Performance of 3D-database molecular docking studies into homology models.

The performance of docking studies into protein active sites constructed by homology model building was investigated using CDK2 and factor VIIa screening data sets. When the sequence identity between model and template near the binding site area is greater than approximately 50%, roughly 5 times more active compounds are identified than would be found randomly. This performance is comparable to docking to crystal structures.

Coupling structure-based design with combinatorial chemistry: application of active site derived pharmacophores with informative library design.

Protein structural information is combined with combinatorial library design in the following protocol. Active site maps are generated from protein structures. All possible 2-, 3- and 4-point pharmacophores are enumerated from the active site map and encoded as bit strings. The pharmacophores define a design space that can be used to select compounds using an informative library design tool. The method was evaluated against a collection of compounds assayed previously against a cyclin-dependent kinase target, CDK-2, starting with 23 X-ray co-crystal structures. Performance was assessed based on the number of active scaffolds selected after four rounds of iterative informative library design. The method selects compounds from 12 out of the 15 active scaffolds from the CDK-2 library and outperforms a two-dimensional similarity search and docking calculations.

Using ensembles to classify compounds for drug discovery.

This paper introduces Signal, a novel method for classifying activity against a small molecule drug target. Signal creates an ensemble, or collection, of meaningful descriptors chosen from a much larger property space. The method works with a variety of descriptor types, including fingerprints that represent four-point pharmacophores or shape descriptors. It also exploits information from both active and inactive compounds and generates predictive models suitable for high throughput screening data analysis. Given the fingerprints and activity data for a set of compounds, Signal is a two step process. The first step is to Evaluate the Descriptors: for each descriptor in the fingerprint, quantify and rank the correlation between the activity of the compounds and the presence of that descriptor. The second step is to Create an Ensemble Model: use the high ranking descriptors to create a model of activity against the biological target. For the first step, two possible ranking strategies were investigated: mutual information and chi-square. For the second step, two types of ensemble models were investigated: high ranking and a novel method called high ranking set cover. Of the four possible pairings, the combination of chi-square and high ranking set cover performed the best on a Thrombin data set.