Xiao Qing Lewell

Evaluation of machine-learning methods for ligand-based virtual screening

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 (NBC) 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 an NBC 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.

Virtual Screening Using Binary Kernel Discrimination: Effect of Noisy Training Data and the Optimization of Performance

Binary kernel discrimination (BKD) uses a training set of compounds, for which structural and qualitative activity data are available, to produce a model that can then be applied to the structures of other compounds in order to predict their likely activity. Experiments with the MDL Drug Data Report database show that the optimal value of the smoothing parameter, and hence the predictive power of BKD, is crucially dependent on the number of false positives in the training set. It is also shown that the best results for BKD are achieved using one particular optimization method for the determination of the smoothing parameter that lies at the heart of the method and using the Jaccard/Tanimoto coefficient in the kernel function that is used to compute the similarity between a test set molecule and the members of the training set.

Novel methylene-linked heterocyclic EP1 receptor antagonists

We describe the SAR, in terms of heterocyclic replacements, for a series of pyrazole EP(1) receptor antagonists. This study led to the identification of several aromatic heterocyclic replacements for the pyrazole in the original compound. Investigation of replacements for the methylene linker uncovered disparate SAR in the thiazole and pyridine series.

Discovery of 6-Amino-2-{[(1S)-1-methylbutyl]oxy}-9-[5-(1-piperidinyl)pentyl]-7,9-dihydro-8H-purin-8-one (GSK2245035), a Highly Potent and Selective Intranasal Toll-Like Receptor 7 Agonist for the Treatment of Asthma.

Induction of IFNα in the upper airways via activation of TLR7 represents a novel immunomodulatory approach to the treatment of allergic asthma. Exploration of 8-oxoadenine derivatives bearing saturated oxygen or nitrogen heterocycles in the N-9 substituent has revealed a remarkable selective enhancement in IFNα inducing potency in the nitrogen series. Further potency enhancement was achieved with the novel (S)-pentyloxy substitution at C-2 leading to the selection of GSK2245035 (32) as an intranasal development candidate. In human cell cultures, compound 32 resulted in suppression of Th2 cytokine responses to allergens, while in vivo intranasal administration at very low doses led to local upregulation of TLR7-mediated cytokines (IP-10). Target engagement was confirmed in humans following single intranasal doses of 32 of ≥20 ng, and reproducible pharmacological response was demonstrated following repeat intranasal dosing at weekly intervals.