Abstract
This paper aims to develop an account of the pursuitworthiness of models based on a view of models as epistemic tools. This paper is motivated by the historical question of why, in the 1960s, when many scientists hardly found QSAR models attractive, some pharmaceutical scientists pursued Quantitative Structure–Activity Relationship (QSAR) models despite the lack of potential for theoretical development or empirical success. This paper addresses this question by focusing on how models perform their heuristic functions as epistemic tools rather than as potential theories. I argue that models perform their heuristic function by “constructing” phenomena from data in the sense that they allow the model users who interact with the medium of the models to recognise the phenomena as such. The constructed phenomena assist model users in identifying which conditional hypotheses that are focused on low-level regularities concerning entities such as chemical compounds are more “testworthy,” a concept that links the costs associated with hypothesis testing with the fertility of the hypothesis.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
For example, an article collection titled “Pursuitworthiness in Scientific Inquiry” will be published by Studies in History and Philosophy of Science in 2023, edited by Jamie Shaw and Dunja Šešelja; the articles of the collection have been released since 2022.
The most recent papers published in the 2010s and 2020s do not respond directly to the logical empiricists. Most of them, however, discuss scientific pursuit in light of Laudan and others’ criticisms of the distinction between discovery and justification.
In the OECD, for example, industry accounts for around 70% of R&D expenditures (OECD, 2021).
Given certain reports referencing Hansch and QSAR’s low reputation in the 1960s—for example, a Merck researcher deemed Hansch’s work “ridiculous” in 1965 (Hansch, 2003, p. 621), and no one at Abbott knew about Hansch’s work until 1967 (Martin, 2018, p. 817)—it is plausible to say that it was not until the mid-1970s that Hansch and his QSAR became widely recognised as promising. A historical overview of QSAR models is provided in Section 3.
For more on the distinction between high-level theories and low-level regularities, see, e.g., Hacking (1983).
This is supported by an interview with George Hitchings, one of the 1988 Nobel laureates who won the award for his contribution to drug design (Altman, 1988). According to him, the field of chemotherapy in the 1940s was divided between “fundamentalists,” who focused on fundamental theories of physiology and biochemistry, and “screeners,” who screened a vast number of random compounds. His research group thought that “some kind of middle course might be possible, a course that would generate basic information which chemotherapy could then exploit.” In other words, what was essential for his research group was not high-level theories in and of themselves but rather the knowledge that could be applied practically.
While the distinction between the “trial-and-error” approach and “rational drug design” is widely recognised, criticisms also exist against this dichotomy (e.g., Lesch, 2008).
Since the 1990s, the development of combinatorial chemistry and high-throughput screening techniques has reduced the cost of synthesis-and-testing.
Hansch could use a computer donated to the Chemistry Department by a College trustee in 1961 (Hansch, 2011, p. 502).
Hansch acknowledges SK&F for their financial help (Hansch, 1969, p. 239).
For example, at the Third Rhone-Poulenc Round Table in November 1982, scientists identified QSAR as their most preferred optimization technique for drug design (Jolles & Wooldridge, 1984, pp. 242–244).
Some stories in Hansch’s memoir reflect his poor reputation at the time. For example, fearing that no good scientist in the United States would want to collaborate with him at the small liberal arts college, he focused on finding a foreign postdoctoral associate willing to visit the country (Hansch, 2011, p. 502). Furthermore, some pharmaceutical and pesticide research directors mocked his QSAR research (ibid., p. 497).
To clarify, while the testworthiness and the pursuitworthiness of hypotheses are related, they are not identical. Testworthiness is a sub-concept of pursuitworthiness that focuses on testing practices, among other pursuit-related practices.
Gelfert claims that there are several distinct aspects of exploratory models that can contribute to the search for target phenomena. They include: as “starting points for further inquiry,” providing “proofs of principle,” providing “potential explanations,” and the “search for” potential target (Gelfert, 2018, pp. 9–12). It should be noted, however, that this does not imply that an exploratory model should not have target phenomena. Cope and Hardy’s exploratory models, for example, provided possible explanations for two molecular rearrangements called the Claisen and Cope rearrangements (Fisher, 2006; Gelfert, 2016, pp. 87–93).
The fact that scientists often test hypotheses in which they have a high level of confidence might appear to refute my claim here. However, we should note that this type of testing typically occurs in the later stages of research, such as when the chemical properties of compounds that a scientist desires are highly specified due to previous testing of their analogues. In this case, because the scientist has already specified the desired properties of compounds to some extent, the scope of interests may be limited to a certain range of hypotheses with high confidence. Yet, even if the scientist has high confidence in these hypotheses, it is necessary for them to have some degree of uncertainty in order to be testworthy; if the scientist already knows the testing results for certain prior to the actual testing, they cannot learn anything new from the testing. More specifically, when there are two hypotheses A and B concerning each corresponding chemical compound, it is not that A is more testworthy than B because the researcher has a higher confidence in A than in B. Rather than that, a more plausible explanation is that only A is considered since only A, and not B, fits the scope that the researcher specified, and A is testworthy because the scientist still lacks confidence in A to some extent. But if A and B all fit the scope of the researcher and all things being equal, the lack of confidence would make B more testworthy than A.
In a regression analysis, as a hypothesis becomes more “far” from the other hypotheses used to estimate the regression coefficient, the prediction about the hypothesis becomes more uncertain, because the prediction will exceed the confidence interval; Yvonne C. Martin, a medicinal chemist, describes the distance between hypotheses as the separation of physical properties between drug candidates. She states, “[d]epending on the difficulty of synthesis and testing one should consider including enough analogs that there would be a good separation of the relevant physical properties.” (Martin, 1978, p. 268).
References
Adam, M. (2005). Integrating research and development: The emergence of rational drug design in the pharmaceutical industry. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 36(3), 513–537. https://doi.org/10.1016/j.shpsc.2005.07.003
Altman, L. K. (1988). 3 drug pioneers win nobel in medicine. The New York Times. https://www.nytimes.com/1988/10/18/science/3-drug-pioneers-win-nobel-in-medicine.html. Accessed 13 Jan 2022
Bailer-Jones, D. M. (2009). Scientific models in philosophy of science. University of Pittsburgh Press.
Bogen, J., & Woodward, J. (1988). Saving the phenomena. Philosophical Review, 97(3), 303–352. https://doi.org/10.2307/2185445
Boyd, D. B. (2007). How computational chemistry became important in the pharmaceutical industry. In K. B. Lipkowitz, & T. R. Cundari (Eds.), Reviews in computational chemistry (Vol. 23, 401–451). John Wiley & Sons.
Brooks, D. A., & Gmyrek, D. P. (2011). Computers come into pharmacy practice: The first retail pharmacy computer system in the state of Virginia. Pharmacy in History, 53(2/3), 113–115.
Craig, P. N. (1971a). Comparison of batch and time-sharing computer runs for correlating structures and bioactivity by the Hansch method. Journal of Chemical Documentation, 11(3), 160–162. https://doi.org/10.1021/c160042a009
Craig, P. N. (1971b). Interdependence between physical parameters and selection of substituent groups for correlation studies. Journal of Medicinal Chemistry, 14(8), 680–684. https://doi.org/10.1021/jm00290a004
Craig, P. N., Caldwell, H. C., & Groves, W. G. (1970). Spasmolytics. 3. 3-Tropanyl 2, 3-diarylacrylates. A case history of pi-sigma structure-function correlation. Journal of medicinal chemistry, 13(6), 1079–1081. https://doi.org/10.1021/jm00300a014
Currie, A. (2017). From models-as-fictions to models-as-tools. Ergo: An Open Access Journal of Philosophy, 4(27), 759–781. https://doi.org/10.3998/ergo.12405314.0004.027
DiMarco, M., & Khalifa, K. (2022). Sins of inquiry: How to criticize scientific pursuits. Studies in History and Philosophy of Science, 92, 86–96. https://doi.org/10.1016/j.shpsa.2021.12.008
Fisher, G. (2006). The autonomy of models and explanation: Anomalous molecular rearrangements in early twentieth-century physical organic chemistry. Studies in History and Philosophy of Science Part A, 37(4), 562–584. https://doi.org/10.1016/j.shpsa.2006.09.009
Fisher, G., Gelfert, A., & Steinle, F. (2021). Exploratory models and exploratory modeling in science: Introduction. Perspectives on Science, 29(4), 355–358. https://doi.org/10.1162/posc_e_00374
French, S. (1995). The esperable uberty of quantum chromodynamics. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 26(1), 87–105. https://doi.org/10.1016/1355-2198(95)00006-f
French, S. (1997). Partiality, pursuit and practice. In M. L. Dalla Chiara, K. Doets, D. Mundici, & J. van Benthem (Eds.), Structures and norms in science: Proceedings of the 10th international congress on logic, methodology and philosophy of science, 19–25 August 1995 (pp. 35–52). Kluwer.
Gambardella, A. (1995). Science and innovation: The US pharmaceutical industry during the 1980s. Cambridge University Press.
Gavroglu, K., & Simões, A. (2012). Neither physics nor chemistry: A history of quantum chemistry. MIT Press.
Gelfert, A. (2016). How to do science with models: A philosophical primer. Springer.
Gelfert, A. (2018). Models in search of targets: Exploratory modelling and the case of Turing patterns. In A. Christian, D. Hommen, N. Retzlaff, & G. Schurz (Eds.), Philosophy of science: Between natural sciences, social sciences, and humanities (pp. 245–271). Springer.
Hacking, I. (1983). Representing and intervening: Introductory topics in the philosophy of natural science. Cambridge University Press.
Hansch, C. (1969). Quantitative approach to biochemical structure-activity relationships. Accounts of Chemical Research, 2(8), 232–239. https://doi.org/10.1021/ar50020a002
Hansch, C. (2003). QSAR and the role of luck in research. Il Farmaco, 58, 621–623. https://doi.org/10.1016/s0014-827x(03)00175-7
Hansch, C. (2011). The advent and evolution of QSAR at Pomona College. Journal of Computer-Aided Molecular Design, 25(6), 495–507. https://doi.org/10.1007/s10822-011-9444-y
Hansch, C., & Anderson, S. M. (1967). The structure-activity relationship in barbiturates and its similarity to that in other narcotics. Journal of Medicinal Chemistry, 10(5), 745–753. https://doi.org/10.1021/jm00317a001
Hansch, C., & Fujita, T. (1964). p-σ-π Analysis: A method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society, 86(8), 1616–1626. https://doi.org/10.1021/ja01062a035
Hansch, C., Maloney, P. P., Fujita, T., & Muir, R. M. (1962). Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature, 194(4824), 178–180. https://doi.org/10.1038/194178b0
Hansch, C., Steward, A. R., Anderson, S. M., & Bentley, D. (1968). The parabolic dependence of drug action upon lipophilic character as revealed by a study of hypnotics. Journal of Medicinal Chemistry, 11(1), 1–11. https://doi.org/10.1021/jm00307a001
Haueis, P., & Kästner, L. (2022). Mechanistic inquiry and scientific pursuit: The case of visual processing. Studies in History and Philosophy of Science, 93, 123–135. https://doi.org/10.1016/j.shpsa.2022.03.007
Hesse, M. B. (1970). Models and analogies in science (2nd ed.). University of Notre Dame Press.
Jolles, G., & Wooldridge, K. R. H. (Eds.). (1984). Drug design: Fact or fantasy? Academic Press.
Knuuttila, T. (2005). Models, representation, and mediation. Philosophy of Science, 72(5), 1260–1271. https://doi.org/10.1086/508124
Knuuttila, T. (2011). Modelling and representing: An artefactual approach to model-based representation. Studies in History and Philosophy of Science Part A, 42(2), 262–271. https://doi.org/10.1016/j.shpsa.2010.11.034
Knuuttila, T., & Boon, M. (2011). How do models give us knowledge? The case of Carnot’s ideal heat engine. European Journal for Philosophy of Science, 1(3), 309–334. https://doi.org/10.1007/s13194-011-0029-3
Laudan, L. (1977). Progress and its problems: Towards a theory of scientific growth. University of California Press.
Lesch, J. E. (2008). 2008 Kremers Award Lecture: Dreams of reason: Historical perspective on rational drug design. Pharmacy in History, 50(4), 131–139.
MacLeod, M. (2016). Heuristic approaches to models and modeling in systems biology. Biology and Philosophy, 31(3), 353–372. https://doi.org/10.1007/s10539-015-9491-1
Martin, Y. C. (1978). Quantitative drug design: A critical introduction. Marcel Dekker.
Martin, Y. C. (2012). Development of QSAR. In D. J. Livingstone & A. M. Davis (Eds.), Drug design strategies: Quantitative approaches (pp. 60–87). The Royal Society of Chemistry.
Martin, Y. C. (2018). How medicinal chemists learned about log P. Journal of Computer-Aided Molecular Design, 32, 809–819. https://doi.org/10.1007/s10822-018-0127-9
McMullin, E. (1976). The fertility of theory and the unit for appraisal in science. In R. S. Cohen, P. K. Feyerabend, & M. W. Wartofsky (Eds.), Essays in memory of Imre Lakatos. Boston studies in the philosophy of science (Vol. 39, pp. 395–432). Reidel.
Morgan, M. S., & Morrison, M. (Eds.). (1999). Models as mediators: Perspectives on natural and social science. Cambridge University Press.
Nickles, T. (2006). Heuristic appraisal: Context of discovery or justification? In J. Schickore & F. Steinle (Eds.), Revisiting discovery and justification: Historical and philosophical perspectives on the context distinction (pp. 159–182). Springer.
Nyrup, R. (2015). How explanatory reasoning justifies pursuit: A Peircean view of IBE. Philosophy of Science, 82(5), 749–760. https://doi.org/10.1086/683262
Nyrup, R. (2020). Of water drops and atomic nuclei: Analogies and pursuit worthiness in science. The British Journal for the Philosophy of Science, 71(3), 881–903. https://doi.org/10.1093/bjps/axy036
OECD. (2021). OECD science, technology and innovation outlook 2021: Times of crisis and opportunity. OECD Publishing. https://doi.org/10.1787/75f79015-en
Popper, K. (1959 [1934]). The logic of scientific discovery. Hutchinson.
Quirke, V. (2006). Putting theory into practice: James Black, receptor theory and the development of the beta-blockers at ICI, 1958–1978. Medical History, 50(1), 69–92. https://doi.org/10.1017/S0025727300009455
Schummer, J. (2021). Knowing-through-making in chemistry and biology: A study of comparative epistemology. HYLE–International Journal for Philosophy of Chemistry, 27, 117–142.
Selassie, C., & Verma, R. P. (2010). History of quantitative structure-activity relationships. In D. J. Abraham & D. P. Rotella (Eds.), Burger’s medicinal chemistry, drug discovery, and development (7th ed., pp. 1–96). John Wiley & Sons Inc.
Šešelja, D., & Straßer, C. (2014). Epistemic justification in the context of pursuit: A coherentist approach. Synthese, 191(13), 3111–3141. https://doi.org/10.1007/s11229-014-0476-4
Šešelja, D., Kosolosky, L., & Straßer, C. (2012). The rationality of scientific reasoning in the context of pursuit: Drawing appropriate distinctions. Philosophica, 86, 51–82.
Shan, Y. (2020). Doing integrated history and philosophy of science: A case study of the origin of genetics. Springer. https://doi.org/10.1007/978-3-030-50617-9
Simon, J. (2012). Chemistry and pharmacy: A philosophical inquiry into an evolving relationship. In A. I. Woody, R. F. Henry, & P. Needham (Eds.), Handbook of the philosophy of science (Vol. 6, 519–530). Amsterdam.
Unger, S. H. (1980). Consequences of the Hansch paradigm for the pharmaceutical industry. In E. J. Ariëns (Ed.), Medicinal chemistry (Vol. 9, pp. 47–119). Academic Press.
Veldstra, H. (1953). The relation of chemical structure to biological activity in growth substances. Annual Review of Plant Physiology, 4(1), 151–198.
Woodward, J. F. (2011). Data and phenomena: A restatement and defense. Synthese, 182(1), 165–179. https://doi.org/10.1007/s11229-009-9618-5
Acknowledgements
I especially thank Grant Fisher for his invaluable guidance and helpful suggestions for writing and improving this paper. I also thank Dunja Šešelja and two anonymous reviewers for their constructive comments on the earlier draft of this paper.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical approval
Not applicable.
Informed consent
Not applicable.
Competing interests
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Han, H. Taking model pursuit seriously. Euro Jnl Phil Sci 13, 22 (2023). https://doi.org/10.1007/s13194-023-00524-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13194-023-00524-x