One of the joys of compiling and reporting on the bucket list is that we are often reading and describing papers that we did not select ourselves.  That is certainly true of this one which is from 1993 – and it shows.  It is also a revealing insight into how a lot of the progress and overhyping of artificial intelligence and computers in chemistry has come about. Some brilliant folk in computer science who had lived through and driven many of the important developments in artificial intelligence were looking for a scientific problem to apply it to. They stumbled on structure elucidation from mass spectrometry. It is hard to be excited at this remove about the particular application but it is clear that they made a big noise about this application even though they also describe Carl Djerassi’s rather unimpressed response to the program.  However, the general rules suggested by the authors are of pretty general use and interest to those developing scientific software of all kinds:


Lesson 1. The efficiency of the generator is extremely important. It is particularly important that constraints can be applied effectively.

Lesson 2. The use of depth-first search, which provides a stream of candidates, is generally better (in an interactive program) than breadth-first search, in which no candidates emerge for examination until all are generated.

Lesson 3. Planning is in general not simply a nice additional feature but is essential for the solution of difficult problems.

Lesson 4. Every effort to make the program uniform and flexible will be rewarded

Lesson 5. An interactive user interface is not merely a nicety but is essential.

Lesson 6. An interesting extension of the plan-generate-test paradigm could improve its power: search and generation might be combined into a single problem solver.

Lesson 7. Choice of programming language is becoming less of an issue.

Lesson 8. Providing assistance to problem solvers is a more realistic goal than doing their jobs for them.

Lesson 9. Record keeping is an important adjunct to problem solving.

Lesson 10. In order to use a program intelligently, a user needs to understand the program’s scope and limits.

Lesson 11. The context in which problem solving proceeds is essential information for interpreting the solutions

Lesson 12. DENDRAL employs uniformity of representation in two senses: (a) in the knowledge used to manipulate chemical structures, and (b) in the data structures used to describe chemical structures and constraints.


DENDRAL: a case study of the first expert system for scientific hypothesis formation. Lindsay, R. K.; Buchanan, B. G.; Feigenbaum, E. A.; Lederberg, J. Artificial Intelligence. 1993, 61, 209-261.


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