version date: 1 December 2006 macromolecule. As a consequence, a comprehensive study of drug structure-activity relationships(SARs) can be developed and provide the proper identification of a 3D pharmacophore model for a rational drug design. Although structure-based design is now a medicinal chemistry routine approach, there are still difficulties in teaching fundamental concepts to undergraduate pharmacy students, such as those related to the molecular recognition process [1, 2 An active-learning strategy in medicinal chemistry involves the incorporation of molecular modeling techniques to assist third-year undergraduate students in the understanding of structure-activity principles. This paper focuses on the use of computational chemistry and the protein data bank(PDB), accessed from the Web site http:/www.rcsb.org/pdb,tounderstandandpredictthechemicalandmolecularbasis involved in the drug-receptor interactions. a comprehensive study of SArs comprises three approaches. The first one involves comparative analysis of antimetabolite drugs and the corresponding metabolites(named substrates), by representing, visualizing, and superimposing their 3D structures, obtained by minimization processes and molecular alignment techniques. Numerical properties of these molecules are then calculated, the most common being molecular energies and physical constants as partition coefficients, dipolar moment, molecular volume, and interatomic distance. Finally, particular structural feature between substrate and antimetabolite are explored by assessing the electrostatic and geometric patterns required for chemical interaction in the active site of the target molecule, obtained from PDB. Table 1 lists therapeutic targets of interest, describing the enzymes and their corresponding substrates, some PDB files and antimetabolites currently used as antineoplastic, anti-HIV, antibiotic, antihipertensive, anti-inflammatory, cholinergic, and hipolipemic drugs 3 <www.iupac.org/publications/cd/medicinalchemistry/> 22 macromolecule. As a consequence, a comprehensive study of drug structure–activity relationships (SARs) can be developed and provide the proper identification of a 3D pharmacophore model for a rational drug design. Although structure-based design is now a medicinal chemistry routine approach, there are still difficulties in teaching fundamental concepts to undergraduate pharmacy students, such as those related to the molecular recognition process [1,2]. An active-learning strategy in medicinal chemistry involves the incorporation of molecular modeling techniques to assist third-year undergraduate students in the understanding of structure–activity principles. This paper focuses on the use of computational chemistry and the protein data bank (PDB), accessed from the Web site http://www.rcsb.org/pdb, to understand and predict the chemical and molecular basis involved in the drug–receptor interactions. A comprehensive study of SARs comprises three approaches. The first one involves comparative analysis of antimetabolite drugs and the corresponding metabolites (named substrates), by representing, visualizing, and superimposing their 3D structures, obtained by minimization processes and molecular alignment techniques. Numerical properties of these molecules are then calculated, the most common being molecular energies and physical constants as partition coefficients, dipolar moment, molecular volume, and interatomic distance. Finally, particular structural features between substrate and antimetabolite are explored by assessing the electrostatic and geometric patterns required for chemical interaction in the active site of the target molecule, obtained from PDB. Table 1 lists therapeutic targets of interest, describing the enzymes and their corresponding substrates, some PDB files and antimetabolites currently used as antineoplastic, anti-HIV, antibiotic, antihipertensive, anti-inflammatory, cholinergic, and hipolipemic drugs [3]. <www.iupac.org/publications/cd/medicinal_chemistry/> version date: 1 December 2006