemotherapy. We applied a methodology combining molecular modeling and virtual screening techniques to identify human topoisomerase II alphainhibitors. Data from structural biology and enzymatic assays together with a good background on the enzyme mechanism of action were helpful in the approach. A human topoisomerase II alpha model provided an insight into the structural features responsible for the activity of the enzyme. A protocol comprising several substructural and protein structure-based three-dimensional pharmacophore filters enabled the successful retrieving of inhibitors of the enzyme from large databases of compounds, thus validating the approach. A subset of protein structural features required for the enzyme inhibition at the protein-DNA interface were identified and incorporated into the pharmacophore models. Compounds sharing a DNA-intercalating chromophore and a moiety interfering with the protein active site emerged as good inhibitors.

Lead identification for modulators of multidrug resistance based on in silico screening with a pharmacophoric feature model.

Considerable effort has been devoted to the characterization of P-glycoprotein - drug interaction in the past. Systematic quantitative structure-activity relationship (QSAR) studies identified both predictive physicochemical parameters and pharmacophoric substructures within homologous series of compounds. Comparative molecular field analysis (CoMFA) led to distinct 3D-QSAR models for propafenone and phenothiazine analogs. Recently, several pharmacophore models have been generated for diverse sets of ligands. Starting from a training set of 15 propafenone-type MDR-modulators, we established a chemical function-based pharmacophore model. The pharmacophoric features identified by this model were (i) one hydrogen bond acceptor, (ii) one hydrophobic area, (iii) two aromatic hydrophobic areas, and (iv) one positive ionizable group. In silico screening of the Derwent World Drug Index using the model led to identification of 28 compounds. Substances retrieved by database screening are diverse in structure and include dihydropyridines, chloroquine analogs, phenothiazines, and terfenadine. On the basis of its general applicability, the presented 3DQSAR model allows in silico screening of virtual compound libraries to identify new potential lead compounds.