The Sheri Sangji accident: The experimental details

Science has just published a summary of the report by California's Division of Occupational Safety and Health about the tragic accident involving Sheri Sangji and tert-butyl lithium. The summary is the most detailed description of the accident that I have seen so far and it makes it clear that there were at least four very significant violations of protocol during the experiment that Sangji was performing. These included:

1. Not wearing a lab coat and other appropriate safety gear.

2. Using a plastic syringe that by definition cannot be oven-baked to remove traces of moisture.

3. Using a syringe with a 2-inch needle that was about an order of magnitude shorter than the recommended length (1-2 ft.). This was a very significant safety breach since it would have required Sangji to tilt the bottle to extract the liquid, thus not only increasing the chances of a spill but also diminishing her general degree of control over the whole procedure.

4. Actually pulling the plunger back rather than let it be pushed by the inert nitrogen pressure from the bottle.

Of these four violations, only the first one can be easily assigned to Sangji herself since lab coats constitute a very general and well-known part of safety equipment. The others are specialized and specific to hazardous substances and their assignment is going to be much more ambiguous. The rub of the matter is going to be in finding out if these violations were the result of inappropriate or insufficient communication by the PI or an oversight on the part of Sangji herself.

From what I can tell, the report seems to lean toward the former possibility. One of the statements I found disturbing was Prof. Harran's admission that he "never discussed with Victim Sangji the risks associated with the tasks she was undertaking". Another important matter which I alluded to in a previous post was the responsibility of senior postdocs and graduate students in the lab, and the report provides a new twist to the issue that I hadn't seen before. Harran says that a postdoc in his group was supposed to train Sangji in the specifics of handling t-BuLi. The postdoc himself admits that he does not have specific recollection of providing "formal" training to Sangji. In addition Harran admits that he never confirmed whether the postdoc had in fact properly instructed Sangji in the use of the hazardous reagent. I would think that the relative apportioning of the blame between Harran and the postdoc is almost certainly going to be a focus during the trial.

None of this is too comforting and it certainly does not sound like it would make it easier for Harran to defend himself. And yet the sad fact of the matter is that this is how many labs around the world probably operate. The PI does not immerse himself in the minutiae of handling specific reagents and leaves it to the postdocs in the group. The postdoc or senior students in turn gingerly step into that notoriously gray area where it becomes difficult to say whether a particular degree of instruction was "sufficient" or not; for instance, was it enough for the postdoc to demonstrate the protocol once? How about twice? How about one actual demonstration followed by two pointed reminders?

These and other questions are almost certainly going to come up during the proceedings and their fuzzy, gray nature is going to make it difficult to assign blame. But the details of the report make it clear that somewhere, sometime, the crucial information undoubtedly slipped through the cracks. And even a clear admission of this fact may make practitioners around the world more vigilant and, one hopes, more humble.

Will quantum physics help us cure Alzheimer's disease?

There's an interesting bit of writing out in the journal ChemMedChem by Jean-Louis Kraus, a medicinal chemist in France who has worked on drug discovery for Alzheimer's disease. The article is essentially a summary of Kraus's pessimistic outlook towards current therapies and approaches addressing Alzheimer's disease. Kraus has worked for a long time in medicinal chemistry and his words reflect experience and not just opinion. The article starts off with some well-founded skepticism but ends up sounding...let's say a little questionable.

For the most part I agree with Kraus that Alzheimer's research in the last decade has seen one disappointment after another and that we are still largely groping in the dark. He refers to the fact that all the concerted research into the disease and the billions of dollars in funding have resulted in only a handful of drugs and a handful of protein targets. None of the drugs even partially repair the damage and none of the proteins have been shown to be decisive as targets for treatment. Beta-secretase, gamma-secretase, the NMDA receptor, acetylcholine esterase; all of these have seen their day in the sun, followed by a disappointing set of results usurping them from front stage. That doesn't mean that none of them are important, it's just that targeting them with therapies seems to have no direct causal connection with treating the disease.

But what is even more disappointing is that the basic hypothesis driving the field - the amyloid hypothesis - has now been seriously questioned. A series of high-profile clinical trial failures have sent researchers scurrying back to their benches and while amyloid almost certainly has an important connection with the disease, it's now not clear at all whether actually targeting the infamous protein aggregates will bring any benefits. What seemed like a promising and rather direct direction of research has devolved into a scientific mess that will need at least a few years to be sorted out. As far as Alzheimer's disease goes, we are still fighting with sticks and stones.

As Kraus says, much of this failure eventually is a failure to understand the basic biology of the disease, which in turn entails a failure to understand the brain on a more general level, including mechanisms of memory generation and storage. Even now, much of drug discovery fails because of ignorance of the detailed biology of the disease and its perturbation by small molecules. What is regarded as a sound mechanistic hypothesis is often thwarted by the complex realities of signal transduction. With Alzheimer's disease we seem to have been biting off more than we could chew, and we need to keep untangling the complex interplay of amyloid, the protein tau, the secretases and a multitude of other biochemical components before we can truly start developing therapies. Thus it's inevitable and essential that in addition to chemists and biologists, we will need crucial input from neuroscientists to target the disease.

But will we also need quantum physicists? Kraus's thoughts on the relevance of subatomic science to AD left me slightly nonplussed. He says that:

The theories behind black holes generally suggest that subatomic particles (electrons, protons, neutrons) are themselves black holes, in which time expands in the opposite direction of our proper (perceived) time. Huge amounts of information could be stored by the spin number of photons present in these particle black holes. Could it be possible that the organization of brain matter, in terms of the properties of subatomic particles (quantum mechanics), confers on brain matter the capacities of memory and cognition, and that these phenomena are not encountered in other types of matter structure in the human body?

Come again? I was not familiar with electrons, protons and neutrons being black holes. But even if they are, I fail to see their direct relevance to understanding memory and cognition. Sure, it's a trivial fact that it's a very specific organization of subatomic particles that leads to a brain rather than to a liver or a chair. But the real action all takes place at the level of aggregates of these particles which we call molecules. I get the feeing that Kraus is indulging in a classic reductionist fallacy here. While subatomic particles do constitute the brain, understanding the brain can only come at a higher level, that of rather old-fashioned physics and chemistry involving ionic currents and neurotransmitters.

But Kraus finds a valuable place for quantum physicists in the war on neurodegenerative disorders:

To me it has become mandatory to create an AD scientific community that includes not only medicinal chemists, pharmacologists, biologists, and medical doctors, but also quantum physicists, in order to understand how aging alters the intimate structure of brain matter, where memory and cognition are located, with the hope of finding new AD treatment research orientations.

To me this sounds suspiciously like Roger Penrose's argument in his rather startling book "Shadows of the Mind" in which he postulated a relationship between wavefunction superposition in quantum mechanics and the growth and shrinkage of microtubules as significantly contributing to consciousness. Even a cursory look at that argument raised serious doubts about the relevance of quantum behavior in microtubules and more formal analysis seemed to confirm these doubts. I am not saying that physicists won't be a valuable asset on a drug discovery team, it's just that they are probably not going to use the tools of quantum gravity to map out cognitive pathways anytime soon.

Somewhat ironically, Kraus ends his piece by extolling the role of a systems biology approach in addressing a problem as complex as Alzheimer's disease. With this I wholeheartedly agree, but systems biology is the opposite of reductionism, where new emergent phenomena provide causal explanations that cannot be reduced to the laws underlying their substrates. We do need a suite of analytical tools operating at various hierarchical levels to address the issue, but given enough time and smart people, we should be able to do the job using standard chemistry and biology, albeit at a more sophisticated level. No fancy biophoton entanglement may be necessary.

Kraus, J. (2011). Why as a Medicinal Chemist I Am Not Optimistic about the Possibility of Finding, in a Reasonable Timeframe, Small-Molecule Drugs Capable of Curing the Evolution of Alzheimer’s Disease ChemMedChem DOI: 10.1002/cmdc.201100431

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Introverts, extroverts and modern science

Over at "In the Pipeline", Derek has a post that indirectly asks the following question; all other factors being the same, is modern scientific research more conducive toward introverts or extroverts?

The post was inspired by an Op-Ed in today's New York Times by Susan Cain, a social scientist who has an interesting book arguing that modern professional life's bias toward extroverted behavior may be stifling the kind of creativity engendered by introverts. Whether at home or at work, we are expected to constantly collaborate, interact, email and reach out and perhaps all this is turning a little obsessive. I haven't read the book yet but the author is suggesting that much of the modern workplace not only encourages but often demands constant collaborations, meetings (both offline and online) and social networking in ways that are stacked against quiet introverts whose valuable skills may be lost in the din.

It may be worth noting first that both introverts and extroverts have made monumental contributions to scientific research, although there could be an argument for certain types of research favoring one or the other kind of personality. If most of your research involves working out dense mathematical theorems or tracing the life histories of ants in the Amazonian rainforest for that matter, an introverted personality that isn't too fond of social interaction may be best for you. On the other hand, if your research involves tracking consumer behavior in supermarkets or evaluating the effects of political bias on brains using fMRI, you may be best off being an extrovert who likes to interact with other social creatures.

Notwithstanding this tailoring of certain kinds of personalities for certain kinds of research, it's pretty clear that there are lots of exceptions and that people with diverse temperaments have flourished in scientific research. Henry Cavendish who would probably have received two or three Nobel Prizes had his discoveries been made in recent times was pathologically shy and would fire his maid if she stepped in the same room with him. As a contrast, among modern scientists, Niels Bohr was famous for being someone who thought best by talking to people. In the words of his protege, the physicist John Wheeler, talking physics with Bohr was like playing a one-man tennis match, with the other person serving as a wall to relentlessly bounce off ideas. Bohr would often take his students and collaborators for long walks, sometimes for circular walks around the building with the intent of hammering out his thoughts. The mumbling and the perpetual refinements and revisions of his sentence constructions made the experience a little harrowing for the listener. As Bohr and his victim went round and round, ideas would spin off. In sharp contrast to Bohr, Paul Dirac was equally famous for being taciturn to an almost pathological degree, mostly remaining silent even when questions were asked. Another example is Fred Sanger, the two-time Nobel Prize winner who is one of the most self-effacing and introverted scientists that you can find. In his case his inward-looking personality led to a career-long life in the lab doing technical work with scant regard for interviews, publicity or even scientific writing. Sanger who faithfully retired at the relevant age and now tends roses in his garden says that he has always been good at doing, listening and talking, in that order.

But as far as the trend of modern research and the current emphasis on open science goes, James Watson and Francis Crick's collaboration may be a better role model than Bohr's peripatetic thinking-out-loud style. The two hit it off almost instantly at the Cavendish Laboratories. Watson was irreverent and not afraid to walk up to anyone and ask any question that came to him. Crick was notoriously talkative and loud to the point of being irritating. The two were ideally suited to constantly bounce ideas off each other and rattle off their latest brainwave without any thought of politeness or social etiquette. But the real reason their collaboration serves as an inspiration for modern research is because of their completely open style of approaching the DNA problem. The two would ask anybody, learn any technique, build any model, perform any calculation, read any textbook and consult any reference, all without abandon. Any person, printed source, experimental or theoretical technique was meat and drink to them, even if in one infamous case Watson used Rosalind Franklin's x-ray data without her knowledge. This practice of doing whatever it takes to solve a problem is cardinal to today's multidisciplinary scientific research and it will continue to serve us well.

Open science will indeed bring us enormous benefits but it may also inevitably cast out the valuable outliers. The book by Cain seems to argue that there can be such a thing as too much collaboration if it becomes enshrined in scientific practice as an inviolable rule, leading to those who don't fit the mold being ignored or even ostracized. The real problem is with implicitly or explicitly penalizing introverts who would rather work by themselves. I myself am someone who prefers a balance; I don't like to be secluded in a private office (as I was in a previous job) and certainly prefer an open work space with desks and cubicles. But I would also like to work on ideas in solitude both before and after I discuss them with others. If I were to criticize modern collaborative science, it would be in its tendency to convene meetings or telephone conferences to sometimes discuss even trivial matters. It would be in emphasizing a talent for teamwork so much that not perceiving someone as an extroverted team player almost automatically ejects him from the pool of job applicants in an interview. And it would be in criticizing or even bullying up on someone who does not instantly subscribe to the latest technological breakthrough in online collaboration and data sharing.

There's one more drawback which I think is inherent in this era of constantly collaborative science. It offers little opportunity to work for some time without preconceived notions. Sometimes the best ideas come from people who can think outside the box precisely because they are not already exposed to the conventional wisdom in the field. In some ways, the first few days in a new position offer the best chance to do independent thinking that is unencumbered by groupthink. Bringing a new researcher in your organization or group up to speed by instantly steeping him or her in the group's research philosophy may seem like a good idea, but it may deny you the chance to tap into fresh and original insight. It is important in my opinion to let a newcomer in a research organization take some time and offer his own way of thinking to others rather than have him fall in line with your preferred mindset right away. And then there's the currently unanswered question of how much a world full of distractions that include the internet, phones, email and co-workers is affecting productivity.

Part of the reason why Cain's outlook strikes a chord with me is because of the realization that scientific research has always benefited from personalities, working styles and mindsets that are as diverse as the discoveries they make. Science has had room for all of them; the introverts, the extroverts, the evangelists, the prophets, the quiet lab technicians, the soap box enthusiasts and the madmen. All of these have made modern science, and all of them deserve a place at the table, no matter how eager its inexorable march.

Fraud in a glass of wine

One of the biggest stories in biomedical research during the last decade has been the discovery that certain molecules can mimic the effect of what's called caloric restriction, the reduced consumption of calories, either by starvation or by deliberation. Caloric restriction in turn has been linked quite reliably to a slowdown in aging and a general improvement in metabolism in lower animals like, yeast, fruit flies and certain worms. What was particularly alluring was that these effects seemed to be mediated by a single family of genes through proteins called sirtuins. The implication was clear; not only did we have a handle on a significant component of the genetic basis of aging but we could also potentially mimic the effects of anti-aging genes by drugs that targeted sirtuins.

But what really catapulted the story to public attention was the finding that resveratrol, a molecule found in red wine, might do this. The presence of a (relatively) cheap edible substance, universally consumed, savored and culturally revered that might slow down aging naturally led to unprecedented public attention. The French and Italians might say "I told you so", but suddenly the holy grail of medical science seemed to be within reach. As usual though, the initial euphoria gradually gave way to a more cautious and tempered belief in the benefits of red wine in mitigating the ill effects of age, and indeed in the whole field of caloric restriction itself. The complete story is fascinating and too convoluted to recount here, but the simple fact of the matter is that the biology of aging is much more complex than we imagined and the initial breakthroughs have not been as unambiguous as they seemed. Not surprisingly, ascribing something as complex as aging and its attendant physiological changes to the action of a single family of genes and proteins has turned out to be simplistic at best (as a comparison, even obesity is thought to be caused by dozens of genes with overlapping effects). In addition, anti-aging effects that got the attention of the New York Times turned out to be significant only in "lower" animals and not in mammals. As it stands today, while research on caloric restriction undoubtedly has great potential, many complications need to be ironed out before the initial optimism can be justified. Curiously, much of the high-profile work in the area can be traced in various forms to a single laboratory at MIT. A recent article in Science does a great job detailing the personalities, the findings and the controversies that sprang from this and other laboratories' work; the entire saga seems fit for a Sinclair Lewis novel.

But whatever the scientific status of the field, its high-profile nature and its potentially revolutionary implications promised ample funding for interested researchers, and over the years it has attracted both highly visible as well as lesser known scientists. One of the individuals who waded into resveratrol territory was Dipak Das of the University of Connecticut Medical School. Over the last few years Das published several papers detailing the beneficial effects of resveratrol in possibly preventing or mitigating oxidative damage caused in cardiovascular and neurological diseases. While most of his research has been published in low-impact journals, it seems that Das was on his way to a lucrative research career involving resveratrol and its role in health and disease.

Until now. It seems that somewhere along the road, he started committing fraud on a massive scale, the likes of which haven't been seen in some time in biomedical research. It started when an anonymous tipster tipped off the university about fabrication in some of Das's papers. The university then launched its own probe and formed a review committee. For the past two years the committee has been working in the shadows with the Office of Research Integrity (ORI) and last week they released their findings in a 50-page document. The findings indicate wholesale fraud, manipulation of results and deliberate doctoring of critical data on a shockingly regular basis between at least 2002 to 2009.

Much of the fabrication centers around Western blots, a research tool that's universally used in all aspects of biological research. The Western blot is to biology what spectroscopy is to chemistry and physics. It essentially detects the presence and identity of specific biomolecules, especially proteins. The proteins appear as dark bands on a white background with different lanes representing different proteins. Western blots are absolutely indispensable in confirming the identity of a novel or unknown protein, and I think it's safe to say that most biomedical researchers who have won Nobel Prizes have used these tools in their research. In case of Das, the report has found dozens of Western blots to be grossly manipulated using image manipulation software.

There are two aspects of the report that bear closer scrutiny. One is the sheer number of Western blots found to have been doctored. The committee examined 26 papers and cited no less than 88 figures which appear to be manipulated (there were also several that appeared normal). This is a staggering amount of manipulation and rules out accidental oversight. Das would have to be involved in a conscious, deliberate and extended effort to tamper with so much data. It's quite clear that the magnitude of the manipulation alone points strongly to purposeful fraud.

The second aspect of the report concerns the great difficulty of detecting the fraud. Western blots seem to be notoriously amenable to manipulation; for instance they prominently featured in another recent high-profile case of fraud in India involving a researcher at the National Center for Cell Science (NCCS). In the report on Das's work, single bands of proteins in Western blots have been enlarged and their borders further magnified to show the contrast between the background for that particular band and for others, indicating that the band in question was copied and pasted. Image manipulation software can sometimes produce such artifacts and some of the data appears like it could also have been the result of negligence or sloppy editing, but the number of such instances again rules out merely these possibilities.

But the difficulty of discerning the doctored data also indicates more generally how difficult it can be to detect fraud in science. Even experienced specialists, let alone laymen or researchers from other fields, wouldn't have thought to look for these minute differences which are evident only in retrospect. To demonstrate the process, at one point in the report the committee actually depicts a mock manipulation protocol where bands are edited or appended to other bands from totally different experiments. The subtleties in the data make it clear that even in the future it would be relatively easy for researchers to get away with this kind of manipulation. What these cases point to is the need for automated systems ("counter-software", if you will) that could detect such fine anomalies in submitted Western blots or other presentations and try to separate artifacts from real red flags. In this case of course, the verdict is unanimous. At the end of the report, the committee finds unambiguous evidence of "unequivocal image manipulation, splicing, background erase and duplication" in a vast number of cases resulting in both data fabrication and falsification.

The debacle is ending in ways that such unfortunate scenarios usually end. The university has already begun proceedings to fire Das from his position. It is very likely that he will never be able to do research again, and that's probably the way it should be given the extent of his fraud. Sadly, Das has not made things any easier by accusing university and department officials of racist prejudice. When you have to resort to such allegations in the face of massive evidence detailing your dishonesty, you only make your guilt seem more likely.

Ultimately this episode speaks as much about the culture of scientific research as it does about the transgressions of a particular researcher. We may not know for some time why Das felt like committing fraud on such a massive scale, but I suspect that the high-profile nature of anti-aging research and the funding that such research commands may have had at least something to do with it. In the last few years, resveratrol, caloric restriction and sirtuins have made it into the public discourse about science like few other topics. The possibility of harnessing all this data to solve the ultimate mystery of aging has ensured both sensationalist news items and eager funding agencies wanting to enable the next breakthrough. When you work in such high-profile fields, it is more tempting to fabricate your results to snare more funding. In this particular case, Das's work was deemed to be low-impact and peripheral to the field and so the damage might be negligible, but in someone else's hands it could well be extensive. The case of Jan Schon immediately comes to mind.

The other reason underlying Das's behavior might simply have been the complexities of biology. Das has been at the university since 1984 and got tenure in 1993, so it's curious why he decided to fabricate results in the last decade or so. I am quite ready to believe that his work on the complex effects of resveratrol on disease may have run into roadblocks, prompting him to start making up results that he wanted to see but which he didn't. Most research these days and especially biomedical research is a complex game. In some ways we are trying to bite off more than we can chew. In such cases wishful thinking can dominate, and when expected results don't pan out because of the complexity of the system under consideration, it becomes easier to succumb to desperation and temptation. The resveratrol story may fit into this paradigm, with initial reports suggesting a tantalizingly simple connection between the drugs and aging and more recent reports questioning this connection. Yet again, nature is not just more complex than we imagine, but it is more complex than we can imagine.

The only remedy for avoiding such debacles may be more acute vigilance, self-policing and an honest willingness to accept failures. And some modesty before nature may be in order here.

Other coverage: Derek Lowe (1, 2), San Francisco Chronicle

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What is your favorite deep, elegant or beautiful explanation (in chemistry)?

Over at Edge, they have a survey asking leading scientists, thinkers and writers about what they think is their favorite "elegant, deep or beautiful explanation". This is meant to be a very general question, not even limited to science and includes responses from people as diverse as the economist Richard Thaler, complexity theorist Stuart Kauffman and Stewart Brand (founder of the Whole Earth Catalog). The answers include ideas, explanations, experiments and entities as general and diverse as the scientific method, genes, Pascal's wager, bounded rationality, relativity and the limits of intuition.

The explanations run across the gamut of the sciences and the humanities including physics, biology, economics, neuroscience, politics and business. But conspicuously absent is chemistry, except for a few peripheral references like Charles Simonyi's listing of Besicovitch's theory of atomic forces. And this is in spite of our friend Derek Lowe of "In the Pipeline" being included in this august list. I was gratified to see a chemist being asked for his opinion, and was somewhat disappointed that Derek's favorite explanation was not chemical (his favorite is the rather deceptively simple notion of "freefall"). I of course don't blame Derek for his choice since there is no law dictating that a chemist's favorite explanation should be from chemistry just because he or she is a chemist. My own favorite beautiful explanation is probably Cantor's notion of multiple infinities.

But I did regret the striking omission of chemistry from the list. Sometime back I had a whole post on elegance in chemistry. And I certainly don't want people to think that deep and elegant explanations are limited to physics and biology, because they are not. Chemistry may not boast of profound philosophical explanatory frameworks like the Big Bang or evolution by natural selection. But it makes up for this fact by creating paradigms that directly touch the lives of millions of human beings in ways much more palpable than the Big Bang and evolution. So I thought I would add my own modest thoughts on my favorite deep idea in chemistry.

There's actually a few things at the top of my list; you certainly don't have to think hard to come up with several foundational chemical ideas. But if you really asked for my absolute favorite deep and elegant explanation, it is the shared-electron chemical bond. That's it. Right there is the simple concept that is at the heart of the material world, a concept that if you think about it has had a staggering impact on our quality of life, our relationships with other nations, our notion of prosperity itself. Chemical bonds as manifested in the foundations of modern civilization have certainly contributed as much to life, liberty and the pursuit of happiness as any scientific idea.

The idea itself as formulated by the great Gilbert Newton Lewis and comprehensible to any high-school student is simplicity incarnated; atoms combine into molecules and form a bond when electrons are shared. Everything that comes after the stating of this fact, important as it is, is details. All the quantum chemical wizardry, the thinking-in-orbitals, the great Gaussian simplification, it's after this basic groundwork has been laid. Heitler and London, Pauling, Slater, Mulliken, Pople, all of them made critical contributions to chemical bonding, but they all stood on Lewis's shoulders and built up from his landscape of the shared electron chemical bond.

Given the absolutely foundational role that the chemical bond plays in the thinking of chemists, it may be both ironic and a tad disturbing that chemists still cannot completely agree on the precise definition of every molecular bond out there. But that's not because the basic framework underlying bonding is uncertain. Part of the reason is simply because there is no such thing as "the" chemical bond. The bonding zoo sports a bewildering variety of animals, from the upstanding "normal" chemical bonds in, say the hydrogen or methane molecules, to the (literally) ready-to-snap pressure cooker entities in strained organic compounds, from the wily, shape-shifting bonds between metals and organic compounds to the ephemeral but biologically vital hydrogen bonds. Although the basic theory of the chemical bond is securely in place, it's going to take some time to craft a net wide and yet rigorous enough to snare the unruly and colorful creatures dotting the chemical landscape.

Now physicists may try to appropriate the chemical bond as their own, but they are out of luck. No explanation based purely on physics can truly impart a feel for the sheer diversity of bonds quoted above and their context-specific personalities. Just one bond serves to create a nightmare for purely reductionist approaches to defining chemical bonding- the hydrogen bond. Last year chemists convened at a meeting with the express purpose of tweaking their description of this all-important biological mediator, the glue that holds life together. Several questions were bandied about, but none more important than the very definition of a hydrogen bond. The problem was simple; hydrogen bonds can be weak or strong, sometimes so weak as to strain the definition of a bond, sometimes strong enough to suspiciously qualify as a covalent bond. How much of hydrogen bonding is electrostatic and how much is covalent? Is "bond" even the right term, or would "bridge" be more accurate? How do you define hydrogen bonds to metals? A consensus was finally reached on a new definition, but not even Linus Pauling could say that the definition would hold for all of eternity. Defining a hydrogen bond would give every physicist out there a run for his money. I find the concept of the chemical bond so enticing and elegant partly because even a single kind of bond like the hydrogen bond can hide a richly textured world of possibilities lurking behind its surface.

So there it is, why the concept of the chemical bond is my favorite idea, certainly in chemistry. It is deep because it underlies the making of the material universe, explaining the stuff that everything from crab shells to the Crab Nebula is made of. It is elegant because of the virtually unlimited amount of explanatory power that it hides in a simple statement of definition. And it is beautiful because of the sheer diversity of materials and structures that are created from a simple law of attraction. A lot of the thinkers in the Edge survey quoted evolution as their favorite deep idea. It certainly is beautiful. But Darwin could well have slightly paraphrased his words to apply to Lewis's shared-electron chemical bond:

"There is grandeur in this view of the material world, with its several powers, having been originally breathed into a single bond; and that, whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a bond endless forms most beautiful and most wonderful have been, and are being, evolved."

Molecular modeling: How far can physics take us?

Of all the scientists writing about modeling and simulation in drug discovery in the last decade or so, I have found Anthony Nicholls of OpenEye Scientific Software to be one of the most insightful. Not only has he written important papers emphasizing the role of rigorous statistics in generating and communicating modeling results, but he has also been a relentless proponent of the need for rigorous, unglamorous but essential experimental data to validate modeling protocols. In the phalanx of modelers pointing to a better future for their field, Anthony has been one of the torchbearers. I usually pay close attention when he writes so I think it's worth noting what he has to say in a recent article titled "The character of molecular modeling".

He starts by asking what the real advances in the field have been in the past 25 years and by observing an apparently rather disconcerting fact about modeling and especially structure-based modeling - successes are still mainly anecdotal. He expresses his disappointment while noting that most of the successful results in modeling are still mainly of the "find protein pocket, fill pocket" type. The chief role of the crystallographer, it seems, is to supply pockets that the computational chemist can then fill. The problem according to Anthony is that chemists are not "abstracting principles of wide applicability; they are recognizing domains of expertise".

At this point let me interject and say that while Anthony's gloomy prognosis might be true, it's also true that "find pocket, fill pocket" (or "find pocket, kill pocket" if you are in a hunter-gatherer mood) campaigns are not as straightforward as we think. There can be unexpected effects on both protein conformation and ligand conformation, similar to the "activity cliffs" witnessed by medicinal chemists. Even if the binding orientation of the ligand stays constant upon small changes, the distribution of solution conformations of the modified ligand is likely quite different, leading to differing energetic penalties that the protein has to pay for binding. I am sure I am not alone in saying that small changes in ligand structure leading to changes in binding affinity enforced by ligand strain and conformation are uncomfortably frequent. But there's another dimension to the "find pocket, fill pocket" campaign; it can actually be quite satisfying to suggest changes to a medicinal chemist for filling the pocket that are borne out by further crystallography. Finding pockets may generate anecdotes, but chemistry is a more anecdotal science than say physics, and chemists often revel in these little successes and failures. Chemists are more frogs than eagles.

But the real sticking point for Anthony is not really the anecdotal success of structure-based modeling but the lack of general physics-based principles and laws for doing molecular modeling. Docking is an example. In the last several years there have been many attempts to use physics-based "scoring functions" - essentially ways to sum up different protein-ligand interactions to a number - for calculating the binding affinity of a ligand. Programs for docking have evolved to a stage where ligands can be docked in the correct orientation with a roughly 30% success rate, depending on how similar the docked ligands are to a reference co-crystallized ligand. But the truth of the matter is that we still fail miserably when trying to dock an arbitrary ligand to an arbitrary protein in an arbitrary conformation. And of course, we are light years away from predicting free energies of binding for the general case. There have been cases in which physics in the form of electrostatics and quantum mechanics (more on this later) has significantly accelerated the search for similar molecules, but the promised land still seems far.

Does this failure reflect an absence of general principles of physics for computing protein-ligand interactions? Paraphrasing Rutherford (not Niels Bohr), in the next few decades will we do more physics or simply collect more stamps? Is this concern even warranted? To some extent, yes. It would certainly be very satisfying to have a general explanatory framework, a pool of more or less universal laws that explained the wide variety of protein-ligand complexes as completely as Newton's laws explain the behavior of an astonishingly diverse set of particle interactions in the classical world. Curiously, such a general framework does exist in the form of statistical mechanics and quantum mechanics. In theory, both these disciplines encompass the binding of every single protein to every single drug. So does that mean we can look forward to a time when every modeler can "abstract these principles of wide applicability" and use them to solve the particular case of his or her protein and ligand?

Here is where I part ways with Anthony at least partly. The reason in my mind is not too hard to discern. Think about how far we have come in explaining protein-ligand binding using the rather extensive developments in either quantum or statistical mechanics over the past five decades. The answer is, not as far as we would have liked to. While we have indeed made great advances in understanding the basic thermodynamics of protein-ligand binding, we have not been very successful in incorporating these principles into predictive computational models. Why so? For the same reason that we have not been successful in using physics to explain "all of chemistry", in Paul Dirac's words. Quantum mechanics has been applied to chemistry for fifty years and exponentially increasing computational power has significantly furthered its application, but even now, for most practical systems chemists use a variety of empirical models to understand and predict. That's partly because most real systems are too complex for the direct use of quantum mechanics, and an imperfectly understood protein and ligand immersed in an imperfectly understood solvent certainly belong to this category. It's also because we are still far from calculating things like entropy and being able to model the differential behavior of water at interfaces and in the bulk.

But even more importantly, physics may not solve our problems because chemists need to abstract general principles at the level of chemistry to ply their trade. Thus, in expressing doubts about the utility of general physics-based principles, I am appealing to the strong sense of non-reductionism that permeates chemistry and separates it from physics. The same principle applies to biology and I have written about this often. Principles drawn from physics have always been very useful in gaining insights into molecular interactions and they will continue to be an essential part of the mix. But unlike Anthony, I see a far smaller role that pure physics can truly make in enabling a general, practical predictive approach to modeling that's "chemical" enough to be widely used by chemists.

So are there cases in which physics can make a contribution? Here I actually do agree with Anthony when he mentions two areas where physics really promises to have a substantial impact, both conceptually and practically. The first is crystal structure prediction for organic molecules which is a notoriously fickle problem (a measure of the difficulty can be gleaned by the fact that even the simple benzene can crystallize in more than 30 different geometries), essentially one of being able to predict fine energy differences between almost equienergetic arrangements. Yet I see this problem as one of the more reductionist problems in chemistry, and as Anthony notes, it is conceivable that it will yield to physics-based approaches in the near future.

The other problem is one of the holy grails of chemistry and biology - protein structure prediction. In various guises, the last few years have seen a startlingly impressive set of cases where protein structures of small and (some) medium-sized proteins were predicted with atomic level accuracy. Protein structure prediction has to overcome the twin challenges of sampling and energy estimation that are a mainstay of almost every other modeling method. In this case Anthony thinks that we will have to get the physics right to address this issue.

But we have to be careful to distinguish between two cases here. The first case is where we get the right structure even if we have no idea how we got there. This is the field of empirical (non-physics based) protein fold prediction and the biggest success in this area has been the ROSETTA suite of programs. ROSETTA has definitely turned heads within the community by its ability to generate accurate structures for hundreds of proteins, but the big drawback of the approach is that it only generates the end result. Curiously Anthony does not mention ROSETTA, but I am also surprised that he does not mention in detail another significant development that does fit into the physics-based paradigm. This is the molecular dynamics approach developed by David Shaw, Vijay Pande and others. Unlike ROSETTA, MD can actually shed light on the process leading to a correct structure, although the details of the process are subject to errors, most notably in the force fields that underlie the simulation. It's quite clear that with all their limitations, ROSETTA and MD have been the biggest contributors to successful protein folding simulations over the last decade.

And yet as Anthony rightly says, their success seems almost like a miracle. This becomes clear when we realize that even now we have trouble predicting something as simple as the solvation energy of a simple organic molecule or the interaction energy of two simple molecules using even sophisticated quantum mechanics calculations. If our ability to predict even such simple scenarios is dismal, how on earth are we getting the structures of all those complex proteins right? The answer deserves as much scrutiny as the solution to these problems, scrutiny that is severely lacking. Anthony's answer (and mine) is "cancellation of errors along with a need to calculate only relative, not absolute, energies" (it's well known that force fields are virtually worthless for the calculation of absolute energies). It still strains my mind to think that these two factors could contribute to so many successful predictions published in the likes of Nature and Science. Cancellation of errors was partly made famous by Enrico Fermi. If that's really what's happening in all these cases, then the entire field needs to start celebrating Fermi as their guardian angel.

Ultimately, there is no doubt that advances will continue to be made with increasing computational firepower, but the foundations of the field will stay brittle unless these fundamental issues are addressed. Anthony ends with something he has been doing for a long time now - appealing to experimentalists, industry and government to contribute a small part of their funds to the kind of basic experiments that can further the field of modeling. This especially involves experiments that can refute an idea, a philosophy that has been dominant in the practice of science since its modern conception but one which seems to be unusually neglected in drug discovery because of the emphasis on positive data gathering. Science has always progressed by the testing of ideas that have no immediate practical bearing, except that they perform the invaluable function of making future scientific research worthwhile. It would be fundamentally unscientific if such ideas are not supported. Anthony puts it well:

"The simple commitment to spend a small percentage of the science budget at the NIH or at pharmaceutical companies on nontranslational work, providing support for the small cabals of scientists actually interested in making fundamental progress would be enormous. Reestablishing the contact between theorists and experimentalists, the publishing of high quality data, conferences devoted to the actual testing of ideas—in 25 years we might hope molecular modeling could become a real scientific discipline."

Is it too early to ask for automation in lab safety?

Let's face it. Graduate students do work occasionally without lab coats. Sometimes you have to do a quick procedure and sometimes you can just get bored of wearing the coat. I don't recall a time when I have walked into a lab and not seen someone not wearing a coat. Most of these individuals were hopefully not working with dangerous reagents, but the point is that it's human nature to be occasionally negligent. While Sheri Sangji's not wearing a coat was a breach of basic safety protocol (although it's not clear how far the coat would have gone in lessening her injuries), she was no more guilty of violating this safety norm than many other graduate students around the world. It's certainly not right but I don't think the practice is going to disappear anytime soon.

Then there's the question of Prof. Harran's responsibility in enforcing safety standards which I have talked about in a previous post. Even if a professor constantly monitors lab coat violation, he is naturally not going to patrol his lab 24 hours a day during each and every experiment. In addition, even the most diligent professor who is straggled with multiple responsibilities (research, grant writing, teaching, mentoring, administrative work) is going to have an occasional lapse of safety.

The reason I was mulling over these two points was to remind myself of something we all know about; you can't fight human nature. And since human nature is not going to go away, it seems odd to depend purely on human beings to enforce safety standards in a lab. The obvious question that then came to my mind was; why aren't automated systems employed for enforcing at least some safety standards in chemistry laboratories? Why do we still mainly depend on human beings to make sure everyone obeys safety protocols?

This seems especially pertinent if we think of the many other industries and research environments where technology reduces our dependence on the whims and uncertainties of human nature. In industries ranging from the nuclear to the aerospace to the automobile industries, multiple primary and backup systems are in place to kick in during those sadly too frequent occasions when human negligence, error and indifference endanger property and lives. It seems odd to me that in an age when technology is extensively used to deploy automated safety systems in multiple spheres of life, we are still depending on humans to constantly enforce basic and essential safety rules like the wearing of lab coats, glasses and gloves.

Automated systems would of course not protect lab personnel against every accident and it goes without saying that human review would still be necessary, but I don't see why relatively simple systems could not lead to a safer chemical workplace.

Two such simple systems come to my mind. In most current cars, you can open the door only when your keys are closer to it than a certain distance. There is clearly a proximity sensor in the car which detects the keys. A similar system could be used in a lab that would allow a chemical hood to function only when it detects a lab coat. A simple RFID tag embedded in the coat would activate a complementary sensor in the hood. So unless the person who approaches the hood has his or her lab coat on all the time, the hood would essentially go into lock down mode or at least activate an annoying alarm that can be turned off only when the coat is worn (similar to the beeping that results from not wearing a seat belt in a car). The proximity sensor system could hinge on RFID, infrared or optical sensors and the exact details would be dictated by cost, efficiency and mass deployment. But the technology certainly seems to exist and it should not be too expensive or difficult to install such a system in place. The system could of course also detect other safety gear like lab goggles and gloves.

As useful as such techniques for detecting lab gear could be, they would not stop an accident after it happens. A comprehensive automated safety framework needs provisions for both prevention and cure. These systems should especially be viable in the presence of a human being who is unable to take care of himself or herself. Although interfering with a runaway accident after it happens is difficult, there could be a few options. In case of Sheri Sangji, a violently flammable chemical spilled on her lab coat and caught fire, spreading to her sweater. For the next few minutes there was an intense cluster of "hot spots" in the room which she worked in. One could have a fairly simple infrared scanning system which sweeps the room and activates an alarm when it detects such a swarm of high-temperature spots, especially when they are moving. Implementing the condition of motion could help prevent the system from being set off by false positives such as hot flasks and beakers.

These are just a few thoughts. Naturally any such system would have to be refined, tuned and tested and would be subject to emergency human overrides. But it just seems to me that we should be able to implement at least a few robust automated safety systems for preventing lab tragedies when we take their existence in virtually every other aspect of our modern industrial lifestyle for granted.

Professorial oversight, availability bias and the Sheri Sangji case

There's a new twist on the tragic case of Sheri Sangji, a UCLA student working in the lab of Prof. Patrick Harran who died from burns resulting from her handling of tert-Butyllithium, a notoriously and violently flammable substance which has to be handled with the utmost case. This is a horrific example that reminds us of the perpetual and always potentially fatal dangers lurking in every corner of the lab. Our heart goes out to the Sangji family whose rage, grief and frustration are understandable.

But the issue gets murkier. It seems that criminal charges have now been brought against UCLA and Harran by the Los Angeles district attorney. Harran is going to surrender to the authorities when he comes back from what I am assuming is a holiday vacation.

I feel extremely doubtful that the charges would hold up, but I also think that these kinds of debates are generally conducive to maintaining a healthy safety culture. Something about Jefferson's quote about the price of democracy being eternal vigilance comes to mind. It's clear that the lab in which Sangji was working was found to violate safety standards, but I am sure that's probably the case for several other labs across the country. This does not excuse the lack of standards, but it makes one wonder if focusing on such stories leads to the typical situation where certain "rare events" seem to dictate our feelings and opinions on a more general issue because of their graphic nature and the emphasis that the media puts on them. More on this later.

The other reason the charges may not hold up is that the culpability of the institution and Prof. Harran, if it exists at all, is likely to be very fuzzy. Unfortunately Sangji was not wearing a lab coat, and I am guessing it would be very difficult, if not impossible, to find demonstrable evidence that she had not been told to constantly use this most basic of lab safety measures. In addition she was also wearing a sweater and was syringing out a rather large amount of the inflammable substance, and the prosecution will also have to find evidence that she was not warned against either of those practices. In addition Sangji was considered fairly well-versed in the hazards of chemical experimentation so she was expected to have known about basic lab protocols. None of this is to lay blame at her feet, but only to note that it muddies the legal aspect of the case.

But I think the greater issue deals with the amount of involvement that a professor should have in the safety of his students. I don't know of any faculty member (although I am sure there are a few) who schedules individual sessions with each of his or her students and instructs them in the minutiae of lab safety. Nor does every professor step into lab several times a day looking for every safety violation and I don't think it's realistic to expect them to. I don't know if it's legally required for any professor to specifically warn their students about the danger of handling t-BuLi. At most professors should periodically (but regularly) remind their students about safety standards, loudly denounce blatant violations and then expect senior graduate students and postdocs to enforce standards. If Prof. Harran is indeed guilty of transgressing safety norms, then it seems that the senior students and postdocs in his lab should share this blame even more. I am not saying either of them should, but it's hard for me to see how the responsibility for safety violations should fall squarely on the shoulders of Prof. Harran and not on his lab personnel.

Coming back to the highlighting of the issue as an indictment of lab safety, I am reminded of the always controversial issue of safety in the nuclear industry. We have constantly lived in times when the graphic, the dramatic and the most sensationalized events have dictated our opinions, no matter how rare they are. In case of the nuclear industry for instance, the occasional Chernobyl and Fukushima color our opinions of nuclear power for decades, even if thousands of nuclear reactors have been humming along for decades without major incidents. The safety record in the nuclear industry is way better than that in the chemical, coal or automobile industries, yet the nuclear industry gets an outrageous share of our derision and disapproval. The result? The distinct censure and under-utilization of nuclear power which has held its widespread deployment back for decades.

An undue focus on the perils of chemical research may similarly detract from the decades of productive chemical research and the education of promising chemists that has largely transpired without incident. I fear that bringing charges against UCLA and Prof. Harran will set a troubling precedent and may result in similar under-utilization of the benefits of chemical education. For instance I can see professors at other institutions holding back and being more reluctant to let undergraduates or technical assistants indulge in research involving common but potentially dangerous chemicals. We are already seeing the consequences of a disproportionate preoccupation with chemical safety in the lack of interesting experiments in chemistry sets for teenagers (presumably because most interesting experiments involve dangerous chemicals). Students themselves might be less eager to burnish their research credentials by working in a chemistry lab. Universities may enforce stricter rules restricting the availability of research opportunities for undergraduates on the grounds that they may lead to potential accidents.

Finally, the undue emphasis on safety and the resulting media circus may simply make worse what has been a perpetual headache for the proponents of chemistry - the public image of the discipline. The media has always been adept at exploiting a version of availability bias, a phenomenon delineated by psychologists Daniel Kahneman and Amos Tversky in which our perceptions of a phenomenon are shaped by what's easily remembered rather than what's the norm. One can be assured that the media will be far more eager to write about the occasional chemical tragedy than the countless number of times when the system actually worked and nobody was harmed. The Sangji case and the current charges against UCLA will do nothing to quell public fears about the dangers of chemical research. The public perception of working in a chemical laboratory will relate to what's "newsworthy" (deaths and fires) rather than what the facts are (thousands of safe experiments resulting in no harm). Ironically these dangers have always been there, but the countless number of times when they have caused no harm and in fact have led to great advances has gone unheeded.

Of course, none of this backlash may occur and certainly none of the ensuing discussion implies that we should be lackadaisical in the implementation and review of safety standards. Safety reviews should be second nature to lab personnel irrespective of tragedies like this one. Whenever they can professors should always remind every student under their wing of the ever-present dangers lurking in their laboratory. Senior graduate students and postdocs should consider the enforcing of lab safety their special responsibility since only a palpable safety-conscious culture could lead to an unconscious regard for safety. And universities should spare no effort in carrying out regular safety assessments.

But none of this should distract us from the very real benefits that chemical research and education have brought to countless young researchers whose time in the lab has inspired them to contribute to the advancement of chemical knowledge. It should not make us ignore the commendable tradition of chemical research in which professors and their students have carried out safe and illuminating chemical experiments in the presence of thousands of potentially fatal chemicals. Yes, students in labs are surrounded by chemical perils. But so are most of us in virtually every sphere of life. In the face of risks we do what we have always done, assess the dangers and constantly review, revise and research. And carry on.

What happened to Sheri Sangji was a tragedy, and sadly a preventable one at that. Yet if we overstep our boundaries of response and reaction, Sangji will not be the only victim. The real tragedy will be the discipline of chemistry itself.

A Christmas message from Steve Jobs for our friends in pharma

I am at the end of Walter Isaacson's excellent biography of Steve Jobs and it's worth a read even if you think you know a lot about the man. Love him or hate him, it's hard to deny that Jobs was one of those who disturbed our universe in the last few decades. You can accuse him of a lot of things, but not of being a lackluster innovator or product designer.

The last chapter titled "Legacy" has a distillation of Jobs's words about innovation, creativity and the key to productive, sustainable companies. In that chapter I found this:

"I have my own theory about why decline happens at companies like IBM or Microsoft. The company does a great job, innovates and becomes a monopoly or close to it in some field, and then the quality of product becomes less important. The company starts valuing the great salesmen, because they're the ones who can move the needle on revenues, not the product engineers and designers. So the salespeople end up running the company. John Akers at IBM was a smart, eloquent, fantastic salesperson but he didn't know anything about product. The same thing happened at Xerox. When the sales guys run the company, the product guys don't matter so much, and a lot of them just turn off."

Jobs could be speaking about the modern pharmaceutical industry. There the "product designers" are the scientists of course. Although many factors have been responsible for the decline of innovation in modern pharma, one of the variables that strongly correlates is the replacement of product designers at the helm by salespeople and lawyers beginning roughly in the early 90s.

There's a profound lesson in there somewhere. Not that wishes come true, but it's Christmas, and while we don't have the freedom to innovate, hold a stable job and work on what really matters, we do have the freedom to wish. So with this generous dose of wishful thinking, I wish you all a Merry Christmas.

Unruly beasts in the jungle of molecular modeling

The Journal of Computer-Aided Molecular Design is having a smorgasbord of accomplished modelers reflecting upon the state and future of modeling in drug discovery research and I would definitely recommend anyone - and especially experimentalists - interested in the role of modeling to take a look at the articles. Many of the articles are extremely thoughtful and balanced and take a hard look at the lack of rigorous studies and results in the field; if there was ever a need to make journal articles freely available it was for these kinds, and it's a pity they aren't. But here's one that is open access, and it's by some researchers from Simulations Inc. who talk about three beasts (or in the authors' words, "Lions and tigers and bears, oh my!") in the field that are either unsolved or ignored or both.

1. Entropy: As they say, entropy, taxes and death (entropy) are the three constant things in life. In modeling both small molecules and proteins, entropy has always been the elephant in the room, blithely ignored in most simulations. At the beginning there was no entropy. Early modeling programs then started extracting a rough entropic penalty for freezing certain bonds in the molecule. While this approximated the loss of ligand entropy in binding, it did nothing to take care of the conformational entropy loss that resulted in the compression of a panoply of diverse conformations in solution to a single bound conformation.

But we were just getting started. A very large part of the entropy of binding a ligand by a protein comes from the displacement of water molecules in the active site, essentially their liberation from being constrained prisoners of the protein to free-floating entities in the bulk. A significant advance in trying to take this factor into account was an approach that explicitly and dynamically calculated the enthalpy, entropy and therefore the free energy of bound waters in proteins. We have now reached the point where we can at least think of doing a reasonable calculation on such water molecules. But water molecules are often ill-localized in protein crystal structures because of low-resolution, inadequate refinement and other reasons. It's not easy to perform such calculations for arbitrary proteins without crystal structures.

However, a large piece of the puzzle that's still missing is the entropy of the protein which is extremely difficult to calculate on many fronts. Firstly, the dynamics of the protein is often not captured by a static x-ray structure so any attempts to calculate protein entropy in the presence and absence of ligands would have to shake the protein around. Currently the favored process for doing this is molecular dynamics (MD) which suffers from its own problems, most notably the accuracy of what's under the hood- namely force fields. Secondly, even if we can calculate the total entropy changes, what we really need to know is how the entropy is distributed between various modes since only some of these modes are affected upon ligand binding. An example of the kind of situation in which such details would be important is the case of slow, tight-binding inhibitors illustrated in the paper. The example is of two different prostaglandin synthase inhibitors which demonstrate almost identical binding orientations in the crystal structure. Yet one is a weak binding inhibitor which dissociates rapidly and the other is slow, tight-binding. Only a dynamic treatment of entropy can explain such differences, and we are still quite far from being able to do this in the general case.

2. Uncertainty: Out of all the hurdles facing the successful application and development of modeling in any field, this might be the most fundamental. To reiterate, almost every kind of modeling starts by using a training set of molecules for which the data is known and then proceeds to apply the results from this training set to a test set for which the results are unknown. Successful modeling hinges on the expectation that the data in the test set is sufficiently similar to that in the training set. But problems abound. For one thing, similarity is the eye of the beholder and what seems to be a reasonable criterion for assuming similarity may turn out to be irrelevant in the real world. Secondly, overfitting is a constant issue and results that look perfect for the training set can fail abysmally on the test set.

But as the article notes, the problems go further and the devil's in the details. Modeling studies very rarely try to quantify the exact differences between the two sets and the error resulting from that difference. What's needed is an estimate of predictive uncertainty for single data points, something which is virtually non-existent. The article notes the seemingly obvious but often ignored fact when it says that "there must be something that distinguishes a new candidate compound from the molecules in the training set". This 'something' will often be a function of the data that was ignored when fitting the model to the training set. Outliers which were thrown out because they were...outliers might return with a vengeance in the form of a new set of compounds that are enriched in their particular properties which were ignored.

But more fundamentally, the very nature of the model used to fit the training set may be severely compromised. In its simplest incarnation for instance, linear regression may be used to fit data points to a set of relationships that are inherently non-linear. In addition, descriptors (such as molecular properties supposedly related to biological activity) may not be independent. As the paper notes, "The tools are inadequate when the model is non-linear or the descriptors are correlated, and one of these conditions always holds when drug responses and biological activity are involved". This problem penetrates into every level of drug discovery modeling, from basic molecular level QSAR to higher-level clinical or toxicological modeling. Only a judicious and high-quality application of statistics, constant validation, and a willingness to wait (for publication, press releases etc.) before the entire analysis is available will preclude erroneous results from seeing the light of day.

3. Data Curation: This is an issue that should be of enormous interest to not just modelers but to all kinds of chemical and biological scientists concerned about information accuracy. The well-known principle of Garbage-In Garbage Out (GIGO) is at work here. The bottom line is that there is an enormous amount of chemical data on the internet that is flawed. For instance there are cases where incorrect structures were inferred from correct names of compounds:

"The structure of gallamine triethiodide is a good illustrative example where many major databases ended up containing the same mistaken datum. Until mid-2011, anyone relying on an internet search would have erroneously concluded that gallamine triethiodide is a tribasic amine. The error resulted from mis-parsing the common name at some point as meaning that the compound is a salt of gallamine and ‘‘ethiodidic acid,’’ identifying gallamine as the active component and retrieving the relevant structure. In fact, gallamine triethiodide is what you get when you react gallamine with three equivalents of ethyl iodide"

So gallamine triethiodide is the triply protonated salt, not the tribasic amine. Assuming otherwise can only lead to chemical mutilation and death. And this case is hardly unique. An equally common problem is simply assigning the wrong ionization state for chemical compounds as illustrated at the beginning of the post. I have already mentioned this as a rookie mistake, but nobody is immune to it. It should hardly be mentioned that any attempt to model an incorrect structure will result in completely wrong results. The bigger problem of course is when the results seem right and prevent us from locating the error; for example, docking an incorrectly positively charged structure into a negative binding site will result in very promising but completely spurious results.

It's hard to see how exactly the entire modeling community can rally together, collectively rectify these errors and establish a common and inviolable standard for performing studies and communicating their results. Until then all we can do is point out the pitfalls, the possibilities, the promises and the perils.

Clark, R., & Waldman, M. (2011). Lions and tigers and bears, oh my! Three barriers to progress in computer-aided molecular design Journal of Computer-Aided Molecular Design DOI: 10.1007/s10822-011-9504-3