Bioisosteres have been defined as structurally different molecules or substructures that can form comparable intermolecular interactions, and therefore, fragments that bind to similar protein structures exhibit a degree of bioisosterism. We present KRIPO (Key Representation of Interaction in POckets): a new method for quantifying the similarities of binding site subpockets based on pharmacophore fingerprints. The binding site fingerprints have been optimized to improve their performance for both intra- and interprotein family comparisons. A range of attributes of the fingerprints was considered in the optimization, including the placement of pharmacophore features, whether or not the fingerprints are fuzzified, and the resolution and complexity of the pharmacophore fingerprints (2-, 3-, and 4-point fingerprints). Fuzzy 3-point pharmacophore fingerprints were found to represent the optimal balance between computational resource requirements and the identification of potential replacements. The complete PDB was converted into a database comprising almost 300,000 optimized fingerprints of local binding sites together with their associated ligand fragments. The value of the approach is demonstrated by application to two crystal structures from the Protein Data Bank: (1) a MAP kinase P38 structure in complex with a pyridinylimidazole inhibitor (1A9U) and (2) a complex of thrombin with melagatran (1K22). Potentially valuable bioisosteric replacements for all subpockets of the two studied protein are identified.

The velocity field in the immediate vicinity of a curved vortex comprises a circulation around the vortex, a component due to the vortex curvature, and a ‘remainder’ due to the more distant parts of the vortex. The first two components are relatively well understood but the remainder is known only for a few specific vortex geometries, most notably, the vortex ring. In this paper we derive a closed form for the remainder that is valid for all values of the pitch of an infinite helical vortex. The remainder is obtained firstly from Hardin's (1982) solution for the flow induced by a helical line vortex (of zero thickness). We then use Ricca's (1994) implementation of the Moore & Saffman (1972) formulation to obtain the remainder for a helical vortex with a finite circular core over which the circulation is distributed uniformly. It is shown analytically that the two remainders differ by 1/4 for all values of the pitch. This generalizes the results of Kuibin & Okulov (1998) who obtained the remainders and their difference asymptotically for small and large pitch. An asymptotic analysis of the new closed-form remainders using Mellin transforms provides a complete representation by a residue series and reveals a minor correction to the asymptotic expression of Kuibin & Okulov (1998) for the remainder at small pitch.

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Abstract. Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of a naive Bayesian classifier when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

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We present an automated QSAR procedure that is used in AstraZeneca's AutoQSAR system. The approach involves automatically selecting the most predictive models from pools of both global and local models. The effectiveness of this QSAR modelling strategy is demonstrated with a retrospective study that uses a diverse selection of 9 early stage AstraZeneca drug discovery projects and 3 physicochemical endpoints: LogD; solubility and human plasma protein binding. We show that the strategy makes a statistically significant improvement to the accuracy of predictions when compared to an updating global strategy, and that the systematic biases inherent in the global model predictions are almost completely removed. This improvement is attributed to the model selection aspect of the strategy.