Intuition in pharmaceutical research
“It is by logic that we prove, but by intuition that we discover”
Henri Poincaré, mathematician
One of the pharmaceutical industry’s biggest problems is not being able to break through the 10% maximum limit of molecules that make it through the clinical trials and to the market. In order to overcome this problem, in the past few years high hopes have been placed on AI and algorithms that calculate the molecules’ likelihood of success. However, the latest results do not confirm expectations. The reasons for this failure are to be found in the fact that artificial intelligence represents only half of the equation that leads to innovation. The other half is taken up by human creativity. Understanding how these two factors come together to bring to light new discoveries is the first step in identifying concrete methods for increasing the probability of success. This article explains how creativity works when applied to scientific innovation, with a particular focus on the pharmaceutical industry. Moreover, an approach is suggested tying intuition to analysis, which may help to break through the 10% ceiling.
In 2020, the news that an immune-resistant-bacteria-beating drug had been developed thanks to artificial intelligence went around the world. The head of the research team responsible for the discovery was Regina Barzilay, an artificial intelligence professor at MIT, together with a diverse team of biologists and computer scientists, led by Jim Collins, a professor of bioengineering at MIT.
Barzilay and the team trained an algorithm on more than 2,300 compounds with antimicrobial properties, to find if any inhibited the growth of E. coli, a noxious bacterium. Then the model was applied to around six thousand molecules in the Drug Repurposing Hub and later to more than one hundred million molecules in another database to predict which might work. In early 2020 they struck gold. One molecule stood out. They named it “halicin” after HAL, the renegade computer in 2001: A Space Odyssey.
The discovery of a Superdrug to kill superbugs made headlines around the world. It was hailed as a “video killed the radio star” moment for the superiority of machine over man. “AI Discovers Antibiotics to Treat Drug-Resistant Diseases,” boomed a front-page headline in the Financial Times. But that missed the real story. It wasn’t a victory for artificial intelligence but a success of human cognition.
Artificial intelligence speeds up experimentation but cannot replace human creativity.
“Humans were the ones who selected the right compounds, who knew what they were doing when they gave the material for the model to learn from,” Barzilay explains. People defined the problem, designed the approach, chose the molecules to train the algorithm, and then selected the database of substances to examine. And once some candidates popped up, humans reapplied their biological lens to understand why it worked. 1
Intuition in scientific research
In science, creativity corresponds to intuition. When we think of intuition, the image we usually come up with is that of a flash of illumination where the solution of a problem suddenly reveals itself in all its clarity. Think, for example, of the famous anecdote in which Archimedes dips into a bath tub and, through a flash of genius, figures out the principle of buoyancy. This is what scientists refer to as ‘insight’, and it is rather the exception than the norm. But there is also another kind of intuition that plays a major role in the process of scientific discovery: what researchers call actual ‘intuition’. Unlike insight, intuition is more blurry, does not come with certainties, and it’s more like a vague feeling that a certain idea is the right one. You ‘sense’ that a certain direction is the right direction. This second kind of intuition is what normally drives scientific research, and represents the essence of creativity in every field.
Researchers Viktor Dörfler and Colin Eden interviewed nineteen science Nobel Laureates to try and find out more about creative intuitions in scientific discovery. They came to the strong conclusion that, for the Nobel Laureates they interviewed, no significant research result has been achieved ‘without intuition playing a major part in the process’. 2
A recent study shows that for the Nobel Laureates no significant research result has been achieved without intuition playing a major part in the process.
Prominent examples include the Swedish neuropharmacologist Arvid Carlsson (Nobel Laureate for medicine, 2000), who is best known for his work on the neurotransmitter dopamine, likened the process of drug development to ‘walking in a labyrinth’ with many decision points where the ‘thing is to not jump in the wrong direction too many times’. For Carlsson, creative intuition is the thing that ‘leads your decision in a certain direction’ in spite of the fact that in the early stages the whole picture is very fragmentary. 3
Carlsson came to appreciate the value of intuitive thinking in science from one of his mentors, Bernard Brodie. Brodie is one of the founders of modern pharmacology and Carlsson recalled that he had ‘remarkable intuition. When he sensed that a research area was “hot” he did not hesitate to go into it’. 4
Medicine Nobel Laureate Michael S. Brown (1985) described his team’s experiences of creative intuition as follows: “As we did our work, I think, we almost felt at times that there was almost a hand guiding us. Because we would go from one step to the next, and somehow we would know which was the right way to go. And I really can’t tell how we knew that, how we knew that it was necessary to move ahead. 5 Stanley Cohen (Nobel Laureate of medicine, 1986) his research is guided by a ‘feeling of … “this is an important result” and “Let us follow this path”, I am not always right, but I do have feelings about what is an important observation and what is probably trivial’. 6
The function of intuition, therefore, is to indicate a direction. It allows to ‘sense’ that an idea may be the right one and that it’s worth exploring it. Then come rational thinking and experimentation in order to test if the direction is the correct one. In turn, experimentation leads to new intuitions – and so on, alternating intuition with analysis, until the solution to the problem is arrived at.
The intuition-analysis cycle
The “intuition-analysis” cycle is the basic principle that drives the scientist’s thinking. As polio vaccine inventor Jonas Salk explains: “Reason alone without intuition can easily lead the wrong way. The both are necessary. The way I like to put it is that I might have an intuition about something, I send it over to the reason department. Then after I’ve checked it out in the reason department, I send it back to the intuition department to make sure that it’s still all right.” 7 What Salk calls “the reason department” for a physicist can be the math, for the biologist lab testing, but in either case the two terms of the equation don’t change. As the great mathematician Henri Poincaré pointed out, in the quote at the beginning of this article: “It is by logic that we prove, but by intuition that we discover.”
The “intuition-analysis” dyad is the basic mechanism that drives the scientist’s thinking.
The “intuition-analysis” cycle is iterated until a solution to the problem is reached, as explained by the great French philosopher Henri Bergson 8. Bergson saw the role of intuition as important for arriving at new ideas, after which we should abandon intuition and work on building the body of knowledge, using the new intuitively obtained knowledge. When we begin to ‘feel lost’, he argued that we should get in touch with our intuition again, often undoing what we have done in the deliberative phase, and continue this process in cycles. As eminent philosophy of science scholar Karl Popper 9 said “there is no such thing as a logical method of having ideas, or a logical reconstruction of this process … every discovery contains … ‘a creative intuition’, in Bergson’s sense’.”
Sensing
Sensing is an innovative method that allows to generate intuitions at will on a problem at hand. Sensing starts from the assumption that intuitions emerge in the conscious mind in the form of feelings. If feelings are the channel for intuitions, Sensing teaches how to go back along this channel and extract the information directly from the intuitive mind. When people are faced with a problem to solve, they can get to the heart of it by Sensing … Bam! Once our course trainees learn how to generate intuitions deliberately, they are also taught how to apply this ability to the “intuition-analysis” cycle.
As Tiziana Tonini of Roche says: “With Sensing I learned to solve all kinds of problems in a simple way. When I have a problem I “do Sensing” and this gives me some indication on which way to turn. I act on the basis of these indications (for example, I compare myself with others, I acquire information, etc.). Then, based on the information I have acquired, I act upon it and then I “do Sensing” again, which gives me new direction, on the basis of which I act again, and I continue like this until I solve the problem. In this way the final result is a success, because the thing comes from within me and not from others, and because I moved progressively on the basis of my intuition, checking the results at each step. Plus it’s simple because I don’t have to force myself to come up with ideas; the ideas arise on their own and I just have to execute and correct the direction of the shot.”
Sensing thus makes it possible to teach researchers how to be creative. Before, this was not possible. Anybody with a bit of experience in corporate training knows that creativity in business has until now been driven through brainstorming and the so-called CPS tools, lateral thinking exercises that have the purpose of generating as many ideas as possible. However, these methods are ill-suited to rigorous scientific thinking. Today, Sensing not only makes it possible to train scientists to be intuitive, but also to teach them how to apply intuition concretely to their work.
The Sensing method, for the very first time, makes it possible to train researchers to be intuitive, and to teach them how to apply intuition to scientific research.
Conclusion
Artificial intelligence can greatly speed up experimentation but cannot replace human creativity. The fact that AI has not succeeded in raising the 10% ceiling of molecules that make it through the clinical trials and to the market, is proof of this. The messianic expectations that machines will be able to make decisions instead of us, risks to flatten out of our ability to think. Of course, creativity still remains alive and kicking in start-ups, which are today the main providers of innovation to the more consolidated corporations. But if incumbents want to increase the probability of success, they must also work on creativity.
Sensing represents an innovation in the field of creativity just as AI has been in experimentation. If we put the two factors together – innovation in creativity and innovation in experimentation – we can try to break through the 10% ceiling of innovation, which, so far, artificial intelligence on its own has not been able to raise.
1. K. Cukier, V. Mayer-Schöenberger, Francis de Véricourt. “Framers. Make better decisions in the age of big data”. Ebury Publishing, 2021
2. Dörfler, V., & Eden, C. (2014). Research on intuition using intuition. In Sinclair, M. (Ed.). Handbook of Research Methods on Intuition (pp. 264–276). Cheltenham: Edward Elgar Publishing, p. 266.
3. Sundgren, M., & Styhre, A. (2004). Intuition and pharmaceutical research: the case of AstraZeneca. European Journal of Innovation Management, 7(4): 267–279, p. 276.
4. Carlsson, A. (1998). Arvid Carlsson. In Squire, L. R. (Ed.). The History of Neuroscience in Autobiography (pp. 28–67). San Diego: Academic Press, p. 35.
5. Sadler-Smith, E. (2008). Inside Intuition. Abingdon: Routledge, p. 66.
6. Ibidem.
7. Jonas Salk on “Anatomy of reality. Merging of intuition and reason. (https://johnljerz.com/superduper/tlxdownloadsiteMAIN/id789.html)
8. Bergson, H. “Creative Evolution” (L’Évolution créatrice, 1907). Henry Holt and Company 1911, p. 238–239.
9. Karl Popper, The Logic of Scientific Discovery (New York: Basic Books, 1959), 27-34
10. S. Dehaene, “How we learn.” Penguin, 2020.
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