Using sets of experimental distance restraints, which characterize active or inactive receptor conformations, and the X-ray crystal structure of the inactive form of bovine rhodopsin as a starting point, we have constructed models of both the active and inactive forms of rhodopsin and the beta2-adrenergic G-protein coupled receptors (GPCRs). The distance restraints were obtained from published data for site-directed crosslinking, engineered zinc binding, site-directed spin-labeling, IR spectroscopy, and cysteine accessibility studies conducted on class A GPCRs. Molecular dynamics simulations in the presence of either "active" or "inactive" restraints were used to generate two distinguishable receptor models. The process for generating the inactive and active models was validated by the hit rates, yields, and enrichment factors determined for the selection of antagonists in the inactive model and for the selection of agonists in the active model from a set of nonadrenergic GPCR drug-like ligands in a virtual screen using ligand docking software. The simulation results provide new insights into the relationships observed between selected biochemical data, the crystal structure of rhodopsin, and the structural rearrangements that occur during activation.

The ability to interpret the predictions made by quantitative structure activity relationships (QSARs) offers a number of advantages. Whilst QSARs built using non6linear modelling approaches, such as the popular Random Forest algorithm, might sometimes be more predictive than those built using linear modelling approaches, their predictions have been perceived as difficult to interpret. However, a growing number of approaches have been proposed for interpreting non6linear QSAR models in general and Random Forest in particular. In the current work, we compare the performance of Random Forest to two widely used linear modelling approaches: linear Support Vector Machines (SVM), or Support Vector Regression (SVR), and Partial Least Squares (PLS). We compare their performance in terms of their predictivity as well as the chemical interpretability of the predictions, using novel scoring schemes for assessing Heat Map images of substructural contributions. We critically assess different approaches to interpreting Random Forest models as well as for obtaining predictions from the forest. We assess the models on a large number of widely employed, public domain benchmark datasets corresponding to regression and binary classification problems of relevance to hit identification and toxicology. We conclude that Random Forest typically yields comparable or possibly better predictive performance than the linear modelling approaches and that its predictions may also be interpreted in a chemically and biologically meaningful way. In contrast to earlier work looking at interpreting non6linear QSAR models, we directly compare two methodologically distinct approaches for interpreting Random Forest models. The approaches for interpreting Random

It has been known for a long time that certain substructures bind to the heme iron in cytochromes P450. Detection of spectroscopic changes and crystal structures of protein ligand complexes have provided qualitative evidence, including for aromatic nitrogen-containing ligands. Compounds containing these same substructures are more likely to inhibit cytochrome P450s than expected due to lipophilicity. These two sets of observations are not easily linked by experiment, because binding to the iron atom alone is not readily measured. Quantum mechanical (density functional) calculations of binding energies for a number of different aromatic heterocycles to heme iron in a range of oxidation and spin states can provide a quantitative link between the observed structures and the biochemical inhibition that is measured. The studies reported here for a set of heteroaromatic rings containing nitrogen begin with quantum mechanical calculations which provide geometries and binding energies. Subsequently, AstraZeneca's database of cytochrome P450 inhibition assays has been searched to find data that are relevant to the same set of heteroaromatic compounds. These data have been analyzed in a number of fashions to account for both the narrow dynamic range of the assays and the lipophilicity dependence of this kind of inhibition. Finally, crystal structures have provided experimental geometric information. Taken together these different sources suggest that binding to the metal in our inhibition assays is dominated by Fe(III) in its doublet state, most likely occurring when the iron is pentavalent. Computed binding energies to this state contrast with the hydrogen-bond acceptor ability and basicity of the compounds, neither of which are able to correctly account for the effect of the particular environment in which the iron is found. This highlights the value of modeling biochemical events as closely as can be computationally afforded. The computational protocol devised was used to make predictions about a set of as yet unknown heteroaromatic compounds suggested by Pitt et al.

Our previously derived models of the active state of the β2-adrenergic receptor are compared with recently published X-ray crystallographic structures of activated GPCRs (G-protein-coupled receptors). These molecular dynamics-based models using experimental data derived from biophysical experiments on activation were used to restrain the receptor to an active state that gave high enrichment for agonists in virtual screening. The β2-adrenergic receptor active model and X-ray structures are in good agreement over both the transmembrane region and the orthosteric binding site, although in some regions the active model is more similar to the active rhodopsin X-ray structures. The general features of the microswitches were well reproduced, but with minor differences, partly because of the unexpected X-ray results for the rotamer toggle switch. In addition, most of the interacting residues between the receptor and the G-protein were identified. This analysis of the modelling has also given important additional insight into GPCR dimerization: re-analysis of results on photoaffinity analogues of rhodopsin provided additional evidence that TM4 (transmembrane helix 4) resides at the dimer interface and that ligands such as bivalent ligands may pass between the mobile helices. A comparison, and discussion, is also carried out between the use of implicit and explicit solvent for active-state modelling.

Databases of small, potentially bioactive molecules are ubiquitous across the industry and academia. Designed such that each unique compound should appear only once, the multiplicity of ways in which many compounds can be represented means that these databases require methods for standardizing the representation of chemistry. This is commonly achieved through the use of "Chemistry Business Rules", sets of predefined rules that describe the "house style" of the database in question. At Syngenta, the historical approach to the design of chemistry business rules has been to focus on consistency of representation, with chemical relevance given secondary consideration. In this work, we overturn that convention. Through the use of quantum chemistry calculations, we define a set of chemistry business rules for tautomer standardization that reproduces gas-phase energetic preferences. We go on to show that, compared to our historic approach, this method yields tautomers that are in better agreement with those observed experimentally in condensed phases and that are better suited for use in predictive models.