Exploring classification strategies with the CoEPrA 2006 contest.

Motivation: In silico methods to classify compounds as potential drugs that bind to a specific target become increasingly important for drug design. To build classification devices training sets of drugs with known activities are needed. For many such classification problems, not only qualitative but also quantitative information of a specific property (e.

Bound Ligand Conformer Revealed by Flexible Structure Alignment in Absence of Crystal Structures: Indirect Drug Design Probed for HIV-1 Protease Inhibitors.

In the absence of structural knowledge on the target protein, the bound ligand conformer (BLC) can be constructed approximately by an indirect drug-design approach that uses a set of ligands binding to the same target. Once the bound ligand conformer (BLC) is known, different strategies of drug design can be pursued. The indirect drug-design approach of the present study is based on the assumption that a set of ligands with chemically different architecture binding to the same target protein carry hidden information of their corresponding true BLCs.

Peptide binding at class I major histocompatibility complex scored with linear functions and support vector machines.

We explore two different methods to predict the binding ability of nonapeptides at the class I major histocompatibility complex using a general linear scoring function that defines a separating hyperplane in the feature space of sequences. In absence of suitable data on non-binding nonapeptides we generated sequences randomly from a selected set of proteins from the protein data bank. The parameters of the scoring function were determined by a generalized least square optimization (LSM) and alternatively by the support vector machine (SVM).