A novel method for the analysis of drug receptor interactions has been developed and used to explore mechanisms involved in the binding of 4-piperidyl oxazole antagonists to alpha1a-, alpha1b- and alpha1d-adrenoceptors. The method exploits affinity data for a series of organic chemical compounds binding to wild-type and artificially mutated receptors. The receptor sequences and compounds are assigned predictor variables that are correlated to the measured pharmacological activities using partial least-squares projections to latent structures. The predictor variables consist of one descriptor block derived from the chemical properties of the receptors' primary amino acid sequences and another descriptor block derived from the chemical properties of the organic compounds. The cross-terms generated from the two descriptor blocks are also derived. Using this approach, very sturdy models were generated describing the interactions of the chemical compounds with the receptors. Models are useful to predict binding affinity and receptor subtype selectivity of compounds prior to their synthesis, and may find use in rational drug design. Moreover, models also give quantitative information about the interactions of the amino acids of the receptors with the ligands, thereby giving an insight into the molecular mechanisms involved in ligand binding.