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.
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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).
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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.
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Han, H. Taking model pursuit seriously. Euro Jnl Phil Sci 13, 22 (2023). https://doi.org/10.1007/s13194-023-00524-x
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DOI: https://doi.org/10.1007/s13194-023-00524-x