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The living world is a rich source of chemicals with many medicines, dyes, flavorings, and foodstuffs having their origins in compounds produced by plants. We describe a chemical trail through the plant holdings of the Cambridge University Botanic Gardens. Visitors to the gardens are provided with a laminated trail guide with 22 stopping points marked on a map. For each of the 22 familiar plants, the principal chemical component(s) and their applications are identified. The virtual trail on the gardens Web site provides additional chemical information including links to the experimentally determined three-dimensional structures of the molecules from the Cambridge Structural Database. Visitors to the gardens with quick response (QR) code readers on their mobile phones will also be able to access this enhanced chemical information digitally. Our hope is that the trails (actual and virtual) will take advantage of students’ inherent interest in the chemistry of the natural world and encourage them to learn mor...

A clear understanding and appreciation of noncovalent interactions, especially hydrogen bonding, are vitally important to students of chemistry and the life sciences, including biochemistry, molecular biology, pharmacology, and medicine. The opportunities afforded by the IsoStar knowledge base of intermolecular interactions to enhance the educational experience of students are summarized. Derived from experimentally determined crystal structures in the Cambridge Structural Database and Protein Data Bank, IsoStar contains geometrical and spatial information on nearly 30,000 different types of interactions in small molecules and proteins.

Parts 1 and 2 of this series described the educational value of experimental three-dimensional (3D) chemical structures determined by X-ray crystallography and retrieved from the crystallographic databases. In part 1, we described the information content of the Cambridge Structural Database (CSD) and discussed a representative teaching subset of ca. 500 CSD structures that have been selected for their educational relevance. In part 2, we exemplified the value of the CSD teaching subset by describing four worked examples of their use in a teaching context. Although the CSD teaching subset and its associated learning modules provide a major resource for chemical educators, there are many cases where the full CSD System is essential to make an educational point. In part 3, we describe the complete CSD System and its associated software and highlight the extended range of discovery-based learning opportunities this affords. Here, we illustrate a number of teaching examples that take advantage of the massive structural information content of the complete CSD System to broaden and enhance the chemical education experience, including, for example, studies of mean molecular dimensions, stereochemistry and conformations, metal coordination sphere geometries, hydrogen bonding and other supramolecular phenomena, and reaction pathways.

Parts 1 and 2 of this series described the educational value of experimental three-dimensional (3D) chemical structures determined by X-ray crystallography and retrieved from the crystallographic databases. In part 1, we described the information content of the Cambridge Structural Database (CSD) and discussed a representative teaching subset of ca. 500 CSD structures that have been selected for their educational relevance. In part 2, we exemplified the value of the CSD teaching subset by describing four worked examples of their use in a teaching context. Although the CSD teaching subset and its associated learning modules provide a major resource for chemical educators, there are many cases where the full CSD System, now covering more than 500,000 crystal structures, is essential to make an educational point. This is particularly true when introducing students to variance in real experimental observations, for example, where many hundreds of observations are required to generate statistically meaningful trends from the structural data or simply to introducing students to the search and manipulation of data that are commonly available in large chemical and biochemical databases. Here, we describe the complete CSD System and its associated software and highlight the extended range of discovery-based learning opportunities this affords.

Knowledge of the three-dimensional (3D) nature of chemical compounds is fundamental to the education of every chemist. In this context, the use of experimentally measured 3D structure, as opposed to idealized computationally generated models, is of great pedagogical value. Five hundred structures that have important applications in chemistry and chemical education were carefully selected from the Cambridge Structural Database (CSD) of over half a million small organic and metal−organic crystal structures determined experimentally by X-ray diffraction. The 500-structure teaching subset is available to educators and students via a simple Web interface, with embedded 3D visualization, at no cost. This unique teaching resource provides not only the 3D structure of compounds but also additional chemical information (compound name, molecular formula, etc.) and full publication details with links to the primary literature. In this article, we summarize the composition of the teaching subset and describe how it c...

A series of online interactive teaching units have been developed that illustrate the use of experimentally measured three-dimensional (3D) structures to teach fundamental chemistry concepts. The units integrate a 500-structure subset of the Cambridge Structural Database specially chosen for their pedagogical value. The units span a number of key chemical concepts (aromaticity, ring strain and conformation, stereochemistry and chirality, VSEPR, and hapticity) and guide students via a Web-based interface to visualize and interact with a diverse range of molecules. The units were designed to help students develop a visual understanding of 3D chemical structure and gain an appreciation of the unique advantages afforded by working with experimentally measured data.