In the Classroom edited by
Computer Bulletin Board
Steven D. Gammon
Conformer Hunting: An Open-Ended Computational Chemistry Exercise That Expresses Real-World Complexity and Student Forethought
University of Idaho Moscow, ID 83844
Kenny B. Lipkowitz* and Daniel Robertson Department of Chemistry, Indiana-Purdue University at Indianapolis, Indianapolis, IN 46202; *
[email protected] Computational chemistry is a multidisciplinary area of science that transcends traditional boundaries separating biology, chemistry, physics, and mathematics. Because the theories, algorithms, and computing machinery are now at the level of sophistication needed to reproduce many types of experiments, a large contingent of non-theorists have been using computational chemistry in their research (1). Not surprisingly, the academic community has been keeping pace with these advances by developing new courses on this topic or by embedding computational exercises into their existing curricula. One aspect of computational chemistry that cuts across subdisciplines in the chemical sciences involves conformational analysis, that is, the study of potential energy surfaces (PESs) where rotations around single bonds take place. Being able to predict the potential energy surface of a molecule a priori is important because that surface dictates the molecule’s shape, its dynamical behavior, and its chemical reactivity; yet for all but the simplest systems, one cannot predict easily (or with great certainty) what those surfaces look like. Several papers published in this Journal explain how students can been taught to map out potential surfaces and to compute the energies of molecular conformations (2, 3). We have found these papers useful for some of our teaching needs, but most of them are somewhat inadequate for many of our educational goals—being limited in scope, containing pedagogical problems (like focusing on how to construct and manipulate chemical structures rather than on student interpretation of results), and lacking many didactic components of learning from which we want our students to benefit (see below). During the past 10 years we have been integrating computational chemistry and molecular modeling into our graduate and undergraduate course offerings and in this paper we describe a class project involving conformational analysis that we have successfully implemented, tested, and modified and that is suitable for either graduate or undergraduate courses. Existing Deficiencies and Pedagogical Needs The published exercises have been developed by university professors to help students understand various aspects of chemistry. Most of those exercises have been described by the authors as being successful for what they were designed to do. However, there are several deficiencies in the existing exercises that make most of them unsuitable for our needs. One major deficiency of most molecular modeling exercises is that automated conformer search strategies are not implemented. Said another way, conformer hunting is not done 206
either in a rigorous fashion or using real-world methodologies. In a few papers dihedral driver calculations are carried out for single-bond rotations and sometimes for two rotatable bonds to map out a PES, but more frequently the conformations generated by the student are guesses that depend only upon their initial inputted geometries. A second deficiency of the existing exercises is that most involve small molecules with few rotatable bonds. Moreover, the examples given in those papers are for molecules having a very limited number of conformations (the largest numbers are 5 conformers in ref 3c, 8 in ref 3e, and 9 in ref 3h). This situation is incongruent with real-world studies of even moderately complex, drug-sized molecules containing multiple rotatable bonds for which hundreds of conformations can exist. The third deficiency with most exercises is that little room exists for students to demonstrate creativity. These exercises typically contain a set of directions, usually for a single molecule selected by the teacher, and calculations are done by the student on that molecule alone. Moreover, an excessive number of publications tend to focus on how to build the molecule under study in the “builder” mode of the software rather than on the results of the calculations. These deficiencies conflict with our pedagogical needs. In our curriculum we wanted our students to (i) be creative, (ii) demonstrate unambiguously their grasp of the topic at hand, (iii) provide as much forethought as afterthought about the experiment, (iv) deal with complex problems that are congruent with real-world problems in chemistry, and (v) maintain their efforts in writing across the curriculum. The following assignment has successfully accomplished these goals. Course Background Conformational analysis with computational methods has been taught in our department at the undergraduate level for the past 5 years and at the graduate level for the past 15 years. The undergraduate exercise is implemented in our firstsemester sophomore-level organic chemistry classes and laboratories. The systems studied in this environment are more restrictive than in our advanced course, the major difference between the two exercises being that the undergraduates are told which system to do their conformational studies on while the graduates must design their own experiments. This paper mainly describes the graduate course. This advanced class is an introductory graduate-level course in organic chemistry that focuses on bonding theory and stereochemistry. Most of the topics described in an early publication in this Journal (4 ) are retained in this advanced
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course curriculum, albeit updated somewhat to take advantage of new methods in computational chemistry. The student population in the graduate course consists of a mixture of senior-level undergraduates (typically 20%) wanting to take advanced classes and first-year graduate students. Of the graduate students enrolled, 25% have selected organic chemistry for a career and the rest are in other subdisciplines of the chemical sciences, most of them being from the divisions of inorganic, biological, and physical chemistry. (The latter group of students take the course because applied theory and computational chemistry/molecular modeling is a major component of the curriculum.) Finally, about one third of the students are usually from local industry and are somewhat older and more experienced than the others in the course. The Student Project The project described herein has been given to our students in the form of a “term paper”, a major assignment equivalent in points to an hour exam. In this open-ended project we have the student answer a simple question: “Which conformer search strategy implemented in Spartan1 is the best?” We do not tell students what “best” means, nor do we indicate to them which molecules should be used to answer this question. This is where student creativity and forethought come into play; students need to tell us which systems they
Figure 1. Part of a complex potential energy surface (PES) illustrating the differences between the computational methods used to explore such surfaces. Quantum and molecular mechanics use energyminimization schemes (geometry optimization). Beginning with a structure near a minimum on the PES, the atoms are moved so that the computed energy can go no lower (large white arrow). Most minimizers seek the nearest minimum on the surface. Molecular dynamics provides kinetic energy, allowing the system to meander over part of the terrain (winding black line). Monte Carlo methods randomly select internal coordinates and energies are computed at those points that are accepted or rejected on the basis of an energy criterion (dropped lines onto the surface).
will select, along with a rationale for their choices, before they begin their task of answering the question (so as to not waste computer time as well as to set them on a productive learning trajectory). It takes a lot of forethought and project planning to address in a meaningful way the question of which search strategy is best. To assist our students we provide them with tutorials and reading materials (described later). The software we use in our sophomore organic chemistry classes as well as in our graduate organic courses is Spartan, but we have used programs like MacroModel (5) in the past. Spartan, like MacroModel, contains a variety of conformational analysis tools suitable for searching conformers of large- and mediumsized organic and inorganic molecules, but other modern software could be used. We assume students have had some introductory experience using the modeling software in the first semester of their sophomore year, but we also provide two tutorials in our molecular modeling laboratory. The first tutorial involves using the software itself and the second is on conformational analysis. In the latter, we describe the “multiple minimum problem” (6 ) and point out that there exist a variety of ways to search a molecule’s PES as illustrated in Figure 1 where we have depicted how several of these methods work. We also have students study a tutorial on the topic of conformational analysis of small and medium sized molecules written by Leach (7). Spartan’s conformer search tools include a grid search, a stochastic search, a genetic algorithm (GA) methodology, and for conformer searching of ring systems, the Osawa corner flapping algorithm and a distance-geometry algorithm (DGEOM). In our experience, focusing on non-ring problems is best because it cuts down on the number of algorithms that need to be tested and it avoids some complex conformational issues associated with rings. Hence we limit our assessment of the best conformer-hunting methodology to (i) grid, (ii) stochastic, and (iii) GA methods, but other techniques such as quenched dynamics or simulated annealing strategies could be evaluated using other software. The real task is for the student to define “best.” To answer this question students must understand both the problems and the goals of conformational searching, as well as the time requirements and limitations of the methodologies used. A fine-grained, systematic search involving multiple rotatable bonds would certainly find all the conformers, but, if this search strategy required 100 years of CPU time, students recognize it might not be the “best” approach to their problem. Likewise, if Metropolis Monte Carlo methods were to locate only the lowest-energy conformations at the expense of higherenergy ones, or if they had a penchant for being unable to locate the global minimum on the PES, this too would not be deemed the best method. To accomplish the task of assessing which search strategy is best we ask our students to first select a particular type of potential energy surface on which their conformational search will take place. Usually an empirical force field is selected, but sometimes semiempirical MO methods have been used, and occasionally some students will compare and contrast QM and MM methods. To carry out the assignment students pick molecules having 3–5 rotatable bonds and carry out a systematic grid search to locate all minima on the PES along
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with their Boltzmann populations. Then, using the same molecule, the same force field or Hamiltonian, and the same geometry optimizer, they compare their results from the other search strategies to the results of the systematic grid search. Although students are not dissuaded from working with floppy molecules, in the lecture part of the course they are made well aware of how the CPU time scales for QM- and MM-based methods and how the number of conformers generally scale with the number of rotatable bonds. Because the students want to finish as quickly as possible, large, floppy systems with more than 4 rotatable bonds are rarely assessed. Some students have defined “best” as being the method that can locate the global minimum most quickly. Others have developed more complex measures of what “best” means, including (i) the algorithm that locates the greatest number of conformers in the least amount of time (regardless of whether or not the global minimum was located), (ii) the algorithm that locates the smallest number of non-redundant conformers, (iii) the algorithm that locates the global minimum along with the greatest number of unique conformers per unit time; and so on. Because the question lacks a simple “yes” or “no” answer, students quickly come to realize they need to provide more than a single case study (more than one molecule) to make their conclusions as compelling as possible. We find that because this exercise involves both forethought and afterthought on the student’s part, it is easy to see which students are able to grasp the concepts of conformational analysis and PESs and which students can not. Although all students are expected to design their projects by themselves, in reality we find that only about a quarter of the students can do this completely by themselves (usually the more mature and bolder industrial scientists). The typical student needs guidance from the instructor and in that regard we encounter a range of student responses. On the one hand we find some students who have good ideas but who are uncertain about their own abilities (in part because they never had to design their own experiments before this) and they are merely seeking assurance from the instructor that their ideas are valid. This group constitutes the vast majority of the class. For them, simply making some suggestions about what type of compounds might be suitable for the project and nurturing their self-confidence is sufficient for success. On the other hand, there are students who in effect want the professor to tell them exactly what to do; they are the minority of the class. This group generally consists of students who are academically ill-prepared and students who are intellectually gifted enough to deal with the issue at hand, but who have always looked for shortcuts in their academic career (in this instance, having the professor do the work for them). For this minority group, the instructor initially takes a hard-line stand indicating that they must design their own experiments, but that stance is softened or fully dismissed in time as it becomes clear which students are unwilling or incapable of coping with the novelty of being creative. Finally, because of our commitment to writing in this department, students are required to write their findings in the format of a JACS article. A handout describing how to do this and how to format information is distributed and a first draft of the paper is required before final submission. The instructor edits the first draft of the manuscript indicating what is both good and bad about it, provides insights about 208
Figure 2. Student responses to a post-project survey. The X’s show the average response and the range of responses is indicated in gray. Students were requested to respond to each of the questions according to the following scale: 1, strongly agree; 2, agree; 3, can’t decide; 4, disagree; and 5, strongly disagree.
what was done correctly or improperly, and makes recommendations to further guide the student to a successful completion of the project. The student is expected to revise the manuscript by making those corrections (and doing more work if necessary) before the final report is handed in. The report is reviewed like a research paper submitted to an ACS journal and grades are based upon that. Both the first draft and the final report are eventually placed in a student portfolio. Assessment There are two assessments of the course. The first is our standard, end-of-the-semester, school of science evaluation that assesses the course in its entirety along with the instructor. The second is an assessment of the new or modified experiment. This latter evaluation consists of 12 questions mailed to the students one or two semesters after the course has ended. We use this as input to fine-tune the exercises. The specific assessment for this class project is given in Figure 2. In this figure we have indicated the average student response with a large, bold X and the distribution of responses with gray bars. It is evident that the project described in this paper is pitched at about the right level and is well received by the students, who feel it is of value to them in their careers, and it should remain as part of the course. Summary Conformer-hunting is a basic technique used in many subdisciplines of chemistry, especially in organic, medicinal, and biological chemistry. Until now relatively few student exercises have addressed that topic, and those that have done so are limited in scope. The exercise described in this paper is suitable for graduate students as well as undergraduates, can be done using large- or small-scale conformer searches depending upon existing computing machinery, and can be done using a variety of modern molecular modeling software because nowadays most of such software contains a variety of search strategies from which to select. We have developed, tested, and successfully implemented a computational chemistry project that focuses on conformer-
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hunting strategies. This project has been successful in our hands because it (i) requires a significant amount of student forethought in addition to afterthought by forcing students to design their own experiments within context, (ii) demonstrates real-world levels of complexity by using molecules having multiple rotatable bonds, (iii) allows for student creativity, which is missing in existing publications, and (iv) emphasizes writing, in the spirit of “writing across the curriculum”.
3.
Note 1. Wavefunction, Inc., 18401 Von Karman, Suite 370, Irvine, CA 92715, USA.
Literature Cited 1. For an assessment of the per cent of papers using computational methods in mainstream chemistry journals see: Reviews in Computational Chemistry, Vol. 8; Lipkowitz, K. B.; Boyd, D. B., Eds.; VCH: New York, 1996; pp v–ix, Preface, Table 1. 2. For papers published in this Journal where molecular conformations are computed as part of the exercise see: Lipkowitz, K. B. J. Chem. Educ. 1984, 61, 1051. Olsen, J. A.; Olsen, R. J. Ibid. 1991, 68, 436. Aduldecha, S.; Akhter, P.; Field, P.; Nagle,
4. 5.
6.
7.
P.; O’Sullivan, E.; O’Connor, K.; Hathaway, B. J. Ibid. 1991, 68, 576. Sauers, R. R. Ibid. 1996, 73, 114. Midland, M. M.; Beck, J. J.; Peters, J. L.; Rennels, R. A.; Asirwatham, G. Ibid. 1994, 71, 897. Martin, N. H. Ibid. 1998, 75, 241. Erickson, L. E.; Morris, K. F. Ibid. 1998, 75, 900. For papers published in this Journal whose focus in exclusively on mapping potential surfaces or on conformational analysis see: (a) Bally, R. A. J. Chem. Educ. 1989, 66, 836. (b) Jarret, R. M.; Sin, N. Ibid. 1990, 67, 153. (c) Biali, S. E. Ibid. 1990, 67, 1038. (d) Buss, D. C.; Fountain, K. R. Ibid. 1993, 70, 295. (e) Fitzgerald, J. P. Ibid. 1993, 70, 988. (f ) Casanova, J. Ibid. 1993, 70, 904. (g) Hanks, T. W. Ibid. 1994, 71, 62. (h) Ringan, N. S.; Grayson, L. Ibid. 1994, 71, 856. (i) Mencarelli, P. Ibid. 1995, 72, 511. Lipkowitz, K. B. J. Chem. Educ. 1982, 59, 595. Mohamadi, F.; Richards, N. G. J.; Guida, W. C.; Liskamp, R.; Lipton, M.; Caufield, C.; Chang, G.; Hendrickson, T.; Still, W. C. J. Comput. Chem. 1990, 11, 440. Scheraga, H. A. In Reviews in Computational Chemistry, Vol. 4; Lipkowitz, K. B.; Boyd, D. B., Eds.; VCH: New York, 1992; Chapter 2, pp 73–142. Leach, A. R. In Reviews in Computational Chemistry, Vol. 2; Lipkowitz, K. B.; Boyd, D. B., Eds.; VCH: New York, 1991; Chapter 1, pp 1–55.
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