Kallikrein 5 inhibitors identified through structure based drug design in search for a treatment for Netherton Syndrome.

Netherton syndrome (NS) is a rare and debilitating severe autosomal recessive genetic skin disease with high mortality rates particularly in neonates. NS is caused by loss-of-function SPINK5 mutations leading to unregulated kallikrein 5 (KLK5) and kallikrein 7 (KLK7) activity. Furthermore, KLK5 inhibition has been proposed as a potential therapeutic treatment for NS. Identification of potent and selective KLK5 inhibitors would enable further exploration of the disease biology and could ultimately lead to a treatment for NS. This publication describes how fragmentation of known trypsin-like serine protease (TLSP) inhibitors resulted in the identification of a series of phenolic amidine-based KLK5 inhibitors 1. X-ray crystallography was used to find alternatives to the phenol interaction leading to identification of carbonyl analogues such as lactam 13 and benzimidazole 15. These reversible inhibitors, with selectivity over KLK1 (10-100 fold), provided novel starting points for the guided growth towards suitable tool molecules for the exploration of KLK5 biology.

Drug rings database with web interface. A tool for identifying alternative chemical rings in lead discovery programs.

This paper describes the development of a drug rings database and Web-based search tools. The database contains ring structures from both corporate and commercial databases, along with characteristic descriptors including frequency of occurrence as an indicator of synthetic accessibility and calculated property and geometric parameters. Analysis of the rings in several major databases is described, with illustrations of applications of the database in lead discovery programs where bioisosteres and geometric isosteres are sought.

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.