Field of Science

On live-tweeting at conferences

Nature News has an interesting piece on another novel aspect of the collision of 21st century social media with 20th century scientific culture, this time related to the phenomenon of live tweeting from scientific conferences. The piece talks about a meeting of the Ecological Society of America (ESA) which in its latest annual gathering asked social media enthusiasts to seek permission from speakers and poster presenters before live tweeting material from the meeting. As the piece indicates, many of the attendees were rather unhappy about this.

I can personally relate to the pleasure of live tweeting from a conference as I have been guilty of this sin several times. In fact I believe that there's no getting away from it in the Age of Social Media. The most recent instance was when, egged on by our spirited colleague Peter Kenny (aka "slayer of druglike metrics") a group of us participated in gleefully live-tweeting the Gordon Conference on Computer-Aided Drug Design (CADD). Now Gordon Conferences are supposed to be "private" in the sense that they are supposed to be forums for dissemination of unpublished data. But as far as I could vouch, at least 90% of the work in the conference was material that I had seen or heard before. My colleagues seemed to concur.

Basically here's what it boiled down to when we decided to tweet the CADD GRC

1. The conference chair did not object. 

2. 90% of the talks were based on material that was already published or presented. 

3. In all our tweets we were careful to separate the messenger from the message. In addition we refrained from tweeting when someone explicitly referred to unpublished material. 

Also, a lot of the tweets referred to reiteration of aspects of the field that are well known, although many are underappreciated and therefore in fact deserve to be highlighted. This point deserves emphasis. There's a lot of underappreciated wisdom in every field that, while not well-known, is crucial to the progress of that field. A good example in computational chemistry and modeling would be the use of statistics which has been woefully under-appreciated until recently. Thus, every time someone referred to the proper use of statistics or the lack thereof, it was a point worth sharing with the broader community.

4. As always, "when in doubt ask for forgiveness, not permission" - Grace Hopper.

5. In an age of crowdsourcing and massively interdisciplinary problem solving, it's not just a bit elitist but impractical and outdated to declare any public meeting as opaque to public inquiry. I believe that withholding information from a public conference goes against the spirit of science in the 21st century. This point is thus also a plea against the traditional guidelines for Gordon Conferences which restrict dissemination of information; guidelines which as indicated before seem rather futile in the face of a preponderance of already published or presented material. As science changes, conference rules need to adapt.

All this being said, I think it's important to respect someone's wishes if they wish to keep their presentation confined to the people in a conference room and explicitly request the audience to do so. In that case I will grumble but acquiesce. As I mentioned, in the CADD GRC I was careful not to tweet material that was explicitly marked as unpublished (although again, this amounted to a vanishing fraction of the total material). 

On the other hand, I think it would be absolute folly to insert an injunction against tweeting along with those against taking photographs in the official guidelines for any conference. For one thing, a picture is worth a thousand words so a photograph can convey much more than a tweet. Secondly, tweets in a sense are no different from notes that one might take in a conference: would we expect an injunction against sharing these personal notes with our colleagues to find even an iota of support? Then why the pushback against tweets?

Live-tweeting along with the other paraphernalia of social media is here to stay. Whatever my negative feelings about outrage and public shaming on twitter have been recently, there is no doubt in my mind that live tweeting from conferences is a net positive to the global scientific community. At the very least it will make up for the blot of public shaming on twitter, which wouldn't be a bad thing at all. So, if tweeting be the food of appropriate conferencing, play on!

What would be a "non-intuitive" prediction in medicinal chemistry?

Over the last two decades when computer-aided drug design was in development, one of the most common refrains you heard from medicinal chemists about its utility was that it did a poor job predicting  “non-intuitive” structural modifications to molecules. But the term is not always easy to define, and while the charge is often valid, it’s also sometimes unfair since what’s non-intuitive can be highly subjective and constitute a moving target. Also, the bar for non-intuitive ideas can be rather high based on the experience of particular medicinal chemists; it’s not always fair to hold a simple computational method up to the same standards as a chemist with thirty years experience (that’s not exactly what the software has been designed for…).

Nonetheless, it’s rather refreshing to hear modelers level the same charge against themselves, which is perhaps a sign that the entire field is now seeking higher standards than before. I was pleased to hear this sentiment noted several times in the ACS meeting in Boston which I just attended. But the question still stands: What prediction could a modeler make that would be deemed ‘non-intuitive’ or 'novel' by a fairly experienced medicinal chemist? There are at least a few cases that come to mind:

Binding and solution conformation prediction: Chemists are used to looking at 2D structures, and even a highly experienced chemist won’t be able to predict most of the time how a complicated-looking molecule will bind in a protein binding pocket. That is what docking is for, and it's one of the few areas of modeling which can claim a modest but solid degree of success. What is still a non-trivial problem is to predict the ensemble of conformations in solution which converge to a single conformation in the protein pocket. I worked on this problem myself in grad school, and it took up the majority of my half-decade or so spent there. The problem is in both determining the population of solution conformations and estimating the binding energy going from multiple to one conformation, and the general solution is still tedious and complicated.

The prediction of conformational changes in general is a problem that cannot be easily visualized by medicinal chemists without some kind of computational or experimental (especially NMR) support. Subtle structural additions like methyl groups or halogens can sometimes cause significant changes in solution conformational populations, which in turn may impact the binding conformation. Generally speaking it is impossible to understand these effects using intuition alone. Intramolecular hydrogen bonds which can stabilize conformations and improve membrane permeability are also hard to visualize or predict without some kind of computational analysis, especially for larger molecules like macrocycles.

Scaffold hopping: Another attractive idea which is not obvious to medicinal chemists. Scaffold hopping involves essentially locating the binding pharmacophore for a molecule and then finding (ideally) a completely different set of bonds and connectivities that would map on to the same pharmacophore. It is especially useful for transforming ring systems to one another or constraining an acyclic system in a ring. One utility of scaffold hopping is to locate bioisosteres. Computational techniques can be very useful here in principle, although pharmacophore detection can be spotty because of problems with false positives and negatives. Scaffold hopping is not just non-intuitive but is also a boon to getting around intellectual property which is usually every chemist’s nemesis.

Calculating desolvation penalties: An experienced medicinal chemist may be able to look at a compound and make a guess about its size or lipophilicity, but guessing desolvation penalties is intuitively quite hard except in obvious cases (as in the case of a positively or negatively charged group – even then, guessing the sum of all the interactions is challenging). One of the reasons is that desolvation being a point charge-dipole interaction, its energy goes up as the square of the charge (instead of just inversely as in Coulomb's law): small changes in heteroatom distributions can thus have significant and non-obvious effects on solvation/desolvation. 

Unfortunately calculating solvation energies is also still hard in a general sense for computational chemists, but progress continues to be made. One of the most successful predictions of modeling would be a case where a highly charged group compensates for all its desolvation by making perfectly formed hydrogen bonds with a protein - this is a very hard thing to predict as of now. Generally speaking though, desolvation penalties would, at least in principle, fall into the category of things that medicinal chemists wouldn’t often be able to guess.

Calculating strain energies: This is another case where even experienced medicinal chemists may not be able to make intuitive statements. Sometimes it’s obvious in an x-ray crystal structure that ligands are strained (manifested for instance in the form of bent amides, bent phenyl rings or any kind of non-planar conjugated systems). But other times the effects of strain can be invisible to the naked eye. The problem is that bond length changes of as little as tenths of an angstrom can translate into significant strain energies of several kcals/mol, and it is hard if not impossible for even seasoned medicinal chemists to actually see these strain-inducing elements without some kind of calculation. That’s where modeling can help.

Water molecules: We know well by now how complicated the behavior of water molecules in protein binding sites can be. Sometimes the kinds of predictions that modelers make about easily and productively displaced water molecules are rather obvious, such as when they are talking about a water molecule in a nice hydrophobic cavity. The subtle cases are harder to intuitively predict. For instance crystallographic waters may be firmly bound and therefore may have good enthalpy but may still be unhappy and displaceable because of an unfavorable entropy. Similarly as detailed in the link above, water molecules at ligand-solvent interfaces may have unexpected thermodynamic features. Unhappy water prediction methods like WaterMap and SZMAP are promising, but only when they can predict non-intuitive scenarios that are refractory to easy analysis by medicinal chemists.

Data analysis: Generally speaking the word ‘non-intuitive’ may also mask more mundane but useful goals like being able to analyze large amounts of data and suggest useful trends. In fact that’s a task that’s usually quite unsuited to the skills of a medicinal chemist because of its reliance on numbers and statistics and opacity to easy structural visualization.

Feel free to note others in the comments section which I might have left out. Even better are cases where modelers can make suggestions that aren't just non-intuitive but counterintuitive. For instance, if you can predict that a methyl group filling a pocket would lead to a drop in potency (steric reasons? trapped water?) that would be a good counterintuitive prediction. Or if you could predict that cyclization of a molecule would actually increase conformational flexibility because of alleviation of syn-pentane interactions (as I found out in my comparison of cyclic dictyostatin with acyclic discodermolide), that prediction would also fall into the same category. Counterintuitive predictions also provide acid tests of any model because of their emphasis on falsifiability.

I don’t claim that all the goals listed above are well within the purview of molecular modeling. What I am claiming is that there are several challenging tasks on which modeling has started to make inroads. And a good number of these could be called “non-intuitive” or even "counterintuitive". A decade or two down the line I don't think such predictions from modeling will be as rare as we currently think they are, and that's something that we all should look forward to.

Robert Oppenheimer, Hans Bethe and the value of balance and compromise



Should you be involved in political causes and activism as a scientist? This was a question that squarely confronted many of the twentieth century's scientists, and it heralded a meld of politics and science that continues to challenge and haunt us today. No other scientific development of the 20th century pushed the problem to the fore as much as the advent of atomic energy, and in some sense, no two individuals showcased the dilemmas and promises inherent in the participation of scientists in political affairs more than Robert Oppenheimer and Hans Bethe. This difference in their perception seems to have played a very significant role in the divergent paths their lives took, and it is one that is well-explored for instance in Sam Schweber’s outstanding contrasting study of Oppenheimer and Bethe, “In the Shadow of the Bomb”.

Both Oppenheimer and Bethe were precocious and were educated at the best universities in the world – Bethe at Munich and Oppenheimer at Göttingen. They met when Bethe fled the Nazis for the United States. Both of them became world-renowned for their accomplishments in research and teaching and for establishing world-class centers of physics; Oppenheimer at the University of California, Berkeley and Bethe at Cornell University. Early on Oppenheimer recognized Bethe as a truly outstanding theoretician and picked him to lead the important theoretical division of the Manhattan Project at Los Alamos. In turn Bethe enormously respected Oppenheimer's intellect, astonishingly quick mind and vast knowledge of diverse fields. After the war both Bethe and Oppenheimer served as top consultants to the government on atomic energy and defense. While Bethe spearheaded the development of physics in the country from Cornell, Oppenheimer served as director of the famed Institute for Advanced Study in Princeton, where he worked with individuals like Einstein, Dyson, Gödel and von Neumann. Both Bethe and Oppenheimer acted as wise men who others consulted for advice on important matters of science and policy. Both men remained very good friends till Oppenheimer's death in 1967; Bethe was one of three speakers at Oppenheimer’s memorial service.

On the other hand there were vast differences which partly owed their provenance to each man's personality and which were responsible for shaping their lives. Oppenheimer harbored a conflict of personality and self-doubt throughout his life. He was insecure in his Jewish identity whereas Bethe was largely indifferent to his Christian identity. While both men were prodigiously talented, Oppenheimer often searched for the center of his identity whereas Bethe was largely secure in his identity. Oppenheimer could be conceited, had a sharp tongue and made enemies, enemies who finally brought about his downfall in the government. Bethe on the other hand was one of the most balanced and strong-willed scientists of the century. He displayed remarkable equanimity and had rock solid self-confidence without a hint of arrogance. He could be calm under the most trying of circumstances and served as a sounding board on whom others could depend for sound advice. One of the reasons Oppenheimer picked Bethe rather than his volatile friend Edward Teller to lead the theoretical division of the project was because he knew that Bethe was far more likely to persevere, soothe egos and carry projects through to their end.

The differences in personality also led to each man's politics being quite different. While Bethe was avowedly liberal, his more balanced frame of mind and dedication to science kept him from actively pursuing radical political causes. Oppenheimer's soul-searching in the 30s led him to being associated with a variety of left-wing organizations on the West Coast. His brother, sister-in-law, wife, girlfriend and several students were active members of the Communist Party. While membership in the party was considered much more innocuous in the 30s than what it was later, I cannot imagine Bethe in a similar position. Such left-wing associations of course meant little of substance in Oppenheimer’s case and were common among intellectuals of that depressing decade; they mostly indicated nothing more than naive idealism, but nonetheless came to haunt Oppenheimer after the war. There is another interesting and unseemly difference between the two men’s personal relationships: Bethe was generally known to be very loyal to those he admired, whereas Oppenheimer was more political and attuned to what direction the wind was blowing in; this led to him denouncing a few of his left-wing colleagues as communists after the war.

A reading of Schweber’s book and other sources demonstrates that these great differences in personal traits were partly responsible for the path that each man's life took. With his plodding approach and enormous stamina, Bethe made contributions of astonishing breadth and depth to modern physics, won a Nobel Prize and continued to work until his death at the ripe age of 99. Oppenheimer's contributions were also outstanding but more limited. Perhaps his greatest contribution was the founding of modern theoretical physics in the United States, a school whose descendants continues to place the United States at the forefront of physics research. Scientifically his greatest feat was the discovery of black holes. Nonetheless, many people thought his contributions were not commensurate with his brilliance, partly because of his less focused approach and his being interested in several fields of study (among other things, he was as interested in French poetry and Sanskrit as he was in physics). Unlike Oppenheimer who in his friend Isidor Rabi’s words often saw the frontiers of physics as “surrounded by a fog”, Bethe was a practical man who stressed that he was not a philosopher, was self-assured in his science and always drove for hard agreement with experiment. He may not have been as quick as Oppenheimer, but he was more thorough in his approach to both physics and life.

Bethe served as a consultant to the government on important matters almost all his life because he could be more gentle and diplomatic than Oppenheimer, who with his sharp tongue and candid opinions quickly made powerful enemies. This led to him being hauled in front of a tribunal which revoked his security clearance. Wounded and depressed by this ungrateful action, Oppenheimer continued to write, teach and speak on science and society but could not influence government policy. On the other hand, Bethe continued to be more valuable as a government advisor throughout his life partly because he knew how to compromise and could be more diplomatic and modest than Oppenheimer. He could take courageous stands, but these stands often came in the form of well-argued and well-researched articles in magazines like Scientific American and the Bulletin of the Atomic Scientists. He was a key voice advising the government on the peaceful exploitation of atomic energy, and during the 80s he clashed with his old friend Edward Teller on the feasibility of Ronald Reagan’s Strategic Defense Initiative (‘Star Wars’). Even Republican administrations sought his advice.

The moral dilemmas generated by physics in the form of Oppenheimer and Bethe in the twentieth century have been picked up by other fields, most notably biology and climatology, in the twenty-first. As science grapples with more and more politically inflammatory topics – climate change, the teaching of evolution, stem cell research, recombinant DNA technology – scientists would do well to heed the lessons offered by Oppenheimer and Bethe. The question before many of them would be the following: should they take a more radical stance like Oppenheimer and try to change policy in the short-term, or should they be more plodding like Bethe and make modest but more solid contributions to policy in the long-term?

The enduring legacy of Leo Szilard, father of the atomic age

70 years ago on this day, a flash above Hiroshima silenced a hundred thousand voices and heralded the beginning of a new Faustian relationship between man and his machines. But one man had seen the flash 12 years before at a traffic light in London. Here's an account of my minor pilgrimage to that traffic light and a rumination on the enduring legacy of physicist Leo Szilard on the occasion of the 70th anniversary of the atomic bombing of Hiroshima.


A few years ago I made a research-related trip to London. While making headway on a collaboration to discover new anticancer drugs was the stated purpose of the trip, my other key goal was to simply stand at a particular traffic light near the British Museum in Bloomsbury and take a photo of myself standing there. I was so drawn to this mundane everyday object that I had made up my mind to visit London at least once in my lifetime for the sole purpose of standing at the intersection. What was so special about this traffic light? The answer had to do with Leo Szilard.

Leo Szilard – peripatetic Hungarian genius, impetuous habitué of hotel lobbies, soothsayer without peer among scientists – stands as a unique signpost at the intersection of twentieth century science and politics. Among his fellow scientists he was the great prophet who anticipated both the advent of Nazism and the coming of the nuclear arms race. To the few members of the public who are familiar with his name, he is perhaps best known as the man who persuaded Albert Einstein in July 1939 to send the famous letter to President Franklin Roosevelt warning the president of the consequences of nuclear fission which had been discovered eight months earlier in Germany. Szilard’s role in the Einstein letter was entirely consistent with his predilection for prediction. Part of the group of brilliant Hungarian “Martians” – scientists like John von Neumann and Edward Teller whose intellects and achievements were considered almost preternatural – Szilard was the most perspicacious in anticipating world events, and the most politically savvy. Even as a student in Berlin, where he hobnobbed with the likes of Einstein, Max Planck and Max von Laue, Szilard was convinced that Europe would soon go to war and that world government was the only solution to our collective problems; this conviction was only strengthened after the atomic bombings of Hiroshima and Nagasaki.

Partly by accident and partly by design, Szilard played a key role in some of the most important scientific events of the twentieth century. He always saw key world events before anyone else, and planned accordingly. Born at the turn of the century to a well-off civil engineer and his wife, Szilard’s background prepared him well for anticipating consequential events. Steeped in languages and showing precocious talent for mathematics at an early age, he also became mindful of changing political fortunes after a succession of communist and fascist regimes took over Hungary after the fall of the Austro-Hungarian Empire in 1919.

Like so many of his fellow scientists he left Hungary for Germany whose scientists were heralding new horizons in physics. It was German scientists like Planck and Einstein who, within a span of only twenty years, had fomented both the quantum and the relativity revolutions. In Berlin Szilard befriended Einstein and later filed a patent for a safe refrigerator with the technically savvy former patent clerk. Filing patents became a Szilard trademark; in fact his penchant for exploiting the commercial benefits of his ideas through would have made Szilard feel at home in the twenty-first century world of venture capitalism. Predating Claude Shannon by forty years, he also made important contributions to what we now call information theory. Politically Szilard could see that the country was a mess; while the 1920s Berlin of Christopher Isherwood’s “Goodbye to Berlin” (later turned into the musical "Cabaret") gleamed with dizzying decadence, unprecedented inflation, political instability and social discontent had brought people to their knees and were encouraging the slow but steady rise of fascist elements, ingredients which were congealing into a recipe for a major disaster. 

It was clear to Szilard, even in the 1920s, that the short-term future for Europe was dismal. He soon started living out of a suitcase, a habit that was certainly prudent and prescient for a Jewish intellectual in 1930s Germany. Acutely aware of the fate of colleagues who had been driven out of their jobs, he became active in a British committee which was helping expelled Jewish scientists secure employment. A man who was averse throughout his life to stable, salaried jobs that would deny him the pleasure of travel and squelch his free-thinking spirit, Szilard spent his time taking long walks and hounding the delis of European capitals in search of his beloved pastrami. 1933 found him living in London on a modest budget, subsisting on money earned from patents and private physics lessons. It was during one of his walks that Szilard had an epiphany that would gain him a place in the history books and, six decades later, compel a callow student of the history of science to seek out a traffic light in London.

Adolf Hitler had come to power in January, The Depression was raging and the future looked bleak to many. On the morning of September 12, 1933, on a miserable, wet English autumn day, at the intersection where Russell Square meets Southampton Row, Szilard waited impatiently at a traffic light waiting for it to change from red to green. He had just been sitting in a cafe, reading an article in The Times reporting a lecture by the great English physicist Ernest Rutherford. Rutherford, known to many as the father of nuclear physics, was discussing the newly prophesied release of energy from atoms, most notably by science-fiction pioneer H G Wells in his book “The World Set Free” which Szilard had recently read. It had been only one year when a Rutherford protégé named James Chadwick had discovered the elusive neutron, the third component of the nuclear atom along with electrons and protons. The past few years had seen a succession of experiments designed to shoot various particles into the heart of the atomic nucleus to probe its structure. Since every one of these particles had been charged they had to contend with the fearsome charged barrier erected by protons and electrons; lacking charge, the neutron was considered an ideal candidate as an atomic projectile. 

The science of nuclear physics was then in its heyday, as were social strife and the rise of fascism. Wells was not averse to speculating on a potentially explosive meld of the two. In his book the great powers of the world wage war with what Wells calls “atomic bombs”, gadgets deriving their enormous stores of energy from the forces holding the atomic nucleus together. Wells’s prognostications as well as Rutherford’s response to it had both made the front page of The Times. When asked whether there was any realistic chance of harnessing energy from the atom, Rutherford in his baritone voice dismissed this fanciful idea as nonsense. Any thought of releasing the energy locked inside the nucleus, he said, was “moonshine”.

Szilard was irritated by this flippant repudiation. Accomplished as he was, how could even the great Lord Rutherford know what the future held in store? During his own days Szilard had seen the great scientific minds of his time not fly but grope toward the truth and he was well aware how unpredictable the course of scientific discovery can be. Steeped in the pioneering discoveries in physics over the last four decades, Szilard himself had thought deeply about nuclear matters before, most often during extended morning bathtub ablutions in expensive hotels. Now, waiting for the light to change, he pondered Rutherford’s words…

What happened next has been immortalized by historian Richard Rhodes in his seminal work, “The Making of the Atomic Bomb”. The first time I read about it the words got seared into my mind.
“In London, where Southampton Row passes Russell Square, across from the British Museum in Bloomsbury, Leo Szilard waited irritably one gray Depression morning for the stoplight to change. A trace of rain had fallen during the night; Tuesday, September 12, 1933, dawned cool, humid and dull. Drizzling rain would begin again in early afternoon. When Szilard told the story later he never mentioned his destination that morning. He may have had none; he often walked to think. In any case another destination intervened. The stoplight changed to green. Szilard stepped off the curb. As he crossed the street time cracked open before him and he saw a way to the future, death into the world and all our woes, the shape of things to come”…
Time cracked open indeed. What Szilard realized as he stepped off that curb was that if we found an element that when bombarded by one neutron would release two neutrons, it could lead to a chain reaction that could possibly release vast amounts of energy. Leo Szilard had discovered the nuclear chain reaction long before anyone else, six years before the discovery of nuclear fission and any inkling that anyone could have had about the release of atomic energy, let alone the woeful apocalyptic future that would await the world because of its release. In his later days Szilard told an audience, “Physics and politics were always my two great interests”. That September morning the two unexpectedly collided at the traffic intersection and foretold a chain reaction of world events.

I first read Rhodes’s book in college; it was one of the books that sealed my resolve to become a scientist. The book begins with this story. Since then the event has been etched in my mind like words in red-hot steel. The description is so riveting, the tale so profound and evocative, the person so singular and the implications so prophetic, that I resolved to visit Szilard’s traffic light even if I had to once make a trip to London for just this purpose. Several years later I had the opportunity.

The traffic light itself is completely nondescript, standing among dozens of other nondescript lights. My friend and I almost missed it. As I mused aloud about my great disappointment in a cafe and wished I had a map, a Spanish tourist sitting at the next table saved my life and procured one. The intersection was there; we had missed it by a block. Back we went and indeed there it was, with not an indication that a famous and prophetic physicist had seen into the future at that light some 75 years ago. I stood at the intersection and had my friend take a photo for posterity. A moment captured in time, in homage to another moment prophesying the possible end of time.

As it happened, Szilard’s choice for the element he was thinking about turned out to be wrong. He dutifully filed a patent about his idea with the British Admiralty, which promptly stashed it away in the dark as the fanciful meanderings of an eccentric scientist. In fact nuclear fission would be discovered only six years later in Germany after a series of close misses in Italy and France. When Otto Hahn and Fritz Strassman reported the unexpected breaking up of the uranium nucleus, it was Szilard’s vision on that wet English day that allowed him to grasp the significance of the discovery instantly and prompted him to persuade Einstein to send his famous letter to FDR. He would go on to work with Enrico Fermi on the world’s first nuclear reactor, exasperate Manhattan Project security with his contempt for compartmentalization and unsuccessfully try to get another letter to FDR - this time presciently warning that direct use of the bomb would spark an arms race - before a stroke unexpectedly cut the president’s life short. Ironically when the first atomic bomb test was conducted on July 16, 1945 in the deathly stillness of the New Mexico desert, the flash was so bright that it would have been seen reflected off the moon. It was, literally, “moonshine”. The rest was history. Leo Szilard died peacefully in his sleep in 1964, hoping that the genie he and his fellow scientists had unleashed would co-exist harmoniously with mankind.

But that day I lived one of my minor dreams at that traffic light in London. A light illuminating a tale of human triumph and folly. Leo Szilard’s light. Standing at the intersection, I could not help but feel my mind racing across gulfs of time and forging a connection with this remarkable man. I stood there in silence for a few minutes, contemplating the consequences of Szilard’s vision for the future. Then we went on our way. A light rain began to batter the sidewalk.

References:
1. William Lanouette - Genius in the Shadows (this remains the best and most authoritative biography of Szilard)
2. Richard Rhodes - The Making of the Atomic Bomb (this remains the most eloquent and definitive history of the birth of the atomic age).

The intersection of Russell Square and Southampton Row where I stood. Of course, it's probably impossible to say which of the four lights Szilard was standing at (I was at the one to the right myself).



Once again, the problem is not synthesis, it's design

University of Illinois chemist Martin Burke who recently got a lot of press for his automated robotic molecular synthesizer has an interview in C&EN in which he says that his and similar other techniques will help to clear the bottleneck of synthesis that has plagued the pharmaceutical and other industries.

Chemjobber who linked to the piece disagrees and says, “I don't think synthesis has been a bottleneck for access to materials. It's cost of synthesis that is a barrier, or design that is the slow step.”

And I think he’s spot on. As I enumerated in some detail in a previous post, the defining molecular challenge of our time is not synthesis but design. I would go a step further and say that it’s not the design of structures per se but the design of properties – a much harder goal. Synthesis has now turned into a well-oiled machine which can now deliver the goods more or less on demand; the only real bottleneck is money and manpower. The medicinal chemists in my company for instance have told me that if I can sketch it, they can make it, or if they can’t make it, WuXi can. And I have found that to be generally true with few exceptions. Technologies like those that are being pioneered by Burke will certainly speed up the delivery of new molecules for applications like pharmaceutical chemistry and materials science, but I don’t believe they will cause a fundamental paradigm shift in the overall process of drug discovery and development.

I noted above that medicinal chemists can make pretty much anything you ask them to, with a few exceptions. The exceptions that I am pointing out don’t really relate to the impossibility of making specific molecules; rather they refer to tradeoffs. When a medicinal chemist tells me he or she is reluctant to make a molecule that I recommended, what they’re really saying is that the synthesis is non-trivial. But what they really mean is that they will make it only if I can justify the effort required to make it with a high degree of confidence. In other words, if I can actually predict the important and desirable properties that a particular molecule is supposed to have, then the chemist will be happy to put in the extra effort needed.

In drug discovery those properties can range from simple increases in binding affinity to ‘higher-order’ properties like toxicity, liver metabolism or clearance rate. In materials science they could be tensile strength, rate of hydrogen absorption or rate of solar energy capture. In drug discovery, predicting the binding affinity of a particular molecule is already a challenging goal which we however are now getting a handle on computationally, but predicting those higher-order properties is still a dim and itinerant light on the horizon. The problem is not in synthesizing molecules which will have these favorable properties; the problem is in having enough confidence in designing those molecules. What the medicinal chemists are telling me is that if I can solve the problem of design and prediction of higher-order properties, they have already solved the problem of making molecules with those properties.

That is precisely why, in a sweeping, highly readable 2013 essay speculating on the role that chemistry should take on in the future of science and technology, George Whitesides emphasized that chemists should move “beyond the molecule”. What he was referring to was again the design of goals and properties. His point was similar to the one made by Chemjobber: we are now really good at making molecules. A hypothetical, fully automated molecular synthesizer where you literally feed it a Chemdraw file and it spits out compounds in vials at the end would be a huge advance in speed and efficiency, but it’s still not going to automatically address the problem of design. And that's the problem which should really keep us awake at night.

The problem with molecular modeling is not just molecular modeling

I am attending the Gordon Conference on Computer-Aided Drug Design (CADD) in the verdant mountains of Vermont this week, and while conference rules prohibit me from divulging the details of the talks, even the first day of the meeting reinforces a feeling that I have had for a while about the field of molecular modeling: the problems that plague the field cannot be solved by modelers alone.

This realization is probably apparent to anyone who has been working the field for a while, but its ramifications have become really clear in the last decade or so. It should be obvious by now to many that while modeling has seen some real and solid progress in the last few years, the general gap between promise and deliverables is still quite big. The good news is that modeling has been integrated into the drug discovery process in many small and sundry ways, ranging from getting rid of duplicates and "rogue" molecules in chemical libraries to quick similarity searching of new proposed compounds against existing databases to refinement of x-ray crystal structures. These are all very useful and noteworthy advances, but they don't by themselves promise a game changing impact of modeling on the field of drug discovery and development.

The reasons why this won't happen have thankfully been reiterated several times in several publications over the last fifteen odd years, to the extent that most reasonable people in the field don't get defensive anymore when they are pointed out. There's the almost complete lack of statistics that plagued the literature, leading people to believe that specific algorithms were better than what they actually were and continuing to apply them (this aspect was well emphasized by the last GRC). There's the constant drumbeats about how badly we treat things like water molecules, entropy, protein flexibility and conformational flexibility of ligands. There are the organizational issues concerning the interactions between modelers and other kinds of scientists which in my opinion people don't formally talk about with anywhere near the level of seriousness and the frequency which they deserve (although we are perfectly happy to discuss them in person).

All these are eminently legitimate reasons whose ills must be exorcised if we are to turn modeling into not just a useful but consequential and even paradigm-shifting part of the drug discovery process. And yet there is one other aspect that we should be constantly talking about that really puts a ceiling on top of even the most expert modeler. And this is the crucial reliance on data obtained from other fields. Because this is a ceiling erected by other fields it's not just something that even the best modelers alone can punch through. And breaking this ceiling is really going to need both scientific and organizational changes in the ways that modelers do their daily work, interact with people from other disciplines and even organize conferences.

The problem is simply of not having the right kind of data. It's not a question of 'Big Data' but of data at the right level of relevance to a particular kind of modeling. One illusion that I have felt gradually creeping up the spines of people in modeling-related conferences is that of somehow being awash in data. Too often we are left with the feeling that the problem is not that of enough data, it's only of tools to interpret that sea of information.

The problem of tools is certainly an important one, but the data problem has certainly not been resolved. To understand this, let's divide the kind of data that is crucial for 'lower level' or basic modeling into three categories: structural, thermodynamic and kinetic. It should be obvious to anyone in the field that we have made amazing progress in the form of the PDB as far as structural information is concerned. It did take us some time to realize that PDB structures are not sacrosanct, but what I want to emphasize is that when serious structure-based modeling like docking, homology modeling and molecular dynamics really took off, the structural data was already there, either in the PDB or readily obtained in house. Today the PDB boasts more than a hundred thousand structures. Meticulous tabulation and analysis of these structures has resulted in high-quality datasets like Iridium. In addition there is no dearth of publications pointing out the care which must be exercised in using these structures for actual drug design. Finally, with the recent explosion of crystallographic advances in the field of membrane protein structure, data is now available for virtually every important family of pharmaceutically relevant proteins.

Now consider where the field might have been in a hypothetical universe where the PDB was just getting off of the ground in the year 2015. Docking, homology modeling, protein refinement and molecular dynamics would all have been in the inky backwaters of the modeling landscape. None of these methods could have been validated in the absence of good protein structure and we would have had scant understanding of water molecules, protein flexibility and protein-protein interactions. The Gordon Conference on CADD would likely still be the Gordon Conference on QSAR.

Apply the same kind of thinking to the other two categories of data - thermodynamic and kinetic - and I think we can see some of the crucial problems holding the field back. Unlike the PDB there is simply no comparable database of tens of thousands of reliable thermodynamic data points that would aid the validation of methods like Free Energy Perturbation (FEP). There is some data to be found in repositories like PDBbind, but this is still a pale shadow of the quantity and (curated) quality of structures in the PDB. No wonder that our understanding of energies - relative to structure - is so poor. When it comes to kinetics the situation is much, much worse. In the absence of kinetic data, how can we start to truly model the long residence times in protein-ligand interactions that so many people are talking about these days? The same situation also applies to what we can call 'higher order' data concerning toxicology, network effects on secondary targets in pathways and so on.

The situation is reminiscent of the history of the development of quantum mechanics. When quantum mechanics was formulated in the twenties, it was made possible only by the existence of a large body of spectroscopic data that had been gathered since the late 1870s. If that data had not existed in the 1920s, even wunderkinder like Werner Heisenberg and Paul Dirac would not have been able to revolutionize our understanding of the physical world. Atomic physics in the 1920s was thus data-rich and theory poor. Modeling in 2015 is not exactly theory-rich to begin with, but I would say it's distinctly data-poor. That's a pretty bad situation to be in.

The reality is very simple in my view: unless somebody else - not modelers - generates the thermodynamic, kinetic and higher-order data critical to advancing modeling techniques the field will not advance. This problem is not going to be solved by a bunch of even genius modelers brainstorming for days in a locked room. Just like the current status of modeling would have been impossible to imagine without the contributions of crystallographers, the future status of modeling would be impossible to imagine without the contribution of biophysical chemists and biologists. Modelers alone simply cannot punch through that ceiling.

One of the reasons I note this problem is because even now, I see very few (none?) meetings which serve as common platforms for biophysical chemists, biologists and modelers to come together and talk not just about problems in modeling but how people from these other fields can address the problem. But as long as modelers think of Big Data as some kind of ocean of truth simply waiting to spill out its secrets in the presence of the right tools, the field will not advance. They need to constantly realize the crucial interfacing with other disciplines that is an absolute must for progress in their own field. What would make their own field advance would be its practitioners knocking on the doors of their fellow kineticists, thermodynamicists and network biologists to get them the data that they need.

That last problem suddenly catapults the whole challenge to a new level of complexity and urgency, since convincing other kinds of scientists to do the experiments and procure the data that would allow your field to advance is a daunting cultural challenge, not a scientific one. Crystallographers were busy solving pharmaceutically relevant protein structures long before there were modelers, and most of them were doing it based on pure curiosity. But it took them fifty years to generate the kind of data that modelers could realistically use. We don't have the luxury of waiting for fifty years to get the same kind of data from biophysical chemists, so how do we incentivize them to speed up the process?

There are no easy ways to address this challenge, but a start would be to recognize its looming existence. And to invite more scientists from other fields to the next Gordon Conference in CADD. How to get people from other fields to contribute to your own in a mutually beneficial relationship is a research problem in its own right that deserves separate space at a conference like this. And there is every reason to fill that space if we want our field to rapidly progress.

On the impact of social media and Twitter on scientific peer review

I am very pleased to note that an my article on the impact of social media and especially of blogs and Twitter on peer review in chemistry in particular and science in general has just come out in a special issue of the journal 'Accountability in Research'. This project has been in the works for almost a year and I have spent quite a bit of time on it. The whole issue is open access and it was made possible by the dedicated and generous efforts of my colleague and friend, the eminent historian of chemistry Jeff Seeman. I am privileged to have my article appear along with those by Roald Hoffmann, William Schulz, Jeffrey Kovac and Sandra Titus. All their papers are highly readable.

Here in a nutshell is what I say. I have had a very dim view of Twitter recently as a vehicle for cogent science communication and rational debate, but in this article I find myself full of praise for the medium. This sentiment has been inspired by the use of Twitter in recent times for demolishing careless science and questioning shoddy or controversial papers in the scientific literature. In my opinion the most spectacular use of Twitter to this effect was Nature Chemistry editor Stuart Cantrill's stark highlighting of 'self-plagiarism' in a review article published by Ronald Breslow in JACS in 2012 (I hold forth on the concept of self-plagiarism itself in the article). As I say in my piece, to my knowledge this is the first and only instance I know in which Twitter - and Twitter alone - was used to point our errors in a paper published in a major journal. If Cantrill's analysis was not a resounding example of peer review in the age of social media, I don't know what is.

I have had a much more consistent and positive views of blogs as tools for instant and comprehensive peer review, and thanks to the vibrant chemistry blogosphere that I have been lucky to be a part of for almost eleven years, have witnessed the true coming of age of this medium. There is no doubt that peer review on blogs is here to stay, and in my article I address the pitfalls and promises inherent in this development. One of the most important concerns that a naive observer would have regarding the use of blogs or Twitter for peer review is the potential for public shaming and ad hominem attacks - and such an observer would find plenty of recent evidence in the general Twittersphere to support their suspicions. Yet I argue that, at least as far as the limited milieu of chemistry blogs is concerned, the signal to noise ratio has been very high and the debate remarkably forward-thinking and positive; in fact I think that, by and large, chemistry blogs could serve as models of civil and productive debate for blogs on more socially or politically contentious topics like evolution and climate change. I am proud to be part of this (largely) civil community.

What I aim to do in this piece is to view the positive role of Twitter and blogs in effecting rapid and comprehensive peer review through the lens of three major case studies which would be familiar to informed observers: the debacle of 'arsenic life', the fiasco of hexacyclinol and the curious case of self-plagiarism in the Breslow 'space dinosaurs' review. In each case I point out how blogs and Twitter were responsible for pointing out mistakes and issues with the relevant material far faster than official review ever could and how they circumvented problems with traditional peer review, some obvious and some more structural. The latter part of the review raises questions about the problems and possibilities inherent in the effective use of these tools, and I muse a bit about how the process could be made fairer and simpler.

Due to the sheer speed with which blogs and social media can turn our collective microscopes on the scientific literature and the sheer diversity of views which can be instantly brought to bear on a contentious topic, there is no doubt in my mind that this new tier of scientific appraisal is here to stay. In my opinion the future of completely open peer review is bright and beckons. How it can complement existing modalities of 'official' peer review is an open question. While I raise this question and offer some of my own thoughts I claim to provide no definitive answers. Those answers can only be provided by our community.

Which brings me to the crux of the article: although my name is printed on the first page of the piece it really is of, by and for the community. Hope there will be something of interest to everyone in it. I welcome your comments.