Field of Science

Cognitive biases in drug discovery, part 2: Anchoring, availability and representativeness

In the last post, I talked about how cognitive biases would be especially prevalent in drug discovery and development because of the complex, information-poor, tightly time-bound and financially-incentivized nature of the field. I talked about confirmation bias which riddles almost all human activity and which can manifest itself in drug discovery in the form of highlighting positive data for one’s favorite belief, metric or technique and rejecting negative data that does not agree with this belief. 

In this post, I will mention a few more important cognitive biases. All of them are classic examples of getting carried away by limited patches of data and ignoring important information; often information on much larger samples. It’s worth noting that not all of them are equally important; a bias that’s more applicable in other parts of life may be less applicable in drug discovery, and vice versa. It’s also interesting to see that a given case may present more than one bias; because the human mind operates in multiple modes, biases often overlap. In the next post we will look at a few more biases related to statistics and comparisons.

Anchoring: Anchoring is the tendency to rely too much on one piece of information or trait, especially if it appears first. In some sense it’s a ubiquitous phenomenon, and it can also be subtle; it can be influenced by random things we observe and hear. A classic anchoring experiment was done by Kahneman and Tversky who showed participants a spinning wheel that would randomly settle on a number. After the spinning wheel stopped, the participants were asked what percentage of African countries are part of the U.N. It turned out that the percentage quoted by the participants was correlated to the random, unrelated number they saw on the wheel; if they saw a larger number they quoted a larger percentage, and vice versa. One important feature of the anchoring effect that this experiment demonstrated was that it involves random numbers or phenomena that can be completely irrelevant to the issue at hand.

It’s hard to point to specific anchoring biases in drug discovery, but one thing we know is that scientists can be skewed by numbers all the time, especially if the numbers are promising and seem very accurate. For instance, being biased by sparse in vitro affinity data for some early hits, leads or series can blind you to optimization of downstream properties. People sometimes come around, but I have seen even experienced medicinal chemists get obsessed with early leads with very good affinities but poor properties. In general, random promising numbers relating to affinity, properties, clinical data etc. for particular sets of compounds can lead one to believing that other similar compounds will have similar properties, or that those numbers are very relevant to begin with.

As has been well-documented, “similarity” itself can be a bias since every chemist for instance will look at different features of compounds to decide whether they are similar or not. Objective computational similarity comparisons can diminish this bias a bit, but since there’s no right way of deciding what the “perfect” computational similarity measure is either (and there’s plenty of misleading similarity metrics), this solution carries its own baggage.

You can also be carried away by measurements (often done using fancy instrumentation) that can sound very accurate; in reality, they are more likely to simply be precise. This problem is a bigger subset of problems related to what is called “technological solutionism”. It is the habit of believing in data when it’s generated by the latest and greatest new experimental or computational technique. This data can anchor our beliefs about drug behavior and lead us to extrapolate when we shouldn’t. The key questions to ask in this regard are: Are the numbers being measured accurate? Do the numbers actually measure the effect we think they do and is the effect real and statistically significant? Is the effect actually relevant to my hypothesis or conclusion? That last question is probably the most important and not asking it can lead you to squander a lot of time and resources.

Availability heuristic: A bias related to anchoring is availability. This is the tendency to evaluate new information based on information - especially recent information - that can be easily recalled. In case of drug discovery, easily recalled information can include early stage data, data that’s simply easier to gather, data that’s “popular” or data that’s simply repeated enough number of times, in the literature or by word of mouth. There are countless reasons and why certain information is easily recalled while other information is not. They can also be related to non-scientific variables like emotional impact. Were you feeling particularly happy or sad when you measured a particular effect? Was the effect validated by groupthink and did it therefore make you feel vindicated? Was the piece of data described by an “important” person who you admire? All these factors can contribute to fixing a certain fact or belief in our minds. Availability of specific information can cement that information as the best possible or most representative information.

Everyone is biased by successful projects they have worked on. They may recall a particular functional group or synthetic reaction or computational technique that worked for them and believe that it will work for other cases. This is also an example of confirmation bias, but the reason it’s an availability heuristic hinges on the fact that other information - and most notably information that can counter one’s beliefs - is not easily available. Most of the times we report positive results and not negative ones; this is a general problem of the scientific literature and research policy. Sometimes gathering enough data that would tweak the availability of the result is simply too expensive to do. That’s understandable, but it also means that we should be more wary about what we choose to believe.

Finally, the availability heuristic is particularly strong when a recent decision leads to an important consequence; perhaps installing a fluorine in your molecule suddenly led to improved pharmacokinetics, or using a certain formulation led to better half lives in patients. It is then tempting to believe that the data that was available is the data that’s generalizable, especially when it has had a positive emotional impact on your state of mind.

Representativeness: The availability bias is also closely related to the representativeness fallacy. In one sense the representativeness fallacy reflects a very common failing of statistical thinking: the tendency to generalize to a large sample based on a representative sample. For instance, a set of “rules” for druglike behavior may have been drawn from a limited set of studies. It would then be tempting to think that those rules applied to everything that was not tested in those studies, simply on the basis of similarity to the cases that were tested. Representativeness can manifest itself in the myriad definitions of “druglike” used by medicinal chemists as we all as metrics like ligand efficiency.

A great example of representativeness comes from Tversky and Kahneman’s test involving personality traits. Consider the following description of an individual:

“Linda is a 55-year-old woman with a family. She likes reading and quiet reflection. Ever since she was a child, Linda has been non-confrontational, and in a tense situation prefers tactical retreats to open arguments.”

Given this information, what’s Linda’s likely profession?
a.             Librarian
b.             Doctor

Most people would pick a. since Linda’s introverted qualities seem to align with one’s mental image of a librarian. But the answer is really likely to be b. since there are far more doctors than librarians, so even a tiny percentage of doctors with the aforementioned traits would constitute a bigger number than librarians.

Now let us apply the same kind of reasoning to a description of a not-so-fictional molecule:

“Molecule X is a small organic molecule with a logP value of 3.2, 8 hydrogen bond acceptors, 4 hydrogen bond donors and a molecular weight of 247. It has shown activity against cancer cells and was discovered at Novartis using a robotics-enabled phenotypic screening technique with high throughput.”

Given this information, what is more likely?
a.             Molecule X is “druglike”.
b.             Molecule X is non-druglike.

What I have just described is the famous Lipinski’s Rule of 5 that lays down certain rules related to basic physicochemical properties for successful drugs. If you were dealing with a compound having these properties, you would be more likely to think it’s a drug. But among the unimaginably vast chemical space of compounds, the number of druglike compounds is vanishingly small. So there are far more non-druglike compounds than druglike compounds. Given this fact, Molecule X is very likely to not be a drug, yet one is likely to use its description to believe it’s a drug and pursue it.

I can also bet that the anchoring effect is at work here: the numbers “3.2” for logP and “247” for molecular weight which sound very accurate as well as the fact that a fancy technique at a Big Pharma company found this molecule are more likely to contribute to your belief that you have a great potential drug molecule at hand. But most of this information is marginally relevant at best to the real properties of Molecule X. We have again been misled by focusing on a tiny sample with several irrelevant properties and thinking it to be representative of a much larger group of data points.

Base rate fallacy: Representativeness leads us to another statistical fallacy: the base rate fallacy. As we saw above, the mistake in both the librarian and the druglike examples is that we fail to take into account the base rate of non-librarians and non-druglike compounds.

The base rate fallacy is generally defined as the tendency to ignore base rate or general information and focus only on specific cases. There are at least two examples in which I can see the base rate fallacy manifesting itself:

1. In overestimating HTS/VS hit rates against certain targets or for certain chemotypes without taking base hit rates into account. In turn, the bias can lead chemists to make fewer compounds than what might be necessary to get a hit.

2. The base rate fallacy is more generally related to ignoring how often you might obtain a certain result by chance; for instance, a correlation between expression levels of two proteins or a drug and a protein, or one involving non-specific effects of a druglike compound. The chance result can then feed into the other biases described above like representativeness or availability.

Anchoring, availability, representativeness and the base rate fallacy are classic examples of both extrapolating from a limited amount of information and ignoring lots of unknown information. They speak to the shortcuts that our thinking takes when trying to quickly conclude trends, rules and future directions of inquiry based on incomplete data. A lot of the solutions to these particular biases involve generating more data or finding it in the literature. Unfortunately this is not always an achievable goal in the fast-faced and cash-strapped environment of drug discovery. In that case, one should at least identify the most important pieces of data one would need to gather in order to update or reject a hypothesis. For example, one way to overcome the base rate fallacy is to calculate what kind of sampling might be necessary to improve the confidence in the data by a certain percentage. If all else fails, one must then regard the data or belief that he or she has as highly tentative and constantly keep on looking for evidence that might shore up other beliefs.

Cognitive biases are a very human construct, and they are so relevant to drug discovery and science in general because these are very human enterprises. In the ideal world of our imagination, science is an objective process of finding the truth (and of discovering drugs). In the real world, science is a struggle between human fallibility and objective reality. Whether in drug discovery or otherwise, at every step a scientist is struggling to square the data with the biases in his or her mind. Acknowledging these biases and constantly interrogating them is a small first step in at least minimizing their impact.

Black holes and the curse of beauty: When revolutionary physicists turn conservative

This is my latest monthly column for 3 Quarks Daily.

On September 1, 1939, the leading journal of physics in the United States, Physical Review, carried two remarkable papers. One was by a young professor of physics at Princeton University named John Wheeler and his mentor Niels Bohr. The other was by a young postdoctoral fellow at the University of California, Berkeley, Hartland Snyder, and his mentor, a slightly older professor of physics named J. Robert Oppenheimer.

The first paper described the mechanism of nuclear fission. Fission had been discovered nine months earlier by a team of physicists and chemists working in Berlin and Stockholm who found that bombarding uranium with neutrons could lead to a chain reaction with a startling release of energy. The basic reasons for the large release of energy in the process came from Einstein's famous equation, E = mc2, and were understood well. But a lot of questions remained: What was the general theory behind the process? Why did uranium split into two and not more fragments? Under what conditions would a uranium atom split? Would other elements also undergo fission?

Bohr and Wheeler answered many of these questions in their paper. Bohr had already come up with an enduring analogy for understanding the nucleus: that of a liquid drop that wobbles in all directions and is held together by surface tension until an external force that is violent enough tears it apart. But this is a classical view of the uranium nucleus. Niels Bohr had been a pioneer of quantum mechanics. From a quantum mechanical standpoint the uranium nucleus is both a particle and a wave represented as a wavefunction, a mathematical object whose manipulation allows us to calculate properties of the element. In their paper Wheeler and Bohr found that the uranium nucleus is almost perfectly poised on the cusp of classical and quantum mechanics, being described partly as a liquid drop and partly by a wavefunction. At twenty five pages the paper is a tour de force, and it paved the way for understanding many other features of fission that were critical to both peaceful and military uses of atomic energy.

The second paper, by Oppenheimer and Snyder, was not as long; only four pages. But these four pages were monumental in their importance because they described, for the first time in history, what we call black holes. The road to black holes had begun about ten years earlier when a young Indian physicist pondered the fate of white dwarfs on a long voyage by sea to England. At the ripe old age of nineteen, Subrahmanyan Chandrasekhar worked out that white dwarfs wouldn't be able to support themselves against gravity if their mass increased beyond a certain limit. A few years later in 1935, Chandrasekhar had a showdown with Arthur Eddington, one of the most famous astronomers in the world, who could not believe that nature could be so pathological as to permit gravitational collapse. Eddington was a previous revolutionary who had famously tested Einstein's theory of relativity and its prediction of starlight bending in 1919. By 1935 he had turned conservative.

Four years after the Chandrasekhar-Eddington confrontation, Oppenheimer became an instant revolutionary when he worked out the details of gravitational collapse all the way to their logical conclusion. In their short paper he and Snyder demonstrated that a star that has exhausted all its thermonuclear fuel cannot hold itself against its own gravity. When it undergoes gravitational collapse, it would present to the outside world a surface beyond which any falling object will appear to be in perpetual free fall. This surface is what we now call the event horizon; beyond the event horizon even light cannot escape, and time essentially stops flowing for an outside observer.

Curiously enough, the black hole paper by Oppenheimer and Snyder sank like a stone while the Wheeler-Bohr paper on fission gained wide publicity. In retrospect the reason seems clear. On the same day that both papers came out, Germany attacked Poland and started World War 2. The potential importance of fission as a source of violent and destructive energy had not gone unnoticed, and so the Wheeler-Bohr paper was of critical and ominous portent. In addition, the paper was in the field of nuclear physics which had been for a long time the most exciting field of physics. Oppenheimer's paper on the other hand was in general relativity. Einstein had invented general relativity more than twenty years earlier, but it was considered more mathematics than physics in the 1930s. Quantum mechanics and nuclear physics were considered the most promising fields for young physicists to make their mark in; relativity was a backwater.

What is more interesting than the fate of the papers themselves though is the fate of the three principal characters associated with them. In their fate as well as that of others, we can see the differences between revolutionaries and conservatives in physics.

Niels Bohr had pioneered quantum mechanics with his paper on atomic structure in 1913 and since then had been a founding father of the field. He had run an intellectual salon at his institute at Copenhagen which had attracted some of the most original physicists of the century; men like Werner Heisenberg, Wolfgang Pauli and George Gamow. By any definition Bohr had been a true revolutionary. But in his later life he turned conservative, at least in two respects. Firstly, he stubbornly clung to a philosophical interpretation of quantum mechanics called the Copenhagen Interpretation which placed the observer front and center. Bohr and his disciples rejected other approaches to quantum interpretation, including one named the Many Worlds Interpretation pioneered by John Wheeler's student Hugh Everett. Secondly, Bohr could not grasp the revolutionary take on quantum mechanics invented by Richard Feynman called the sum-over-histories approach. In this approach, instead of considering a single trajectory for a quantum particle, you consider all possible trajectories. In 1948, during a talk in front of other famous physicists in which Feynman tried to explain his theory, Bohr essentially hijacked the stage and scolded Feynman for ignoring basic physics principles while Feynman had to humiliatingly stand next to him. In both these cases Bohr was wrong, although the verdict is still out on the philosophical interpretation of quantum mechanics. It seems however that Bohr forgot one of his own maxims: "The opposite of a big truth is also a big truth". For some reason Bohr was unable to accept the opposites of his own big truths. The quantum revolutionary had become an old-fashioned conservative.

John Wheeler, meanwhile, went on to make not just one but two revolutionary contributions to physics. After pioneering nuclear fission theory with Bohr, Wheeler immersed himself in the backwater of general relativity and brought it into the limelight, becoming one of the world's foremost relativists. In the public consciousness, he will probably be most famous for coining the term "black hole". But Wheeler's contributions as an educator were even more important. Just like his own mentor Bohr, he established a school of physics at Princeton that produced some of the foremost physicists in the world; among them Richard Feynman, Kip Thorne and Jakob Bekenstein. Today Wheeler's scientific children and grandchildren occupy many of the major centers of relativity research around the world, and until the end of his long life that remained his proudest accomplishment. Wheeler was a perfect example of a scientist who stayed a revolutionary all his life, coming up with wild ideas and challenging the conventional wisdom.

What about the man who may not have coined the term "black holes" but who actually invented them in that troubled year of 1939? In many ways Oppenheimer's case is the most interesting one, because after publishing that paper he became completely disinterested in relativity and black holes, a conservative who did not think the field had anything new to offer. What is ironic about Oppenheimer is that his paper on black holes is his only contribution to relativity – he was always known for his work in nuclear physics and quantum mechanics after all – and yet today this very minor part of his career is considered to be his most important contribution to science. There are good reasons to believe that had he lived long enough to see the existence of black holes experimentally validated, he would have won a Nobel Prize.

And yet he was utterly oblivious to his creations. Several reasons may have accounted for Oppenheimer's lack of interest. Perhaps the most obvious reason is his leadership of the Manhattan Project and his fame as the father of the atomic bomb and a critical government advisor after the war. He also became the director of the rarefied Institute for Advanced Study and got saddled with administrative duties. It's worth noting that after the war, Oppenheimer co-authored only a single paper on physics, so his lack of research in relativity really reflects his lack of research in general. It's also true that particle physics became the most fashionable field of physics research after the war, and stayed that way for at least two decades. Oppenheimer himself served as a kind of spiritual guide to that field, leading three key postwar conferences that brought together the foremost physicists in the field and inaugurated a new era of research. But it's not that Oppenheimer simply didn't have the time to explore relativity; it's that he was utterly indifferent to developments in the field, including ones that Wheeler was pioneering at the time. The physicist Freeman Dyson recalls how he tried to draw out Oppenheimer and discuss black holes many times after the war, but Oppenheimer always changed the subject. He just did not think black holes or anything to do with them mattered.

In fact the real reason for Oppenheimer's abandonment of black holes is more profound. In his later years, he was afflicted by a disease which I call "fundamentalitis". As described by Dyson, fundamentalitis leads to a belief that only the most basic, fundamental research in physics matters. Only fundamental research should occupy the attention of the best scientists; other work is reserved for second-rate physicists and their graduate students. For Oppenheimer, quantum electrodynamics was fundamental, beta decay was fundamental, mesons were fundamental; black holes were applied physics, worthy of second-rate minds.

Oppenheimer was not the only physicist to be stricken by fundamentalitis. The malady was contagious and in fact had already infected the occupant of the office of the floor below Oppenheimer's – Albert Einstein. Einstein had become disillusioned with quantum mechanics ever since his famous debates with Bohr in the 1920s and his belief that God did not play dice. He continued to be a holdout against quantum mechanics; a sad, isolated, often mocked figure ignoring the field and working on his own misguided unification of relativity and electromagnetism. Oppenheimer himself said with no little degree of scorn that Einstein had turned into a lighthouse, not a beacon. But what is less appreciated is Einstein's complete lack of interest in black holes, which in some sense is even more puzzling considering that black holes are the culmination of his own theory. Einstein thought that black holes were a pathological example of his relativity, rather than a general phenomenon which might showcase deep mysteries of the universe. He also wrongly thought that the angular momentum of the particles in a purported black hole would stabilize its structure at some point; this thinking was very similar to Eddington's rejection of gravitational collapse, essentially based on faith that some law of physics would prevent it from happening.

Unfortunately Einstein was obsessed with the same fundamentalitis that Oppenheimer was, thinking that black holes were too applied while unified field theory was the only thing worth pursuing. Between them, Einstein and Oppenheimer managed to ignore the two most exciting developments in physics – black holes and quantum mechanics – of their lives until the end. Perhaps the biggest irony is that the same black holes that both of them scorned are now yielding some of the most exciting, and yes – fundamental – findings in cosmology, thermodynamics, information theory and computer science. The children are coming back to haunt the ghosts of their parents.

Einstein and Oppenheimer's fundamentalitis points to an even deeper quality of physics that has guided the work of physicists since time immemorial. That quality is beauty, especially mathematical beauty. Perhaps the foremost proponent of mathematical beauty in twentieth century physics was the austere Englishman Paul Dirac. Dirac said that an equation could not be true until it was beautiful, and he had a point. Some of the most important and universal equations in physics are beautiful by way of their concision and universal applicability. Think about E= mc2, or Ludwig Boltzmann's equation relating entropy to disorder, S=klnW. Einstein's field equations of general relativity and Dirac's equation of the electron that marries special relativity with quantum mechanics are both prime examples of elegance and deep beauty. Keats famously said that "Beauty is truth and truth is beauty", and Dirac and Einstein seem to have taken his adage to heart.

And yet stories of Dirac and Einstein's quest for beauty are misleading. To begin with, both of them and particularly their disciples seem to have exaggerated the physicists' reliance on beauty as a measure of reality. Einstein may have become enamored of beauty in his later life, but when he developed relativity, he was heavily guided by experiment and stayed very close to the data. He was after all the pioneer of the thought experiment. As a patent clerk in the Swiss patent office at Bern, Einstein gained a deep appreciation for mechanical instrumentation and its power to reveal the secrets of nature. He worked with his friend Leo Szilard on that most practical of gadgets – a refrigerator. His later debates with Bohr on quantum mechanics often featured ingenious thought experiments with devices that he had mentally constructed. In fact Einstein's most profoundly emotional experience came not with a mathematical breakthrough but when he realized that his theory could explain deviations in the perihelion of Mercury, an unsolved problem for a century; this realization left him feeling that "something had snapped" inside him. Einstein's success thus did not arise as much from beauty as from good old-fashioned compliance with experiment. Beauty was a sort of secondary effect, serving as a post-facto rationalization for the correctness of the theory.

Unfortunately Einstein adopted a very different attitude in later years, trying to find a unified field theory that was beautiful rather than true. He started ignoring the experimental data that was being collected by particle physicists around him. We now know that Einstein's goal was fundamentally flawed since it did not include the theory of the strong nuclear force, a theory which took another thirty years to evolve and which could not have progressed without copious experimental data. You cannot come up with a complete theory, beautiful or otherwise, if you simply lack one of the key pieces. Einstein seems to have forgotten a central maxim of doing science, laid down by the sixteenth century natural philosopher Francis Bacon, one of the fathers of the scientific method: "All depends on keeping the eye steadily fixed upon the facts of nature and so receiving their images simply as they are. For God forbid that we should give out a dream of our own imagination for a pattern of the world". In his zeal to make physics beautiful, Einstein ignored the facts of nature and pursued the dreams of his once-awesome imagination.

Perhaps the biggest irony in the story of Einstein and black holes comes from the words of the man who started it all. In 1983, Subrahmanyan Chandrasekhar published a dense and authoritative tome called "The Mathematical Theory of Black Holes" which laid out the complete theory of this fascinating object in all its mathematical glory. In it Chandra (as he was called by his friends) had the following to say:

"In my entire scientific life, extending over forty-five years, the most shattering experience has been the realization that an exact solution of Einstein's equations of general relativity, discovered by the New Zealand mathematician, Roy Kerr, provides the absolutely exact representation of untold numbers of massive black holes that populate the universe. This shuddering before the beautiful, this incredible fact that a discovery motivated by a search after the beautiful in mathematics should find its exact replica in Nature, persuades me to say that beauty is that to which the human mind responds at its deepest and most profound."

Black holes and beauty had come full circle. Far from being a pathological outlier as believed by Einstein and Oppenheimer, they emerged as the epitome of austere mathematical and physical beauty in the cosmos.

Dirac seems to have been guided by beauty to an even greater extent than Einstein, but even there the historical record is ambiguous. When he developed the Dirac equation, he was very closely aware of the experimental results. His biographer Graham Farmelo notes, "Dirac tried one equation after another, discarding each one as soon as it failed to conform to his theoretical principles or to the experimental facts". Beauty may have been a criterion in Dirac's choices, but it was more a way of serving as an additional check rather than a driving force. Unfortunately Dirac did not see it that way. When Richard Feynman and others developed the theory of quantum electrodynamics – a framework that accounts for almost all of physics and chemistry except general relativity - Dirac was completely unenthusiastic about it. This was in spite of quantum electrodynamics agreeing with experiment to a degree unprecedented in the history of physics. When asked why he still had a problem with it, Dirac said it was because the equations were too ugly; he was presumably referring to a procedure called renormalization that got rid of infinities that had plagued the theory for years.

He continued to believe until the end that those ugly equations would somehow metamorphose into beautiful ones; the fact that they worked spectacularly was of secondary importance to him. In that sense beauty and utility were opposed in Dirac's mind. Dirac continued to look for beauty in his equations throughout his life, and this likely kept him from making any contribution that was remotely as important as the Dirac equation. That's a high bar, of course, but it does speak to the failure of beauty as a primary criterion for scientific discovery. Later in his life, Dirac developed a theory of magnetic monopoles and dabbled in finding formulas relating the fundamental constants of nature to each other; to some this was little more than aesthetic numerology. Neither of these ideas has become part of the mainstream of physics.

It was the quest for beauty and the conviction that fundamental ideas were the only ones worth pursuing that turned Einstein and Dirac from young revolutionaries to old conservatives. It also led them to ignore most of the solid progress in physics that was being made around them. The same two people who had let experimental facts serve as the core of their decision making during their youth now behaved as if both experiment and the accompanying theory did not matter.

Yet there is something to be said for making beauty your muse, and ironically this realization comes from the history of the Dirac equation itself. Perhaps the crowning achievement of that equation was to predict the existence of positively charged electrons or positrons. This discovery seemed so alien and unsettled Dirac so much at the beginning that he thought positrons had to be protons; it wasn't until Oppenheimer showed this could not be the case that Dirac started taking the novel prediction seriously. Positrons were finally found by Carl Anderson in 1932, a full three years after Dirac's prediction. This is one of the very few times in history that theory has genuinely predicted a completely novel fact of nature with no experimental basis in the past. Dirac would claim that it was the tightly knit elegance of his equation that logically ordained the existence of positrons, and one would be hard pressed to argue with him. Even today, when experimental evidence is lacking or absent, one has to admit that mathematical beauty is as good a guide to the truth as any other.

Modern theoretical physics has come a long way from the Dirac equation, and experimental evidence and beauty still guide practitioners of the field. Unfortunately physics at the frontiers seems to be unmoored from both these criteria today. The prime example of this is string theory. According to physicist Peter Woit and others, string theory has made no unique, experimentally testable prediction since its inception thirty years ago, and it also seems that its mathematics is unwieldy; while the equations seem to avoid the infinities that Dirac disliked, they also presents no unique, elegant, tightly knit mathematical structure along the lines of the Dirac equation. One wonders what Dirac would have thought of it.

What can today's revolutionaries do to make sure they don't turn conservative in their later years? The answer might come not from a physicist but from a biologist. Charles Darwin, when explaining evolution by natural selection, pointed out a profoundly important fact: "It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change". The principle applies to frogs and butterflies and pandas, and there is no reason why it should not apply to theoretical physicists.

What would it take for the next Dirac or Einstein to make a contribution to physics that equals those of Einstein and Dirac themselves? We do not know the answer, but one lesson that the lives of both these physicists has taught us – through their successes as well as their failures – is to have a flexible mind, to always stay close to the experimental results and most importantly, to be mindful of mathematical beauty while not making it the sole or even dominant criterion to guide your thought processes, especially when an "uglier" theory seems to agree well with experiment. Keep your eye fixed on the facts of nature, not just on the dream of your imagination.

Friday Book Review: "The One Device: The Secret History of the iPhone"

Isaac Newton's quote about standing on the shoulders of giants applies to science as well as technology. No technology arises in a vacuum, and every technology is in some sense a cannibalized hybrid of versions of it that came before. Unlike science, however, technology suffers from a special problem: that of mass appeal and massive publicity, usually made possible by one charismatic individual. Because of the myth-weaving associated with it, technology even more than science can thus make make us forget its illustrious forebears.

Brian Merchant's book on the origin story of the iPhone is a good example of both these aspects of technological innovation. It was the culmination of dozens of technical innovations going back decades, most of which are now forgotten. And it was also sold to the public as essentially the brainchild of one person - Steve Jobs. This book should handily demolish that latter myth.

Merchant's book takes us into both the inside of the iPhone as well as the inside of the technically accomplished team at Apple that developed the device. He shows us how the idea of the iPhone came about through fits and starts, even as concepts from many different projects were finally merged into one. The initial goal was not a phone; Jobs finally made it one. But for most of the process Job was not involved, and one of the biggest contributions that the book makes is to highlight the names of many unsung engineers who both conceived the project and stuck with it through thick and thin.

Merchant illuminates the pressure-cooker atmosphere at Apple that Jobs cultivated as well as his quest for perfection. Jobs comes across as an autocratic and curmudgeonly task master in the account; most of the innovations were not his, and people were constantly scrambling to avoid incurring his wrath, although that did not prevent him from being first on all the key patents. In some sense he seems to have hampered the development of the iPhone because of his mercurial and unpredictable personality. Nonetheless, he had a vision for the big picture and commanded an authority that none of the others did, and that vision was finally what made the device a reality. Merchant's doggedness in hunting down the true innovators behind the phone and getting them to talk to him - a constantly uphill battle in the face of Apple's ultra-secret culture - is to be commended. This is probably as much of an outsider's inside account as we are likely to get.

The second part of the book is more interesting in many ways, because in this part Merchant dons the hat of investigative field reporter and crisscrosses the world in search of the raw materials that the phone is made up of. As a chemist I particularly appreciated his efforts. He surreptitiously sends a phone to a metallurgist who pulverizes it completely and analyzes its elemental composition; Merchant lovingly spends three pages listing the percentages of every element in there. His travels take him deep into a Bolivian mine called Cerro Rico which mines almost all the lithium that goes into the lithium-cobalt battery that powers the device. This mine, along with mines in other parts of South America and Africa which produce most of the metals found in the phone, often have atrocious safety records; many of the miners at Cerro Rico have average life expectancies of 40 years, and it's only the terrible standard of living that compels desperate job-seekers to try to make a quick buck here. Merchant also hunts down the father of the lithium-ion battery, John Goodenough (perpetual contender for a Nobel Prize), who gives him a tutorial not just on that revolutionary invention but on another, even more powerful sodium-powered batter that the 94-year-old chemist is working on.

Merchant also explores the origin of the Gorilla Glass that forms the cover of the phone; that glass was the result of a late-stage, frenzied negotiation between Jobs and Corning. He leads us through the history of the gyroscopes, image stabilizing camera and accelerometers in the device, none of which were invented at Apple and all of which are seamlessly integrated into the system. And there is a great account of the transgender, maverick woman who massively contributed to the all-important ARM chip that is at the heart of the phone's operating system. Equally important is the encryption system which illustrates one of the great paradoxes of consumer technology: we want our data to be as secure as possible, and at the same time we also want to use technology in myriad ways in which we willingly give up our privacy. Finally, there is an important discussion of how the real innovation in the iPhone was not the iPhone at all - it was the App Store: only when third party developers got permission to write their own apps did sales soar (think Uber). That's a marketing lesson for the business school textbooks I believe.

One of the most important - if not the most important - innovations in the iPhone is the multitouch display, and no other part of the phone illustrates how technology and ideas piggyback on each other. Contrary to popular wisdom, neither Steve Jobs nor Apple invented multitouch. It was in fact invented multiple times before over three decades; at particle physics lab CERN, at the University of Toronto, by a pioneering educator who wanted to make primitive iPad-like computers available to students, and finally, by a small company trying to make it easier for people with hand disabilities to operate computers. One of Apple's employees whose hand was sprained was seen wearing that device; it caught the eye of one of the engineers on the team, and the rest is history. Multitouch is the perfect example of how curiosity-based research gradually flows into useful technology, which then accidentally gets picked up by a giant corporation which markets it so well that we all misattribute the idea to the giant corporation.

Another example of this technological usurpation is the basic idea of a smartphone, which again did not come from Apple at all. In fact this discussion takes Merchant into a charming sojourn into the nineteenth century when fanciful ideas about wireless telegraphy dotted the landscape of popular culture and science fiction; in one illustration from 1907, Punch Magazine anticipated the social isolation engendered by technology by showing a lady and her lover sitting next to each other but choosing to communicate through a fictional wireless telegraph. Like many other inventions, ideas about wireless communication had been "in the air" since Bell developed the telephone, and so the iPhone in a sense is only the logical culmination of this marketplace of ideas. The smart phone itself came from an engineer at IBM named Frank Canova. For a variety of reasons - most notably cost - Canova's device never took off, although if you look at it it appears to be an almost identical albeit primitive version of the iPhone.

In the last part of the book, Merchant takes us on a trip to Foxconn, the world's largest electronics factory. Foxconn which is based in China is a city unto itself, and it's fascinating to have Merchant lead us through its labyrinthine and dimly-lit corridors, housing literally hundreds of thousands of workers whose toil reminds us of scenes from the underground city of Zion in the "Matrix" franchise. At one point Merchant makes an unauthorized excursion into forbidden parts of the factory and is amazed to see a landscape of manufacturing whose sheer scale seems to stretch on forever. The scenes are fascinating even if morbidly so; the working environment is brutal, the workers are constantly overworked and live in cramped quarters, and the suicides are so frequent that the authorities had to install nets in front of buildings to catch those who jumped from the top.

In one sense everything - the Bolivian lithium salt mines with workers breathing noxious fumes and being paid in pennies, the iPhone scrap heaps in Africa over which eager counterfeiters drool, the dozen other odd sourcing companies for metals and plastics, the dizzying cornucopia of iPhone parts with their diverse history and the sweat and toil of countless unknown laborers in far-flung parts of the world struggling to produce this device, often under conditions that would be downright illegal in the United States - come together on that dimly lit factory floor in Foxconn to bring you the piece of technology on which you may be reading these words.

You should never look at your phone the same way again.

Lab automation using machine learning? Hold on to your pipettes for now.

There is an interesting article on using machine learning and AI for lab automation in Science that generally puts a positive spin on the use of smart computer algorithms for automating routine experiments in biology. The idea is that at some point in the near future, a scientist could design, execute and analyze the results of experiments on her MacBook Air from a Starbucks.

There's definitely a lot of potential for automating routine lab protocols like pipetting and plate transfers, but this has already been done by robots for decades. What the current crop of computational improvements plans to do is potentially much more consequential though; it is to conduct entire suites of biological experiments with a few mouse clicks. The CEO of Zymergen, a company profiled in the piece, says that the ultimate objective is to "get rid of human intuition"; his words, not mine.

I must say I am deeply skeptical of that statement. There is no doubt that parts of experiment planning and execution will indeed become more efficient because of machine learning, but I don't see human biologists being replaced or even significantly augmented anytime soon. The reason is simple: most of research, and biological research in particular, is not about generating and rapidly testing answers (something which a computer excels at), but about asking questions (something which humans typically excel at). A combination of machine learning and robotics may well be very efficient at laying out a whole list of possible solutions and testing them, but it will all come to naught if the question that's being asked is the wrong one.

Machine learning will certainly have an impact, but only in a narrowly circumscribed set of experimental space. Thus, I don't think it's just a coincidence that the article focuses on Zymergen, a company which is trying to produce industrial chemicals by tweaking bacterial genomes. This process involves mutating thousands of genes in bacteria and then picking combinations that will increase the fitness of the resulting organism. It is exactly the kind of procedure that is well-adapted to machine learning (to try to optimize and rank mutations for instance) and robotics (to then perform the highly repetitive experiments). But that's a niche application, working well in areas like directed evolution; as the article itself says, "Maybe Zymergen has stumbled on the rare part of biology that is well-suited to computer-controlled experimentation."

In most of biological research, we start with figuring out what question to ask and what hypotheses to generate. This process is usually the result of combining intuition with experience and background knowledge. As far as we know, only human beings excel in this kind of coarse-grained, messy data gathering and thinking. Take drug discovery for instance; most drug discovery projects start with identifying a promising target or phenotype. This identification is usually quite complicated and comes from a combination of deep expertise, knowledge of the literature and careful decisions on what are the right experiments to do. Picking the right variables to test and knowing what the causal relationships between them are is paramount. In fact, most drug discovery fails because the biological hypothesis that you begin with is the wrong one, not because it was too expensive or slow to test the hypothesis. Good luck teaching a computer to tell you whether the hypothesis is the right one.

It's very hard for me to see how to teach a machine this kind of multi-layered, interdisciplinary analysis. One we have the right question or hypothesis of course we can potentially design an automated protocol to carry out the relevant experiments, but reaching that point is going to take a lot more than just rapid trial and error and culling of less promising possibilities.

This latest wave of machine learning optimism therefore looks very similar to the old waves. It will have some impact, but the impact will be modest and likely limited to particular kinds of projects and goals. The whole business reminds me of the story - sometimes attributed to Lord Kelvin - about the engineer who was recruited by a company to help them with building a bridge. After thinking for about an hour, he made a mark with a piece of chalk on the ground, told the company's engineers to start building the bridge at that location, and then billed them for ten thousand dollars. When they asked what on earth he expected so much money for, he replied, "A dollar for making that mark. Nine thousand nine hundred and ninety nine for knowing where to make it." 

I am still waiting for that algorithm which tells me where to make the mark.

Silvan "Sam" Schweber (1928-2017)

I was quite saddened to hear about the passing of Sam Schweber, one of the foremost and most scholarly historians of physics of the last half century. Schweber occupied a rather unique place in the annals of twentieth century physics history. He was one of a select group of people - Abraham Pais, Jeremy Bernstein and Jagdish Mehra were others - who knew many of the pioneers of quantum mechanics and theoretical physics personally and who excelled as both scientists and historians. His work was outstanding as both historiography as well as history, and he wrote at least half a dozen authoritative books.

Schweber got his PhD with Arthur Wightman at Princeton University, but his real break came when he went to Cornell for a postdoc with the great Hans Bethe. Schweber became close friends with Bethe and his official biographer; it was a friendship that lasted until Bethe's demise in 2005. During this time Schweber authored a well received textbook on quantum theory, but he was just getting started with what was going to be life's work.

Schweber became known for two high achievements. Probably the most important one was his book "QED and the Men Who Made It" which stands as the definitive work on the history of quantum electrodynamics. The book focused on both the personal background and the technical contributions of the four main contributors to the early history of QED: Richard Feynman, Julian Schwinger, Sin Itiro Tomonaga and Freeman Dyson. It's one of those rare books that can be read with profit by both technically competent physicists as well as laymen, since the parts concerning the informal history of physics and personal background of the main participants are as fascinating to read about as the technical stuff. Other prime participants like Hans Bethe, Robert Oppenheimer and Paul Dirac also make major appearances. If Schweber had written just that book and nothing else, he would still have been remembered as a major historian. The book benefited immensely from Schweber's unique combination of talents: a solid understanding of the technical material, a sound immersion in the history of physics, a personal friendship with all the participants and a scholarly style that gently guided the reader along. 

But Schweber did not stop there. His other major work was "In the Shadow of the Bomb", a superb contrasting study of Hans Bethe and Robert Oppenheimer, their background, their personalities, their contributions to physics and nuclear weapons and their similarities and differences. It's a sensitive and nuanced portrait and again stands as the definitive work on the subject.

Two other Schweber contributions guaranteed his place as a major historian. One was another contrasting study, this time comparing Oppenheimer and Einstein. And finally, Schweber put the finishing touches on his study of Bethe's life by writing "Nuclear Forces: The Making of the Physicist Hans Bethe". This book again stands as the definitive and scholarly study of Bethe's early life until World War 2. It's a pity Schweber could not finish his study of the second half of Bethe's remarkably long and productive life.

Another valuable contribution that Schweber made was to record a series of in-depth interviews with both Freeman Dyson and Hans Bethe for the Web of Stories site. These interviews span several hours and are the most detailed interviews with both physicists that I have come across: they will always be a cherished treasure.

Schweber's style was scholarly and therefore his books were not as well known to the layman as they should be. But he did not weigh down his writing with unnecessary baggage or overly academic-sounding phrases. His books generally strike a good balance between academic and popular writing. They are always characterized by meticulous thoroughness, a personal familiarity with the topic and an intimate knowledge of the history and philosophy of science.

By all accounts Schweber was also a real mensch and a loyal friend. When Oppenheimer's student David Bohm became the target of a witch hunt during the hysterical McCarthy years, Schweber and Bohm were both at Princeton. Bohm was dismissed by the university which worried far more about its wealthy donors and reputation than about doing the right thing. Schweber went to the office of Princeton's president and pleaded with him to reinstate Bohm. The president essentially threw Schweber out of his office.

Schweber spent most of his career at Brandeis University near Boston. I was actually planning to see him sometime this year and was in the process of arranging a letter of introduction. While I now feel saddened that I will miss meeting him, I will continue to enjoy and be informed by the outstanding books he has penned and his unique contributions to the history of science.

Cognitive biases in drug discovery: Part 1

The scientific way of thinking might seem natural to us in the twenty-first century, but it’s actually very new and startlingly unintuitive. For most of our existence, we blindly groped rather than reasoned our way to the truth. This was because evolution did not fashion our brains for the processes of hypotheses generation and testing that are now intrinsic to science; what it did fashion them for was gut reactions, quick thinking and emotional reflexes. When you were a hunter- gatherer on the savannah and the most important problem you faced was not how to invest in your retirement savings but to determine whether a shadow behind the bushes was a tree or lion, you didn’t quite have time for hypotheses testing. If you tried to do that it could likely be the last hypothesis you tested.

It is thus no wonder that modern science as defined by the systematic application of the scientific method emerged only in the last few hundred years. But even since then, it’s been hard to override a few million years of evolution and unfailingly use its tools every time. At every single moment the primitive, emotional, frisky part of our brain is urging us to jump to conclusions based on inadequate data and emotional biases, and so it’s hardly surprising that we often make the wrong decisions, even when the path to the right ones is clear (in retrospect). It’s only in the last few decades though that scientists have started to truly apply the scientific method to understand why we so often fail to apply the scientific method. These studies have led to the critical discovery of cognitive biases.

There are many important psychologists, neuroscientists and economists who have contributed to the field of cognitive biases, but it seems only fair to single out two: Amos Tversky and Daniel Kahneman. Over forty years, Tversky and Kahneman performed ingenious studies - often through surveys asking people to solve simple problems - that laid the foundation for understanding human cognitive biases. Kahneman received the Nobel Prize for this work; Tversky undoubtedly would have shared it had he not tragically died young from cancer. The popular culmination of the duo’s work was Kahneman’s book “Thinking Fast and Slow”. In that book he showed how cognitive biases are built into the human mind. These biases manifest themselves in the distinction between two systems in the brain: System 1 and System 2.

System 2 is responsible for most of our slow, rational thinking. System 1 is responsible for most of our cognitive biases. A cognitive bias is basically any thinking shortcut that allows us to bypass slow, rational judgement and quickly reach a conclusion based on instinct and emotion. Whenever we are faced with a decision, especially in the face of inadequate time or data, System 1 kicks in and presents us with a conclusion before System 2 has had time to evaluate the situation more carefully, using all available data. System 1 heavily uses the emotional part of the brain, including the amygdala which is responsible among other things for our fight-flight-freeze response. System 1 may seem like a bad thing for evolution to have engineered in our brains, but it’s what allows us to “think on our feet”, face threats or chaos and react quickly within the constraints of time and competing resources. Contrary to what we think, cognitive biases aren’t always bad; in the primitive past they often saved lives, and even in our modern times they allow us to occasionally make smart decisions and are generally indispensable. But they start posing real issues when we have to make important decisions.

We suffer from cognitive biases all the time - there is no getting away from a system hardwired into the “reptilian” part of our brain through millions of years of evolution - but these biases especially become a liability when we are faced with huge amounts of uncertain data, tight time schedules, competing narratives, quest for glory and inter and intragroup rivalry. All these factors are writ large in the multifaceted world of drug discovery and development.

First of all, there’s the data problem. Especially in the last two decades or so, because of advances in genomics, instrumentation and collaboration and the declining cost of technology, there has been an explosion of all kinds of data in drug discovery; chemical, biological, computational and clinical. In addition, much of this data is not integrated well into unified systems and can be unstructured, incomplete and just plain erroneous. This complexity of data sourcing, heterogeneity and management means that every single person working in drug development always has to make decisions based on a barrage of data that still only presents a partial picture of reality. Multiparameter optimization has to be driven when all parameters are almost always unknown. Secondly, there’s the time factor. Drug development is a very fast-paced field, with tight timelines driven by the urgency of getting new treatments to patients, the lure of large profits and the high burn rates and attrition rates. Most scientists or managers in drug discovery cannot afford to spend enough time getting all the data, and are almost always forced to make major decisions based on what they have rather than what they wish they had.

Thirdly, there’s the interpersonal rivalry and the quest for glory. The impact of this sociological factor on cognitive biases cannot be underestimated. While the collaborative nature of drug discovery makes the field productive, it also leads to pressure on scientists to be the first ones to declare success, or the first ones to set trends. On a basic scientific level for instance, trendsetting can take the form of the proclamation of “rules” or “metrics” for "druglike" features; the hope is that fame and fortune will then be associated with the institution or individuals who come up with these rules. But this relentless pressure to be first can foster biases of all kinds, including cognitive biases.

It would thus seem that drug discovery is a prime candidate for a very complex, multifaceted field that would be riddled with cognitive biases. But to my knowledge, there has been no systematic discussion of such biases in the literature. This is partly because many people might shrug off obvious biases like confirmation bias without really taking a hard look at what they entail, and partly because nobody really pays scientists in drug discovery organizations to explore their own biases. Yet it seems to me that trying to watch out for these biases in everyday organizational behavior would go at least some way in mitigating them. And it’s hard to refute the argument that mitigating these biases would likely make scientists more prone to smarter decision-making and contribute to the bottom line; in terms of both more efficient drug discovery as well as the ensuing profits. Surely pharmaceutical organizations would find that endpoint desirable.

A comprehensive investigation into cognitive biases in drug discovery would probably be a large-scale undertaking requiring ample time and resources; most of this would consist of identifying and recording such biases through detailed surveys. The good news though is that because cognitive biases are an inescapable feature of the human mind, the fact that they haven’t been recorded in systematic detail does not refute the fact of their existence. It therefore makes sense to discuss how they might show up in the everyday decision-making process in drug discovery.

We will start by talking about some of the most obvious biases, and discuss others in future posts. Let’s start with one that we are all familiar with: confirmation bias. Confirmation bias is the tendency to highlight and record information that reinforces our prior beliefs and discard information that contradicts it. The prior beliefs could have been cemented for a good reason, but that does not mean they will apply in every single case. Put simply, suffering from confirmation bias makes us ignore the misses and consider only the hits.

We see confirmation bias in drug discovery all the time. For instance, if molecular dynamics or fragment-based drug discovery or machine learning or some other technique, say Method X, is your favorite technique for discovering drugs, then you will keep on tracking successful applications of this technique without keeping track of the failures. Why would you do this? Several reasons, some of which are technological and some are sociological. You may have been trained in Method X since graduate school; method X is thus what you know and do best, and you don’t want to waste time learning Method Y. Method X might legitimately have had one big success, and you might therefore believe in it - even with an n of 1. Method X might just be easy to use; in that case you are transformed into the man who looks for his keys under the streetlight, not because that's where they are but that's where it's easiest to look. Method X could be a favorite of certain people who you admire, and certain other people who you don’t like as much might be hating it; in that case you will likely believe in it even if the haters actually have better data against it. Purported successes of Method X in the literature, in patents and as communicated by word-of-mouth will further reinforce it in your mind.

The same logic applies to the proliferation of metrics and “rules” for druglike compounds. Let me first say that I have used these metrics myself and they are often successful in a limited context in a specific project, but confirmation bias may lead me to only keep track of their successes and try to apply them in every drug discovery project. In general, confirmation bias can lead us to believe in the utility of certain ideas or techniques far beyond their sphere of applicability. The situation is made worse by the fact that the scientific literature itself suffers from a fundamental confirmation bias, publishing only successes. The great unwashed mass of Method X failures is thus lost to posterity.

There are some other biases that confirmation bias subsumes. For instance, the backfire effect leads people to paradoxically reinforce their beliefs when they are presented with contradicting evidence; it’s a very well documented phenomenon in political and religious belief systems. But science is also not immune from its influence. When you are already discounting evidence that contradicts your belief, then you can as readily discount evidence that seems to strengthen the opposite belief. Another pernicious and common subset of confirmation biases is the bandwagon effect, which is often a purely social phenomenon. In drug discovery it has manifested itself through scores of scientists jumping on to a particular bandwagon: computational drug design, combinatorial chemistry, organocatalysis, HTS, VS...the list goes on. When enough people are on a bandwagon, it becomes hard to resist not being a part of it; one fears this could lead to both missed opportunities as well as censure from the community. And yet it’s clear that the number of people on a bandwagon has little to do with the fundamental integrity of the bandwagon; in fact the two might even be inversely correlated.

Confirmation bias is probably the most general bias in drug discovery, probably because it’s the most common bias in science and life in general. In the next few posts we will take a look at some other specific biases, all of which lend themselves to potential use and misuse in the field. For now, an exhortation for the twenty-first century: "Know thyself. But know thy cognitive biases even better."

Bottom-up and top-down in drug discovery

There are two approaches to discovering new drugs. In one approach drugs fall in your lap from the sky. In the other you scoop them up from the ocean. Let’s call the first the top-down approach and the second the bottom-up approach.

The bottom-up approach assumes that you can discover drugs by thinking hard about them, by understanding what makes them tick at the molecular level, by deconstructing the dance of atoms orchestrating their interactions with the human body. The top-down approach assumes that you can discover drugs by looking at their effects on biological systems, by gathering enough data about them without understanding their inner lives, by generating numbers through trial and error, by listening to what those numbers are whispering in your ear.

To a large extent, the bottom-up approach assumes knowledge while the top-down approach assumes ignorance. Since human beings have been ignorant for most of their history, for most of the recorded history of drug discovery they have pursued the top-down approach. When you don't know what works, you try things out randomly. The Central Americans found out by accident that chewing the bark of the Cinchona plant relieved them of the afflictions of malaria. Through the Middle Ages and beyond, people who called themselves physicians prescribed a witches' brew of substances ranging from sulfur to mercury to arsenic to try to cure a corresponding witches' brew of maladies, from consumption to the common cold. More often than not these substances killed patients as readily as the diseases themselves.

The top-down approach may seem crude and primitive, and it was primitive, but it worked surprisingly well. For the longest time it was exemplified by the ancient medical systems of China and India – one of these systems delivered an antimalarial medicine that helped its discoverer bag a Nobel Prize for Medicine. Through fits and starts, scores of failures and a few solid successes, the ancients discovered many treatments that were often lost to the dust of ages. But the philosophy endured. It endured right up to the early 20th century when the German physician Paul Ehrlich tested 604 chemical compounds - products of the burgeoning dye industry pioneered by the Germans - and found that compound 606 worked against syphilis. Syphilis was a disease that so bedeviled people since medieval times that it was often a default diagnosis of death, and cures were desperately needed. Ehrlich's 606 was arsenic-based, unstable and had severe side effects, but the state of medicine was such back then that anything was regarded as a significant improvement over the previous mercury-based compounds.

It was with Ehrlich's discovery that drug discovery started to transition to a more bottom-up discipline, systematically trying to make and test chemical compounds and understand how they worked at the molecular level. But it still took decades before the approach bore fruition. For that we had to await a nexus of great and concomitant advances in theoretical and synthetic organic chemistry, spectroscopy and cell and molecular biology. These advances helped us figure out the structure of druglike organic molecules, they revealed the momentous fact that drugs work by binding to specific target proteins, and they also allowed us to produce these proteins in useful quantity and uncover their structures. Finally at the beginning of the 80s, we thought we had enough understanding of chemistry to design drugs by bottom-up approaches, "rationally", as if everything that had gone on before was simply the product of random flashes of unstructured thought. The advent of personal computers (Apple and Microsoft had launched in the late 70s) and their immense potential left people convinced that it was only a matter of time before drugs were "designed with computers". What the revolution probably found inconvenient to discuss much was that it was the top-down analysis which had preceded it that had produced some very good medicines, from penicillin to thorazine.

Thus began the era of structure-based drug design which tries to design drugs atom by atom from scratch by knowing the protein glove in which these delicate molecular fingers fit. The big assumption is that the hand that fits the glove can deliver the knockout punch to a disease largely on its own. An explosion of scientific knowledge, startups, venture capital funding and interest from Wall Street fueled those heady times, with the upbeat understanding that once we understood the physics of drug binding well and had access to more computing power, we would be on our way to designing drugs more efficiently. Barry Werth's book "The Billion-Dollar Molecule" captured this zeitgeist well; the book is actually quite valuable since it's a rare as-it-happens study and not a more typical retrospective one, and therefore displays the same breathless and naive enthusiasm as its subjects.

And yet, 30 years after the prophecy was enunciated in great detail and to great fanfare, where are we? First, the good news. The bottom-up approach did yield great dividends - most notably in the field of HIV protease inhibitor drugs against AIDS. I actually believe that this contribution from the pharmaceutical industry is one of the greatest public services that capitalism has performed for humanity. Important drugs for lowering blood pressure and controlling heartburn were also the beneficiaries of top-down thinking. 

The bad news is that the paradigm fell short of the wild expectations that we had from it. Significantly short in fact. And the reason is what it always has been in the annals of human technological failure: ignorance. Human beings simply don't know enough about perturbing a biological system with a small organic molecule. Biological systems are emergent and non-linear, and we simply don't understand how simple inputs result in complex outputs. Ignorance was compounded with hubris in this case. We thought that once we understood how a molecule binds to a particular protein and optimized this binding, we had a drug. But what we had was simply a molecule that bound better to that protein; we still worked on the assumption that that protein was somehow critical for a disease. Also, a molecule that binds well to a protein has to overcome enormous other hurdles of oral bioavailability and safety before it can be called a drug. So even if - and that's a big if - we understood the physics of drug-protein binding well, we still wouldn't be any closer to a drug, because designing a drug involves understanding its interactions with an entire biological system and not just with one or two proteins.

In reality, diseases like cancer manifest themselves through subtle effects on a host of physiological systems involving dozens if not hundreds of proteins. Cancer especially is a wily disease because it activates cells for uncontrolled growth through multiple pathways. Even if one or two proteins were the primary drivers of this process, simply designing a molecule to block their actions would be too simplistic and reductionist. Ideally we would need to block a targeted subset of proteins to produce optimum effect. In reality, either our molecule would not bind even one favored protein sufficiently and lack efficacy, or it would bind the wrong proteins and show toxicity. In fact the reason why no drug can escape at least a few side effects is precisely because it binds to many other proteins other than the one we intended it to.

Faced with this wall of biological complexity, what do we do? Ironically, what we had done for hundreds of years, only this time armed with far more data and smarter data analysis tools. Simply put, you don't worry about understanding how exactly your molecule interacts with a particular protein; you worry instead only about its visible effects, about how much it impacts your blood pressure or glucose levels, or how much it increases urine output or metabolic activity. These endpoints are agnostic of knowledge of the detailed mechanism of action of a drug. You can also compare these results across a panel of drugs to try to decipher similarities and differences.

This is top-down drug design and discovery, writ large in the era of Big Data and techniques from computer science like machine learning and deep learning. The field is fundamentally steeped in data analysis and takes advantage of new technology that can measure umpteen effects of drugs on biological systems, greatly improved computing power and hardware to analyze these effects, and refined statistical techniques that can separate signal from noise and find trends.

The top-down approach is today characterized mainly by phenotypic screening and machine learning. Phenotypic screening involves simply throwing a drug at a cell, organ or animal and observing its effects. In its primitive form it was used to discover many of today's important drugs; in the field of anxiety medicine for instance, new drugs were discovered by giving them to mice and simply observing how much fear the mice exhibited toward cats. Today's phenotypic screening can be more fine-grained, looking at drug effects on cell size, shape and elasticity. One study I saw looked at potential drugs for wound healing; the most important tool in that study was a high-resolution camera, and the top-down approach manifested itself through image analysis techniques that quantified subtle changes in wound shape, depth and appearance. In all these cases, the exact protein target the drug might be interacting with was a distant horizon and an unknown. The large scale, often visible, effects were what mattered. And finding patterns and subtle differences in these effects - in images, in gene expression data, in patient responses - is what the universal tool of machine learning is supposed to do best. No wonder that every company and lab from Boston to Berkeley is trying feverishly to recruit data and machine learning scientists and build burgeoning data science divisions. These companies have staked their fortunes on a future that is largely imaginary for now.

Currently there seems to be, if not a war, at least a simmering and uneasy peace between top-down and bottom-up approaches in drug discovery. And yet this seems to be mainly a fight where opponents set up false dichotomies and straw men rather than find complementary strengths and limitations. First and foremost, the ultimate proof of the pudding is in the eating, and machine learning's impact on the number of approved new drugs still has to be demonstrated; the field is simply too new. The constellation of techniques has also proven itself to be much better at solving certain problems (mainly image recognition and natural language processing) than others. A lot of early stage medicinal chemistry data contains messy assay results and unexpected structure-activity relationships (SAR) containing "activity cliffs" in which a small change in structure leads to a large change in activity. Machine learning struggles with these discontinuous stimulus-response landscapes. Secondly, there are still technical issues in machine learning such as working with sparse data and noise that have to be resolved. Thirdly, while the result of a top-down approach may be a simple image or change in cell type, the number of potential factors that can lead to that result can be hideously tangled and multifaceted. Finally, there is the perpetual paradigm of garbage-in-garbage-out (GIGO). Your machine learning algorithm is only as good as the data you feed it, and chemical and biological data are notoriously messy and ill-curated; chemical structures might be incorrect, assay conditions might differ in space and time, patient reporting and compliance might be sporadic and erroneous, human error riddles data collection, and there might be very little data to begin with. The machine learning mill can only turn data grist into gold if what it's provided with is grist in the first place.

In contrast to some of these problems with the top-down paradigm, bottom-up drug design has some distinct advantages. First of all, it has worked, and nothing speaks like success. Also operationally, since you are usually looking at the interactions between a single molecule and protein, the system is much simpler and cleaner, and the techniques to study it are less prone to ambiguous interpretation. Unlike machine learning which can be a black box, here you can understand exactly what's going on. The amount of data might be smaller, but it may also be more targeted, manageable and reproducible. You don't usually have to deal with the intricacies of data fitting and noise reduction or the curation of data from multiple sources. Ultimately at the end of the day, if like HIV protease your target does turn out to be the Achilles heel of a deadly disease like AIDS, your atom-by-atom design can be as powerful as Thor's hammer. There is little doubt that bottom-up approaches have worked in selected cases, where the relevance of the target has been validated, and there is little doubt that this will continue to be the case.

Now it's also true that just like with top-downers, bottom-uppers have had their burden of computational problems and failures, and both paradigms have been subjected to their fair share of hype. Starting from that "designing drugs using computers" headline in 1981, people have understood that there are fundamental problems in modeling intermolecular interactions: some of these problems are computational and in principle can be overcome with better hardware and software, but others like the poor understanding of water molecules and electrostatic interactions are fundamentally scientific in nature. The downplaying of these issues and the emphasizing of occasional anecdotal successes has led to massive hype in computer-aided drug design. But in case of machine learning it's even worse in some sense since hype from applications of the field in other human endeavors is spilling over in drug discovery too; it seems hard for some to avoid claiming that your favorite machine learning system is going to soon cure cancer if it's making inroads in trendy applications like self-driving cars and facial recognition. Unlike machine learning though, the bottom-up take has at least had 20 years of successes and failures to draw on, so there is a sort of lid on hype that is constantly waved by skeptics.

Ultimately, the biggest advantage of machine learning is that it allows us to bypass detailed understanding of complex molecular interactions and biological feedback and work from the data alone. It's like a system of psychology that studies human behavior purely based on stimuli and responses of human subjects, without understanding how the brain works at a neuronal level. The disadvantage is that the approach can remain a black box; it can lead to occasional predictive success but at the expense of understanding. And a good open question is to ask how long we can keep on predicting without understanding. Knowing how many unexpected events or "Black Swans" exist in drug discovery, how long can top-down approaches keep performing well?

The fact of the matter is that both top-down and bottom-up approaches to drug discovery have strengths and limitations and should therefore be part of an integrated approach to drug discovery. In fact they can hopefully work well together, like members of a relay team. I have heard of at least one successful major project in a leading drug firm in which top down phenotypic screening yielded a valuable hit which then, midstream, was handed over to a bottom-up team of medicinal chemists, crystallographers and computational chemists who deconvoluted the target and optimized the hit all the way to an NDA (New Drug Application). At the same time, it was clear that the latter would not have been made possible without the former. In my view, the old guard of the bottom-up school has been reluctant and cynical in accepting membership in the guild for the young Turks of the top-down school, while the Turks have been similarly guilty of dismissing their predecessors as antiquated and irrelevant. This is a dangerous game of all-or-none in the very complex and challenging landscape of drug discovery and development, where only multiple and diverse approaches are going to allow us to discover the proverbial needle in the haystack. Only together will the two schools thrive, and there are promising signs that they might in fact be stronger together. But we'll never know until we try.

(Image: BenevolentAI)