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On Drug Design

"About the blog"

Personal reflections on drug design. Research interest includes combining new technology, informatics and science in innovative ways to tackle the challenging tasks in drug well as trying to distinguish science facts from science fiction using the power of computers...something I'll post a text on now and then...usually after having read an interesting book/paper.

“This Is Not a Molecular Visualizer”

Cool Technology Posted on Wed, November 18, 2015 23:46:35

Shortly after the first iPhone was launched Olli-Pekka Kallasvuo, the former CEO of Nokia, commented on Apple’s new product. The iPhone was brushed off as a niche product, with comments like “this is not a mobile phone”. Kallasvuo was right, it is more than that. Nokia’s mobile phone business has since collapsed. Many others have also failed to recognize the impact of new technology, and have failed to remain innovative and stay on the market. For example, Blockbuster was outperformed by Netflix who made on-demand streaming tv and video available to viewers. Kodak is another giant to falter in the face of advancing technology. Everyone knows that the new king of digital photography is Instagram.

The digital technology is currently changing our world in unprecedented ways. I have previously argued that the time has come for cloud computing. Another exciting example is the recent advances in interaction design creating new and very cool ways to use computer programs. For example, the ability to create highly visual 3D environments that simulates physical presence in the real world – a virtual reality.

Virtual reality was hype in the late eighties, but it never took off. Now, virtual reality is back with a vengeance; affordable with significantly improved performance. Thus, the long-promised technology is becoming mainstream. Virtual reality has begun to play a role in gaming and films. For example, Mythbusters is already shooting in virtual reality. As a moment of affirmation the Time magazine put virtual reality and the Oculus founder Lucky Palmer on the cover of its August issue.

Moreover, Zuckerberg and Facebook acquired the virtual reality startup Oculus with the wordsSo if you go back ten years, most of how people communicated were through text. We’re going through a period where now it’s mostly visual and photos. We’re entering into a period where that’s increasingly going to be video […] I think immersive 3D content is the obvious next thing after video.

We wanted to give virtual reality a try and developed a tool for molecular modeling (Molecular Rift) as part of a collaboration with the Department of Interaction Design (LTH). The ability to interact with molecular models is relevant to drug discovery since computer-generated molecular models are frequently used to obtain deeper understandings in many areas (e.g. ligand-protein complexes). In fact, visualizing and interacting with molecular models are a significant part of a drug designer’s day job nowadays. The virtual reality environment was created by a talented student using Oculus Rift (a head-mounted display) coupled with the MS Kinect gaming sensor to handle gesture recognition, eliminating the need for standard input devices (keyboard and mouse), providing the drug designer with a more natural way to navigate in 3D.

Student Magnus Norrby, a 10x programmer, demoing ‘his’ Molecular Rift at an internal science symposium

The BIG (and somewhat boring) question – is this very cool virtual reality environment more useful than using traditional molecular visualizers? Well, it should be noted that a virtual reality experience is different from using conventional 3D computer graphics. In the virtual reality, objects have a location in 3D-space relative to the user’s position. Thus the main difference is that you are working with things as opposed to working with images of things. The brain interprets it as you interact with the objects, and you feel like you are really there. The presence changes everything. Traditional visualizers ‘just’ show molecules in front of you, with Oculus a drug designer can enter a protein-ligand complex and look around.

Is that more useful? No one method will ever single-handedly make all the difference in drug discovery (not even CRISPR). Each method, however valuable it is, is one part of the puzzle. That said, if you believe that understanding the 3D component is important, then we believe virtual reality the best way to do it. What we learned during development was that once people try it, they get it. “This is so cool” – was an often heard comment. To compare with sophisticated CAVEs (which are also very cool!), Molecular Rift is easily accessible and the cost for setting up the environment (i.e. Oculus Rift and MS Kinect) is reasonable (a couple of hundred USD). It can easily be used at home or in office spaces. Molecular Rift and its source code have been made open source and is available at GitHub free of charge. We integrated an open-source cheminformatics toolkit paving the way for future development – hopefully in a collaborative and concerted fashion.

Whether Molecular Rift will be the new iPhone of molecular visualizers or be reduced to some kind of exhibition, something to show-off for “important” visitors remains to be seen. If you are interested in the details, the manuscript is here. It includes initial attempts on voice control, and one section with reflections on virtual reality’s capabilities in Chemistry and future possibilities. For example, Chemistry as a University subject is certainly not trending. In many countries gloomy pictures are reported. Gamifying learning using Molecular Rift might be one way to motivate and inspire students.

Molecular Rift Is Not a Molecular Visualizer.

The Halo(gen) Effect in para-Substituted Phenyl Rings

Drug Design Posted on Wed, November 18, 2015 23:37:02

This was first posted: here

One key to successfully progress a drug discovery project is to make first-rate decisions (hopefully) based on unambiguous data. This is not trivial since our scientific problems are often very complex and data can be fuzzy. In drug design we try to approach this uncertainty by being rational. It is however sometimes forgotten that our rational approaches may not be that rational after all – decisions may well be based on personal preferences and intuitive biases…. perhaps unconsciously made on biased data.

In their great paper “Judgment under Uncertainty” the behavioral scientists Kahneman and Tversky elaborated on decision making and how people deal with uncertain events. It was shown that we tend to push ahead with confidence even though lacking enough information (to make informed decisions). There’ also the entertaining (?) and somewhat controversial notion that scientific facts can be constructed in a tribe-like fashionin laboratory settings. In drug discovery, a number of psychological biases that pose risks to good decision making was recently highlighted by Segal and Chadwick.

“The Halo Effect” is a specific type of confirmation bias that makes us perceive someone (or something) favorably because of one very positive quality in that person/thing. That is, one good feature lends its attractiveness to other properties of a person’s character: “that Hollywood actress is beautiful, so she must also be clever/happy/fill in the blank“. We tend to make attributions based on other data that we for some reason believe are reliable, and it can cloud our judgment and infer with decision-making.

David Beckham is looking good and few in the world can kick the ball like him. He’s likable and extremely popular. It is easy to think that he’s all good. Nonetheless, he has recently been accused of triggering a halo effect around unhealthy drinksby endorsing Pepsi. Is there also a halo(gen) effect in para-substituted phenyl rings? They are (on average) metabolically more stable than their ortho and meta regioisomeric partners and (perhaps therefore) the most popular regioisomer among medicinal chemists. Yet, para-substitution is (on average) the worst regioisomer with respect to hERG binding and aqueous solubility.

Dean Brown, a colleague of mine, recently discovered an unexpected biasin most (if not all) drug databases by performing exhaustive population analysis of phenyl-ring substitutions. It could be concluded that para-substitution are significantly more often occurring than meta and ortho. In attempt to gauge AstraZeneca medicinal chemists personal preferences regarding aromatic substitution pattern we set up a survey. The result was clear – the primary choice was indeed para. The two main reasons for this preference were: (a) para-substitution provides better protection against metabolism than ortho/meta; (b) the para-position was most likely to boost potency. The first reason was confirmed true whilst the second not.

There could be many reasons for this bias, such as the Topliss work that promoted para-substitutions, a range of possible DMPK (solubility, metabolism) and Safety (hERG) property differences, as well as ligand-binding effects (potency). Other possible factors are synthetic accessibility, cost differences for chemical reagents and historically different design strategies (classic pharmacology vs. target-based design). All of these were scrutinized and it was concluded that the para bias could not be attributed to one single factor. What we do know, however, is that personal preferences and subjectivity still play a pretty big role when selecting reagents for syntheses. In fact, a range of possible preconceptions was recently highlighted when the Dean Brown article was inthepipelined (it’s verb right?). Not to mention that luck influences most everything of what we do.

Why does this matter? Using skewed molecular databases can be risky if one is not aware of any uneven distributions. For example, if there are more para-substituted phenyls in a database than ortho/meta there will be more para hits (from a screen) out of sheer probability. This could in turn lead the inexperienced scientist to assume that the screened target favors para-substitution. Luckily there are remedies – statistical approaches combined with cheminformatics can be used to avoid these issue.

Relying on our intuition is often effective, when making decisions in situations of uncertainty. However, failing to understand the underlying reasons can lead to systematic and predictable errors as the one just described (ease of synthesis is not the reason for the para bias). We hope that our analysis will lead to a broader awareness of unevenly populated databases, a better understanding of how to deal with them to improve our judgments and decisions in medicinal chemistry. To learn more about this, a biased suggestion would be to read our article to see if any of your potential prejudices (regarding phenyl substituents) are supported by data.

Don’t Push That Single (Placebo) Target!

Drug Design Posted on Wed, November 18, 2015 23:27:56

This was first posted:09/29/2014 here

The popular Swedish video game commentator PewDiePie, hosting the currently most subscribed YouTube channel, recently highlighted a game called “Dumb Ways to Die”. One of the dumb ways to die is to press a red button. The only thing you need to do to progress in the game is to resist the urge to press a red button that appears on the screen. It’s surprisingly difficult not to push, and of course PewDiePie pushed it…and probably did many of his 30+ million (!) subscribers that as well.

As a matter of fact, it is likely that clever people such as yourself also have pushed similar buttons, with a clear expectation of what was going to happen. Some of the public buttons, that we push daily, don’t do anything at all. For example, in New York City around 75% of the pedestrian crossing buttons do nothing. Similar numbers have been reported for Los Angeles and for other big cities in the US. Around the globe, many traffic signals have been automated to optimize throughput in intersections. Reason being, if pedestrians were allowed to manually alter the signals (by pushing the buttons) traffic would run less smoothly. A more conspiratorial theory is that cities won’t pay to remove the push-buttons. It’s cheaper to cut the cord, and leave us with the buttons…and the illusion of control. Not a bad thing perhaps. We humans tend to feel better when we do something that we think we have control over, even if we don’t. These “placebo buttons” can occur in other places (e.g. the open/close buttons in elevators), as well as in other forms. For example, many top athletes have their rituals before important games, and stockbrokers often believe they can control the market.

In drug discovery, where uncertainty often is high and predictability can be low, we also tend to resort to a reductionist mode. The simplistic “one drug, one target, one disease” concept has, for example, dominated pharmaceutical research the last decades. Most efforts have been focused on hitting one particular target, hit it hard (nM) and selectively*. Now, this is somewhat difficult to understand. For most drugs we suspect and for lots we now know they interact with more than one target.

With large databases of biological activity becoming publicly available, drugs that previously have been claimed to be selective against one target, have later been shown to hit several targets. For example, the histamine H1 receptor was believed to be the only target for cetirizine and hydroxyzine, which both now have been reported to interact with other GPCRs (in in vitro assays). Similarly, celecoxib is referred to as a COX-2 selective inhibitor; although we now know that it interacts with at least two other targets (carbonic anhydrase II and 5-lipoxygenase).

On the other side of the scale we have the antipsychotic drugs (and multi-kinase inhibitors), where a plethora (>20) of targets have been known for a long time for most of them. Numerous mechanism-of-action hypotheses have been formulated, nearly all trying to tease out one particular target responsible for the therapeutic effect. But no selective drugs have reached the market, despite 50+ years of research for better antipsychotics. My own Ph.D. studies can serve as an illustrative example. In brief, the “dirty” drug clozapine had shown slightly more potency at the dopamine D4 receptor over D2, and hence the leading theory back then was that selective D4 antagonist would be beneficial. We (and others) managed to design selective and high-affinity D4 antagonists, but without the desired therapeutic effect.

It is very clear that not all diseases can be sorted under the “one target, one disease” theory. Humans are complex, and drugs are likely to affect several biochemical responses simultaneously, which in turn will cause feedback reactions on the effected pathways. The chance that the net result linearly correlates to a single target is almost negligible (not to mention degeneracy and robustness in biological system as a central survival mechanism). Not surprisingly, a lesson learnt from the fate of AstraZeneca’s drug pipeline was that 40% of the AZ internal drug projects lack a clear link between the main target and disease.

There are some other facts disfavoring the “one drug, one target” push-button theory. Drugs don’t have to be high-affinity to work; the most widely used drug aspirin has no high-affinity target reported. Some “off-target” activities have been reported to contribute to the efficacy of SSRI’s (e.g. fluoxetine). A drug can act through several different mechanisms and unrelated targets (e.g. ritonavir inhibits both the HIV protease and CYP2D6). Many anti-depressants show different target profiles, but result largely in the same therapeutic effect. Finally, it’s indeed possible to bring new drugs to the market without knowing their targets. There are many (30+) drugs with unknown mechanism of action.

And then there’s the relentless perception that the “one drug, one target” approach will provide inherently safer drugs. The assumption is that drugs cannot cause side-effects via other targets if they’re selective. However logical that may seem there is one thing wrong with that statement – it’s not right. Well, it hasn’t been proven and the jury is still out. Side-effects may indeed come from interaction with the therapeutic target itself, due to no (or little) separation between the efficacious dose and safety related outcomes. Another point that deserves to be stressed is that toxicity can arise from many different mechanisms, and that the term promiscuity in itself can easily be misinterpreted as the more targets you hit the greater the safety concern.

The scientific problems we are trying to solve (and understand) in drug discovery are extremely hard, and we need to recognize the complexity and powerful forces of randomness more. In hindsight, this reductionistic approach must have hampered drug discovery. It’s easy to understand the fondness and general acceptance of the theory. The desire to generate simple (and “not even wrong”) concepts is understandable, particularly when they are easy to measure. Trying to optimize for multiple targets, with optimal phys-chem properties may be viewed as a too challenging task to even start.

Nevertheless, there’s a willingness to challenge this view, and move away from the simplistic target definition view. To be fair, “polypharmacology” (a more fancy word for “dirty”) is used increasingly more often now, and it has been labeled as the “next paradigm in drug discovery”. Regulatory hurdles are fewer: the FDA have recently approved polypharmacological drugs (asenapine, sunitinib, and dronedarone) in several different therapy areas (anticancer, antipsychotics and antidepressants) for different target classes (kinases, GPCRs and ion-channels). In addition, multikinase drugs are believed to suppress mutations and expression changes and thus prevent drug resistance.

But still, at conferences, in the recent literature, and in current drug projects the “one target, one drug” approach very much dominates. There might well be cases when hitting one target will do the trick, but that’s likely to be the exception rather than the norm. Reflections on targets are important, but if we continue trying to find the “right target” and just push one button it’s quite possible that we, just like PewDiePie, end up not progressing…in the game of drug discovery.

* Although the “Term-Which-Must-Not-Be-Named” (Big Data) is indeed here the “compound x target” interaction matrix is still extremely sparse. The word “selective” is thus very much a relative concept, since it only refers to those targets we have data for. Both unexpected positive and negative mechanisms may come from hitting unknown targets. One “easy” solution out of this would of course be to expose our compounds to living animals as early as possibly in drug projects.

Compulsive Gambling, Pregnancy Scores and Matched-Molecular Series

Cheminformatics Posted on Wed, November 18, 2015 23:20:01

This was first posted: 05/29/2014 here

Medicinal chemists are known to display behaviour akin to compulsive gamblers – “this last molecule will solve [fill in the blank] and be the next [candidate] drug”. This can be attributed to a combination of a positive never-give-up attitude and a naive over-confidence in forecasting.To be fair, with more computer power, better algorithms and more data we are indeed constantly getting better at forecasting. But the gains in our ability to predict are still often dwarfed by randomness and the complexity of the problem at hand.

In his wonderful book “The Black Swan” Nassim Taleb colourfully describes that we humans often over-interpret and come up with stories that are used to convince ourselves that we understand the past. Sequences of events are taken and explanations are interlaced into them. This despite the fact that much of what did happen was deemed unlikely, if not insane, before it actually happened. Still, we find it rather trivial to post-rationalize and come up with logic explanations which do not seem crazy after the event happened. This illusion of understanding and our ability to simplify is called the “narrative fallacy”. Take the most famous doping incident in sport for example. It is obvious to everyone now that Lance Armstrong was doped. But before he was caught, Armstrong’s success was credited to hard work and mental superpower. Few people, if any, suspected that he was doped, and it was only after the facts emerged people believed it. And now it obvious to everyone that he had to be doped to win Tour de France seven (!) times in a row.

The human mind is hard-wired to construct stories around events, making them easier to remember. To stretch it just a little, it’s almost as we have a got a mini John Nash sitting on our shoulder (I mean the Russel Crowe-Nash version that could see patterns where there were no patterns, not the real John Nash, the Nobel prize winner version).

Russel Crowe as John Nash in “A Beautiful Mind” can see patterns where there are no patterns (left), whereas the new matched-molecular series tool Matsy can find patterns where there really are patterns (right).

Lo and behold, there are ways to escape the narrative fallacy. By applying hypothesis-based approaches – conducting experiments with testable predictions, knowledge can supersede storytelling. A previous blogpost addresses some of these concerns – Anthony Nicholls at OpenEye and Pat Walters at Vertex are championing for better use of statistics and reproducibility in molecular modelling.

The more information one collects and the smarter one uses it, the better the predictions are likely to be. There are, however, entertaining examples of when collecting too much information and being too clever leads to unwanted results. A personal favourite is when the American retail store Target figured out that a teenage girl was pregnant due to her buying patterns, before her father did (making him quite upset). The way Target went about can be seen as standard. They maintained a database storing everything their customers bought and used math and statistics to find patterns, which could be formed into a “pregnancy score”. They identified key products (scent-free soap, bags of cotton balls, hand sanitizers, washcloths etc.), and when these were combined in certain amounts and ways a pregnancy alert would go off, and targeted advertising went out to the customer. Such recommender system approaches are often very useful. Target realized, however, that revealingthat they had such information freaks people out.

What has this got to do with drug design then? Well…there are many large databases of biological activity data available that could be mined in a similar fashion, using new algorithms and evaluated with statistics. The concept of leveraging such datasets to predict new trends is conceptually very attractive, and is consequently a hot topic in the current medicinal chemistry literature.

In collaboration with Noel O’Boyle and Roger Sayle (NextMove Software) we recentlydescribed a matched-molecular series*method that exploits “Big Data” to recommend what R-group to put on next. Matched-series may be seen as an extension to the popular matched-molecular pairs approach. A draw-back with matched-pairs is that effects are often normally distributed around zero, when investigating big sets of biological data. We found that certain activity orders of R-groups are preferred and the longer the matched series, the more predictive it is. A bit like the pregnancy-score. These observations provide medicinal chemists with a knowledge-based recommender tool to help win the jackpot quicker. The tool is called Matsy, and can consequently be used as a (testable) hypothesis generator for molecular design. In case you are interested in finding out more about this approach, the manuscript is freely available here.

Finally, if you would like to hear more on the “narrative fallacy” just listen to athletes/coaches/fans explaining why they lost/won their latest game. We scientists do not do such things, right?

*A matched molecular series describes a set of molecules with the same scaffold but different R-groups at a particular position

A World Without (New) Drugs – Play It Before You Live It?

Gamification Posted on Wed, November 18, 2015 23:17:28

This was first posted:01/09/2014 here

Gamification is currently peaking on Gartner’s Hype Cycle for Emerging Technologies. Typical during this phase is that some companies take action, whereas many do not. Within drug discovery there are, logically, few examples of successful implementations. The naive idea of this blogpost is to change that…wishful thinking perhaps. For once, like any other new trend, the next phase in the hype cycle is the “trough of disillusionment” where many examples of poor implementations will follow. In addition, the power of gaming is not yet fully understood, and gamers are sometimes seen as “wasting their time” or not “living in real life”. However, playing games can be seen as a happiness engine (if voluntary participation) providing constant rewards at a difficulty level we (often) just can master, unlike real life. When designed correctly, gamification has proven to be successful in engaging people to change behaviours, develop skills and solve problems, in many different areas. To name one specific example, Nike+ has turned fitness into a game, designed to solve motivational issues when it comes to running. Nike+ has now as many as 18 million members worldwide.

To back up a little, computer and video games have come a long way since the days of Pac-Man and Donkey Kong, and so have those who play them. Today’s games are enjoyed by players all ages and backgrounds. According to the iconic game-designer Jane McGonigal, the average young American will have spent more than ten-thousand hours playing video or computer games by the age of 21. This corresponds to the entire time spent in elementary school (1thto the 9th grade) taught all other subjects, in Sweden, where similar gaming patterns are assumed. For those of you familiar with Malcolm Gladwell’s ten-thousand-hours of practise theory, the obvious conclusion is that these youngsters will not just be good gamers; they will be exceptionally good gamers. Hence, we will soon have an entire generation possessing outstanding technical skills at the same thing – gaming. What a human resource! Not to be confused with human resources management.

In her superb book “Reality is broken” McGonigal describes that gamers are exceptionally skilled at one central thing – collaboration. Collaboration may be described as the collective effort of achieving a joint goal and/or joining forces. It’s about creating something that would not be possible to do alone. To me, this sounds a lot like a drug discovery project, as well as many other types of projects of course.

Nike+ and EteRNA both use game design techniques in a non-gaming context. When playing EteRNA, one can interact with thousands of other players with the goal to improve computer models predicting RNA folding.

Within medicine there are games helping patients taking their pills in time, and within drug discovery the most well-known gamification examples are probably Foldit, and a Facebook game about drug discovery called ‘Syrum’. The latter certainly did not engage me (not realistic, not user-friendly and not collaborative). There’s also EteRNA. Similar to Foldit, EteRNA engages users to solve puzzles related to the folding of RNA molecules. It’s really quite cool!

There are of course endless of opportunities to successfully implement gamification within drug discovery. The world is running out of oil, as well as new drugs; there is a serious game called World Without Oil which was created to call attention to and engineer solutions to a possible near-future global oil shortage. One wishful idea would be to copy the WWO game1and design it towards a massive collaborative effort to address the decline in developing new drugs. Interestingly, copying is not frowned upon in the gaming business, it’s rather the opposite.

In many ways computer-aided drug design is similar to (serious) gaming – it fulfils many of the criteria required. In fact, there’s an example going back all the way to the early eighties,2demonstrating a computer game very much like today’s compound library enumeration tools (combined with a prediction). Not too different to my day-to-day job. Although there has been progress since then (e.g. mobile apps3), one can certainly leverage on game techniques even further.

The software does not all have to run on iPads or the new PS4, and include badges and leaderboards, but to reach the next level they need to be much more collaborative and intuitive. Capish?! Do you hear that all my favorite software vendors? – let’s make reality even more fun!

1. The WWO game’s tagline is “Play it – before you live it.

2. Meisenheimer,J.L. ”Design-a-drug: A medicinal chemistry computer game, J. Chem. Educ., 1982, 59, 600

3. “Mobile apps for chemistry in the world of drug discovery”, Drug Discov Today. 2011,16, 928-939

Luck, Molecular Modeling and the Sports Illustrated Curse

Statistics Posted on Wed, November 18, 2015 23:13:18

This was first posted:10/07/2013 here

Accomplish something extraordinary within Sports and you may appear on the prestigious cover of the Sports Illustrated magazine. There’s a downside to that however. An in-depth analysis revealed that a “curse” (i.e. poorer performance in the near future) followed a cover appearance 37.2 percent of the time. The largest effect was seen for golfers, who were jinxed almost 70 percent of the time. The jinx effect may be attributed to an often-forgotten statistical rule termed “regression to mean”.

Wikipedia helps us with the definition: “in statistics, regression to the mean is the phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on its second measurement — and, paradoxically, if it is extreme on its second measurement, it will tend to have been closer to the average on its first”. Hence, after a peak performance, it is likely to go downhill from there. Lindsey Vonn effectively illustrates the effect on the cover in the middle below.

Regression effects occur whenever the correlation between two measures is incomplete, and since regression to mean can predict both the future and the past, it effectively puts causality out of play. The more extreme the accomplishment is the more regression we should expect. This can be difficult to grasp and it is dissatisfying to us humans. Daniel Kahneman, Nobel Prize winner in economic sciences, states in his marvelous book “Thinking, Fast and Slow” that we always want to associate a cause to an effect, and that we have difficulties with handling statistical facts. On top of that, it is a mathematical inevitable consequence that luck influences most everything of what we do. Events happen over which we have no control, and these can carry more weight than our own actions. When asked his favorite equation, Kahneman shared this:

Success = talent + luck

Great success = a little more talent + a lot of luck

If, for example, you play golf and pull off an exceptionally stunning score regression to mean predict the next round more towards the mean (in either time direction!), in the absence of other information. The same effect is obviously also true if you perform a virtual screen and obtain an extraordinary hit-rate. The next time you perform a virtual screen the results are likely not to be as good. You might be jinxed!? One way to make sure that you can draw any conclusions is to compare with a control, for example using a NULL hypothesis.

If, for example, you play golf and pull off an exceptionally stunning score regression to mean predict the next round more towards the mean (in either time direction!), in the absence of other information. The same effect is obviously also true if you perform a virtual screen and obtain an extraordinary hit-rate. The next time you perform a virtual screen the results are likely not to be as good. You might be jinxed!? One way to make sure that you can draw any conclusions is to compare with a control, for example using a NULL hypothesis.

Molecular modeling and virtual screening reports are notoriously bad at accounting for statistical effects. One reason for this is that testing compounds in biology assay is often very expensive, and modelers/chemists/biologists generally do not want to ‘waste’ precious slots with ‘some random compounds’. This is extra problematic for low-throughput assays, since extreme effects are larger for small sample sizes. But, without randomization it is difficult to rule out that an extreme hit-rate was not a twist of fate.

There is also a danger that without proper validation new modeling software with one or two early accidental successes are hyped, and vendors can play on this and not have to worry about people validating their software and coming up unimpressed. As scientists we need to be more mindful about such things. The modeling community is however progressing; general statistics and the quest for reproducibilityare being taken more seriously. In fact, my friend Anthony Nicholls arranged a different conferencedevoted entirely to statistics in molecular modeling this summer (slide decks).

To be fair, other disciplines than molecular modeling can also be sloppy. The Sports Illustrated analysis mentioned above did not, for example, include a NULL model. A colleague of mine hinted that it could just as well be a blessing instead of a curse! Here, I need to come clean and admit that I have misbehaved myself. We recently publisheda virtual screen where shape and electrostatics was used to select a few (68) compounds from a large database (1M). The remarkable result was a hit compound, a fibrinolysis inhibitor four times as potent as the reference compound (an existing drug!). No NULL model was used. My bad. To my defense the method we used had previously proven useful on numerous occasions. There was also a significant amount of luck. The hit compound was acquired and added to the AstraZeneca corporate database only a month before the virtual screen was made.

Finally, might there be a Journal of Medicinal Chemistry curse as well? The cover to the right(above) is made by yours truly to “highlight outstanding research” performed by AZ colleagues and myself (Note: words in italics from J. Med. Chem editors, not me). Was that the kiss of death for me? I hope not. Another classical way to judge if you have an extreme value is to increase the sample size. That is, if you can repeat an extreme occurrence multiple times it can both be outstanding and expected behavior. Golfer Arnold “the more I practice the luckier I get” Palmer has appeared on as many as thirteen Sports Illustrated covers, which is both outstanding and expected for him. I also play golf, is there causal connection?

Searching for E.T. in Space and Drugs in the Cloud

Cool Technology Posted on Wed, November 18, 2015 23:06:02

This was first posted: 06/04/2013 here

A long time ago, in the state of Denmark, something unusual was going on. All the screensavers in the lab had been changed to new ones. The new ones all appeared to be identical, and they had the same purpose – to search for extraterrestrial intelligence in outer space.

I quickly learned that my Ph.D. supervisor wanted to be part of the SETI@home project. SETI@home was (and is) using screensaver technology to analyze radio signals from outer space. It is now a well-known example of distributed computing. The power of distributed computing quickly becomes apparent when analyzing stats from SETI’s first three years – the project accomplished in that time what a single computer would have taken 400,000 years to do.

Within the drug discovery community, Professor Graham Richards at the University of Oxford was an early adopter. Richards and his ”Screensaver Lifesaver” project exploited idle time on 3 million PC’s, cross the globe, to dock 3.5 billion compounds in protein targets. The results from the effort still remain inconclusive however. Among other initiatives, related to drug discovery, Folding@home is probably the most well-known.

Big Pharma are late into the game. Security concerns, both real and imaginary, are/were believed to outweigh possible benefits. This reasoning may be excused since the vast amounts of proprietary data within a company are key assets. Nevertheless, times are changing. Distributed computing within Big Pharma is here – in the shape of a Cloud!

Cloud computing is a synonym for distributed computing over a network and means the ability to run a program (e.g. to search for drugs and aliens) on many connected computers at the same time. The computing resources are typically off-site, available on demand, scalable, and paid per use.

I attended an excellent meeting a few weeks ago. Joe Corkery, VP at OpenEye, gave a very interesting and balanced overview on the current state of Cloud computing in Pharma. It turns out that several Big Pharma’s recently have started to exploit the Cloud; from Virtual Screens to Electronic Lab Notebooks. But the Cloud still generates most traction amongst CROs and small biotechs, where a remotely managed and maintained IT system is seen as of greater economic benefit.

Joe Corkery left stage with a parting thought: “In a pay-per-use world, speed matters.” This observation is often over-looked; the Cloud can be extremely cost-effective when time is a priority. As an example, Joe highlighted that BMS used Amazon’s services to build a research Cloud for running computationally intense PK simulations for their clinical studies. In this fashion they were able to reduce the number of enrolled pediatric subjects from 60 to 40. That’s a significant saving!

Moreover, there is now a real incentive to make software faster since one pays for the time one spends in the Cloud. An incentive for getting happy customers that is. On many occasions we, the customers, do not really ‘need’ faster software. But faster software will be cheaper-to-use software. On that note, in the area of computer-aided molecular design it is not uncommon that algorithms and software have been written by non computer scientists. These are typically people who give accuracy more attention than speed and efficiency, instead of all three. So there might be some room for efficiency improvement here…and it’s always possible to make things more efficient, right? As a consequence, speedier software may lead to new ways of using it and lead to unexpected findings. That said, I am personally not in favor of a (pay-per-use) world where there’s an obvious disincentive to use the software. This presents a hindrance, small though it may be (a few bucks), that might put people off from launching that extra little investigation…that in the end would have made all the difference.

I believe the Cloud computing is going to be a game-changer. My hope is that large-scale computations will provide a springboard for new scientific achievements. That is, providing opportunities for doing things that were completely intractable before (full flexible docking using quantum chemistry, including explicit waters?!), instead of just doing a lot more of the same in the spirit of “let’s switch off the brain and turn on the computers in the Cloud”.

I bet that we’ll find drugs in the Cloud before we find aliens in space. Is there anyone out there, on Earth, willing to take the bet?

Copy More! Copy Better!

Drug Design Posted on Wed, November 18, 2015 22:58:21

This was first posted: 05/07/2013 here

In the business literature and in various seminars, conferences and workshops, we are continuously bombarded with the message that originality and new ideas are good, while copied and old ideas are bad or unethical – criminal even. Such stupidity. Such complete and utter idiocy!

It is not true that original ideas are always the best, or that copying is always inferior. On the contrary, history is full of stories where original thinking failed completely, and copies managed to outdo originals. Take Google, for instance. Google got in the game at a stage of massive expansion, and was at the time just one search engine among many others. If you look at Google today, you’ll see a company famous for its many brilliant web-based services, but also a company where the most used ones tend to be copies or developments of things invented elsewhere. When I say that Google have copied en masse, I say it with praise and envy. Google are brilliant because they are amazing copiers! Copying can be a highly successful strategy, even though it might not sound quite as elegant and alluring as being recognized as a great original.

In a (rare) moment of clarity, I thought what would not be better than copying text myself to illustrate the power of copying. In fact, what you just read has been copied, word by word, from the book “Dangerous Ideas” [1], with permission from author Alf Rehn. Alf Rehn is a former professor of innovation and entrepreneurship. Thinkers 50, the listing of the world’s top 50 business thinkers, recently included him on their Guru Radar…and I love his provocative way of writing.

In the book, Alf’s lists eight “commandments” (or 7 since the 8th is a copy of the 7th) on how to copy better. One commandment reads “Sometimes you just need to change contexts…” – think current efforts on rescuing and repurposing drugs? Another one states “Small changes can generate big effects: “Dancing with the Stars is a copy of American Idol, but with famous amateurs dancing. Whoever thought of that little variation is rich today”. The analogy that comes to mind here are “follow-on” drugs. The most famous example of a “follow-on” drug is probably Levitra, which is basically a short nitrogen-walk from the original Viagra. Levitra sells for an enormous amount of money (total sales 2010: $242,446,000) [2], and helps people to a…eh…very natural way of copying.

Viagra vs Levitra

Numerous drugs are “follow-ons”, and small changes can indeed make for important patient benefits. For example, replacing a twice-a-day with a once-a-day pill (Terazosin vs Prazosin [3]), and switching to ‘personalized’ medicines (some people respond well to Prozac but not Zoloft). Even so, the approach is most often mentioned with a negative connotation. “Follow-ons” are generally not considered to be truly innovative, as well as the relentless debate on their legal and financial aspects. In a recent review, we analyzed the DiMasi and Faden data set [4] of “first-in class–follow-on” pairs on the market. As many as 70% (N=74) of the pairs are characterized by minor structural variations [5]. Thus, whereas it is generally accepted that large changes in molecular structure leads to large variations in properties, we tend to take too lightly on the fact that small molecular changes can also generate big effects.

It should not be forgotten that many astonishing scientific advances come from copying the science of Mother Nature itself. Evidently, many drugs are close analogues of native ligands or natural product. So, the design message is the following. Do not be afraid of seeking inspiration from competitor’s patent specifications, we have provided tools for that [6], or from nature. Biology and chemical space is to your advantage, increasing the odds that your optimized compounds will be novel with a significantly different, and improved, profile. And when encountering a medicinal chemistry related problem that you believe is specific to your structural series, take a moment to reflect. Your next candidate drug might be closer than you think, quite possibly only a few atoms away.

Some final words of wisdom from “Dangerous Ideas” [1]:

I’m obviously not encouraging people to flout copyright law, and just as in any other activity, you need to ensure that you’re behaving in a sensible and ethical way when copying. But our reaction to this tends to be exaggerated and overly cautions, and just like no one wants to be queer zero, we’re all afraid of being seen as less than original. There is no shame in copying. On the contrary, it is a necessity. Instead of turning away, we should make copying our friend, create copying cultures in our organizations, and see how this approach can generate both brilliant new ideas and an understanding for the reinvention of wheels. We have nothing to lose but our preconceived notions.

Rock on! [7] Alf


  1. Rehn, A. (2011) “Dangerous Ideas: When Provocative Thinking Becomes Your Most Valuable Asset”,
  3. Kyncl, J.J. (1986) “Pharmacology of Terazosin” Am. J. Med. 80, 12–19
  4. DiMasi, J.A. and Faden, L.B. (2011) “Competitiveness in follow-on drug R&D: a raceor imitation?”Nat. Rev. Drug Discov. 10, 23–27
  5. Giordanetto, F., Boström, J. and Tyrchan C. (2011) “Follow-on drugs: how far should chemists look?” Drug Discovery Today, 16, 722-732.
  6. Tyrchan, C. Boström, J. Giordanetto, F., Winter and Muresan S. (2012) ”Exploiting structural information in patent specifications for key compound prediction” J. Chem. Inf. Model. 52, 1480–1489.
  7. Personal communication with Alf Rehn.

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