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A new approach to analysis of protein-ligand interactions, termed proteochemometrics, has been developed. Contrary to traditional quantitative structure-activity relationship (QSAR) methods that aim to correlate a description of ligands to their interactions with one particular target protein, proteochemometrics considers many targets simultaneously.

Proteochemometrics thus analyzes the experimentally determined protein-ligand interaction activity data by correlating the data to a complex description of all interaction partners and; in a more general case even to interaction environment and assaying conditions, as well. In this way, a proteochemometric model analyzes an “interaction space,” from which only one cross-section would be contemplated by any one QSAR model.

Proteochemometric models reveal the physicochemical and structural properties that are essential for protein-ligand complementarity and determine specificity of molecular interactions. From a drug design perspective, models may find use in the design of drugs with improved selectivity and in the design of drugs for multiple targets, such as mutated proteins (e.g., drug resistant mutations of pathogens).

In this thesis, a general concept for creating of proteochemometric models and approaches for validation and interpretation of models are presented. Different types of physicochemical and structural description of ligands and macromolecules are evaluated; mathematical algorithms for proteochemometric modeling, in particular for analysis of large-scale data sets, are developed. Artificial chimeric proteins constructed according to principles of statistical design are used to derive high-resolution models for small classes of proteins.

The studies of this thesis use data sets comprising ligand interactions with several families of G protein-coupled receptors. The presented approach is, however, general and can be applied to study molecular recognition mechanisms of any class of drug targets.