Jim Simons: "We never override the computer"

Billionaire-mathematician Jim Simons has been called the most successful investor of all time. His Renaissance Technologies hedge fund has returned an average of 40% returns (after fees) over the last 20 years. The firm uses proprietary mathematical algorithms and models to exploit statistical asymmetries and fluctuations in stock prices to leverage price differentials and make money. 

Simons had made groundbreaking contributions to algebraic topology before founding Renaissance, and his background enabled him to recruit top mathematicians, computer scientists, physicists and statisticians to the company. In fact the company actively stays away from recruiting anyone with a financial or Wall Street background.

I've been enjoying the recent biography of Simons, "The Man Who Solved the Market", by Gregory Zuckerman. But there's an interesting video of a Simons talk at San Francisco State University from 2014 in which he says something very intriguing about the models that Renaissance builds:

"The only rule is that we never override the computer. No one ever comes in any day and says the computer wants to do this and that’s crazy and we shouldn’t do it. You don’t do it because you can’t simulate that, you can’t study the past and wonder whether the boss was gonna come in and change his mind about something. So you just stick with it, and it’s worked."

It struck me that this is how molecular modeling should be done as well. As I mentioned in a previous post, a major problem with modeling is that it's mostly applied in a slapdash manner to drug discovery problems, with heavy human intervention - often for the right reasons, because the algorithms don't work great - obscuring the true successes and failures of the models. But as Simons's quote indicates, the only way to truly improve the models would be to simply take their results at face value, without any human intervention, and test them. At the very minimum, "simulating" historical human intervention is going to be pretty hard. So the only way we'll know what works and what doesn't is if we trust the models and let them rip through. As I pointed out though, in most organizations experimenters are simply not incentivized, nor are there enough resources, to carry out this comprehensive testing. 

Jim Simons and Renaissance can do it because 1. They have the wisdom to realize that that's the only way in which they can get the models to work and 2. They have pockets that are deep enough so that even model failures can be tolerated. Most drug discovery organizations, especially smaller ones, presumably can't do 2. But they could still do it in a limited sense in a handful of projects. What's really necessary though is 1. and my concern is that we'll be waiting for that even if we have the resources to do 2.

Review: James Hornfischer's "The Fleet at Flood Tide".

A superb book on the last year of the war in the Pacific Theater, full of incredible details about underappreciated leaders like Raymond Spruance (commander of the Fifth Fleet, the navy's primary strike force against Japan), Hollland Smith (head of amphibious operations), Draper Kauffman (creator of the Underwater Demolition Teams that later became the SEALS) and Paul Tibbets (pilot of the Enola Gay), along with other remarkable men and women, both American and Japanese. Among these were Guy Gabaldano, a Mexican-American marine who coolly talked 800 Japanese soldiers into surrendering and Shizuko Miura, an 18-year-old nurse, wise beyond her years, who held out on the island of Saipan.

The book is primarily about the invasion of Saipan, Tinian and Guam, the three islands constituting the Marianas that were considered crucial to staging air attacks by B-29s against the Japanese mainland. The brilliant island hopping strategy that King, Nimitz and Halsey orchestrated saw its culmination in the invasion of the Marianas, followed by the infamous battles of Iwo Jima and Okinawa. The sheer difficulties of logistics and air support involved in carrying out strikes against tiny specks of land separated by thousands of miles in the face of an implacable foe who has the home advantage are driven home well by Hornfischer. It was from Tinian that the Enola Gay and Bockscar that dropped the atomic bombs on Hiroshima and Nagasaki took off. The last part of the book deals with the aftermath of the bombing and the occupation of Japan.

There are two central themes pervading the book. One is the key role the navy and its aviators played in securing the islands. The other is the sheer fanaticism and tenacity demonstrated by the Japanese that convinced the Allies how expensive an invasion of Japan could be. The most horrifying description is regarding the mass suicides on Saipan in which - chilled into desperate fear by Japanese propaganda which warned the civilians of the untold horrors that Americans would inflict on them - thousands of mothers and fathers killed their children and jumped off the cliffs. The few Japanese who were actually captured by American soldiers were astonished by the humane treatment their received. The propaganda was so extreme that the Japanese people as a whole were getting ready to commit national suicide in the service of national salvation when the mainland was going to be invaded.

This played a critical role in the decision to use the bombs and Hornfischer is unapologetic about the decision. His main argument - with which I largely agree - is that the fanaticism displayed by the Japanese along with the paralysis that their leadership exhibited in sending out any clear signals to accept the terms of unconditional surrender made it impossible for the Allies to assume that Japan was anywhere close to surrendering. Later historians have always stressed that the Japanese would have surrendered if they had been allowed to keep their Emperor, a mortal descended from a God. But they never made this intention clear, and even when they did, it came with a list of other unacceptable conditions like retaining the authority to try their own war criminals. The unavoidable fact is that by the summer of 1945, the sheer barbarity and fanaticism of the Pacific War had made ending it a matter of desperate urgency. About the only two other options apart from using the bombs would have been a prolonged starvation of the Japanese people by the navy or an invasion that would have easily caused a quarter of a million casualties. 

Note that the question of whether the Allies thought that using the bombs was necessary is separate from whether the bomb *actually* caused Japan to surrender. The historical scholarship on that second question seems to mainly conclude that it was the entry of the Soviet Union through the invasion of Manchuria from the north that finally caused the Japanese leadership to surrender: curiously Hornfischer practically ignores a detailed discussion of this argument, which shows his bias a bit. But even if this is true, it ignores the fact that eventually it was Emperor Hirohito who really broke the deadlock; even *after* Nagasaki was bombed, the war leadership was still divided.

The book concludes by describing the American occupation of Japan which was unprecedented in its decency and progressivism; it was perhaps MacArthur's finest hour. The war against Japan and its subsequent occupation stand as a fine example of both the atrocities that human beings inflict on each other and the redemption that can salvage them.

Malcolm Gladwell's "The Bomber Mafia" - Weak Tea

Just finished reading Malcolm Gladwell's new book, "The Bomber Mafia", and am sorely disappointed. It's like Gladwell has expanded a short blog post into a 150 page book that's big on storytelling but essentially a complete lightweight when it comes to content and conclusions.

The basic thesis of the book can be summed up in a few short sentences: During WW2, there was a group of air force officers led by Haywood Hansell called the Bomber Mafia who thought that they could bomb cities "more morally" through daytime precision bombing; they were hedging their bets on a revolutionary invention, the Norden bombsight, that allowed bombardiers to pinpoint targets from 15,000 feet. In reality, the philosophy failed miserably because the bombsight was less than perfect under real conditions, because even with the bombsight the bombs were not that precise, and most crucially because in Japan, the hitherto undiscovered jet stream which buffeted airplanes with 120 knot winds basically made it impossible for the B-29s to stabilize and successfully bomb their targets. 

Enter Curtis LeMay, the ruthless air force general who took the B-29 down to 5000 feet to avoid the jet stream, ripped most of the guns out and instead of precision bombs, used incendiary bombs with napalm at night to burn down large built up civilian areas of Japanese with horrific casualties, the most famous incident of course being the March 1945 firebombing of Tokyo that killed over 100,000 people.

Gladwell tells these stories and others like the invention of napalm well, but the core message in the book is that the switch from precision bombing by Hansell which failed to strategic bombing by LeMay which presumably worked was the linchpin air strategy of the war. This message is a highly incomplete and gross oversimplification. The fact of the matter is that strategic bombing did very little damage to morale and production until very late in the war. And while strategic bombing in Japan was more successful, the bombing in Europe did not work until the bombers were accompanied by long-range P-51 Mustang fighters, and even then its impact on shortening the war was dubious. Even in Japan, strategic bombing could have been hobbled had the Japanese had better fighter defenses the way the Germans did. The Germans used a novel method of firing called "Schräge Musik" that allowed their fighters to shoot at the British Lancaster bombers vertically - if the Japanese had used such efforts they would likely have been devastating to LeMay's strategy. Even from a civilian standpoint, the strategic bombing of Dresden and Hamburg did little to curb either morale or production. But in talking only about Tokyo and not Dresden or Hamburg, only about Japan and not Europe, Gladwell leaves the impression that strategic bombing was pretty much foolproof and always worked. These omissions are especially puzzling since he does discuss the lack of effectiveness of the bombing of civilians in London during The Blitz.

There are very few references in this short book - Gladwell seems obsessed with quoting two historians named Tami Biddle and Stephen McFarland for most of the discussion. These are fine historians, but the superficial treatment is especially jarring because strategic bombing has been written about extensively during the last several decades by historians like Richard Overy and Paul Kennedy. The Nobel Prize-winning physicist Patrick Blackett wrote about the mistaken assumptions about strategic bombing way back in the 1950s. I would also recommend physicist Freeman Dyson's essays on the part he himself played in strategic bombing during the war that really drives home how boneheaded the method was. But Gladwell quotes none of these sources, instead just focusing on Haywood Hansell and Curtis LeMay as if they and their thoughts on the matter were the only things that counted.

Perhaps worst of all, the complex moral consequences of LeMay's argument and strategic bombing in general are completely sidelined except for a short postscript in which he discusses how precision bombing has gotten so much better (except in that case the moral consequences have also gotten more complex, precisely because it's become easier). Strategic bombing was wasteful and morally unforgivable because it cost both pilot and civilian lives. LeMay generally receives a very favorable treatment and there are copious quotes from him, but interestingly the one quote which is missing is one which might have shed a different perspective - this is his quote after the war that he would have been hanged as a war criminal had the Allies lost.

I really wish this book were better, given Gladwell's fine storytelling skills which can draw the reader in. As it stands it's slim pickings, a couple of anecdotes and stories compressed as a grand philosophical message in 150 pages that leaves the reader completely unsatisfied. If you are really interested in the topic of bombing during WW2, look at other sources

The human problems with molecular modeling

Molecular modeling and computational chemistry are the neglected stepchildren of pharmaceutical and biotech research. In almost every company, whether large or small, these disciplines are considered "support" disciplines, peripheral to the main line of research and never at the core. At the core instead are synthetic chemistry, biology and pharmacology, with ancillary fields like formulations and process chemistry becoming increasingly important as the path to a drug progresses.

In this post I will explore two contentions:

1. Unless its technical and human problems are addressed, molecular modeling and simulation will remain peripheral instead of core fields in drug discovery.

2. The overriding problem with molecular modeling is the lack of a good fit between tools and problems. If this problem is addressed, molecular modeling stands a real chance of moving from the periphery to, if not the very core, at least close to the core of drug discovery.

There are two kinds of challenges with molecular modeling that practitioners have known for a long time - technical and human. The technical problems are well known; although great progress has been made, we still can't model the details of biochemical systems very accurately, and even key aspects of these systems like protein motion, water molecules and - in case of approaches like machine learning - lack of adequate benchmarks and datasets continue to thwart the field. 

However, in this piece I will focus on the human problems and explore potential ways of mitigating them. My main contention is that the reason modeling often works so poorly in a pharmaceutical setting is because the incentives of modelers and other scientists are fundamentally misaligned. 

In a nutshell, a modeler has two primary objectives - to make predictions about active, druglike molecules and to validate the models they are using. But that second part is actually a prerequisite for the first - without proper validation, a modeler cannot know if the exact problem space they are applying their models to is actually a valid application of their techniques. For proper validation, two things are necessary:

1. That the synthetic chemist actually makes the molecules they are suggesting.

2. That the synthetic chemistry does not make molecules which they aren't suggesting.

In reality the synthetic chemist who takes up the modelers' suggestions has little to no interest in model validation. As anyone who has done modeling knows, when a modeler suggests ten compounds to a synthetic chemist, the synthetic chemist would typically pick 2 or 5 out of those 10. In addition, the synthetic chemist might pick 5 other compounds which the modeler never recommended. The modeler typically also has no control and authority over ordering compounds themselves.

The end result of this patchwork implementation of the modeler's predictions is that they never know whether their model really worked. Negative data is especially a problem, since synthetic chemists are almost never going to make molecules that the modeler thinks will be inactive. You are therefore left with a scenario in which neither the synthetic chemist nor the modeler knows or is satisfied with the utility of the models. No wonder the modeler is relegated in the back of the room during project discussions.

There is another fundamental problem which the modeler faces, a problem which is actually more broadly applicable to drug discovery scientists. In one sense, not just modeling but all of drug discovery including devices, assays, reagents and models can be considered as a glorious application of tools. Tools only work if they are suited to the problem. If a practitioners thinks the tool will be unsuited, they need to be able to say so and decline using the tool. Unfortunately, incentive structures in organizations are rarely set up for employees to say "no". Hearing this is often regarded as an admission of defeat or an unwillingness to help out. This is a big mistake. Modelers in particular should always be rewarded if they decline to use modeling and can gives good reasons for doing so. As it stands, because they are expected to be "useful", most modelers end up indiscriminately using their tools on problems, no matter what the quality of the data or the probability of success is. This means that quite often they are simply using the wrong tool for the wrong problem. Add to this the aforementioned unwillingness of synthetic chemists to validate the models, and it's little surprise that modeling so often fails to have an impact and is relegated to the periphery.

How does one address this issue? In my opinion, the issue can be mitigated to a significant extent if modelers know something about the system they are modeling and the synthesis which will yield the molecules they are predicting. If a modeler can give sound reasons based on assays and synthesis - perhaps the protein construct they are using for docking is different from one in the assay, perhaps the benchmarks are inadequate or perhaps the compounds they are suggesting won't be amenable to easy synthesis because of a weird ring system - other scientists are more likely to both take their suggestions more seriously as well as respect their unwillingness to use modeling for a particular problem. The overriding philosophy that a modeler utilizes should be captured not in the question, "What's the best modeling tool for this problem?" but "Is modeling the right tool for this problem?". So, the first thing a modeler should know is whether modeling would even work, but if not, he or she will go a long way in gaining the respect of their organization if they can say at least a few intelligent things about alternative experimental approaches or the experimental data. There is no excuse for a computational chemist to not be a chemist in the first place.

More significantly, my opinion is that this mismatch will not be addressed until modelers themselves are in the driver's seat, until they can ensure that their predictions are tested in their entirety. Unfortunately there's little control modelers have over testing their models; much of it simply depends on how much the synthetic chemists trust the modelers, a relationship driven as much by personality and experience as modeling success. Even today, modelers can't usually simply order their compounds for synthesis from internal or external teams.

Fortunately there are two very significant recent developments that promise modelers a degree of control and validation that is unprecedented. One is the availability of cheap CROs like WuXi and Enamine which can make many of the compounds that are predicted by modeling. These CROs have driven the cost down so significantly that even negative predictions can now importantly be tested. In general, the big advantage of external CROs relative to internal chemists is that you can dictate what the external CROs should and shouldn't make - they won't make compounds which you don't recommend and they will make every compound that you do; the whims of personal relationships won't make a difference in a fee-for-service structure.

More tantalizingly, there have been a few success stories now of fully computationally-driven pipelines, most notably Nimbus and Morphic Therapeutic and, more recently, Silicon Therapeutics. When I say "fully computationally driven" I don't mean that synthetic chemists don't have any input - the inaccuracy of computational techniques precludes fully automated molecule selection from a model - what I mean is that every compound is a modeled compound. In these organizations the relationship between modeling and other disciplines is reversed, computation is front and center - at the core - and it's synthetic chemistry and biology in the form of CROs that are at the periphery. These organizations can ensure that every single prediction made by modelers is tested and made, or conversely, that no molecule that is made and tested fails to go through the computational pipeline. At the very least, you can then keep a detailed bookkeeping record of how designed molecules perform and therefore validate the models; at best, as some of these organizations showed, you can discover viable druglike leads and development candidates.

Computational chemistry and modeling have come a long way, but they have a long way to go both in terms of technical and organizational challenges. Even if the technical challenges are solved, the human challenges are significant and will hobble the influence computation has on drug discovery. Unless incentive structures are aligned the fields will continue to have poor impact and be at the periphery. The only way for them to progress is for computation to be in the driver's seat and for computational chemists to be as informed as possible. Fortunately with the advent of the commodification of synthesis and the increased funding and interest in computationally driven drug pipelines, it seems there may be a chance for us to find out how well these techniques work after all.

Image source

Book review: Charles Seife’s “Hawking Hawking”

I still remember the first time I encountered “A Brief History of Time”. I must have been in high school. I marveled at the elfin-looking bespectacled man on the cover who looked like an alien. And were the contents the very definition of exotic or what. Clearly I understood very little of what was written about black holes, the Big Bang and quantum theory, but the book definitely got me hooked on to both cosmology and Stephen Hawking and cemented the image of the scientist in my mind as some kind of otherworldly alien superintelligence.

Now I just finished Charles Seife’s unique, must-read contribution to Hawking biography, “Hawking Hawking” and realize that in fact that was the intended effect.  Seife’s book does a first-rate job of stripping the myth-making, hype and self-promotion from the celebrity and revealing the man inside in all his triumph and folly. The achievement is all the more remarkable since Seife did not have access to Hawking’s personal papers and family members, resources which the foundation set up after his death guards carefully in order to preserve the image.

The book recounts several episodes of Hawking being very human; of opposing scientists who did not agree with his ideas and trying to hobble their professional advancement, of playing favorites and denying credit to others, of neglecting and mocking his wife and her work in the humanities, of making pronouncements especially in his last years about topics that were far beyond his expertise and which the media and the public held up as sacrosanct - an image that he not only didn’t do much to dispel but often encouraged. Of course, all scientists can occasionally be cruel, vain, jealous and egotistical, but these qualities of Hawking were hidden behind a blitz of media publicity.

And yet the book is not a takedown in any way. It acknowledges Hawking’s brilliant and important contributions to science, especially his key discovery of Hawking radiation that married general relativity and quantum theory in a tour de force of calculation. Seife sensitively describes how much Hawking struggled because of his terrible diseases, and how ambivalent he was about the media and public highlighting his disability. Much of the public never understood how hard even doing calculations was for him, even aided by his powerful memory and remarkable imagination. It’s not surprising that a lot of his best work was done with collaborators, brilliant scientists in their own right whose names the public never remembered.

Ultimately, although Hawking seems to have contributed to a good deal of self-promotion and myth-making himself, he seems to have been much more in touch with the inner human being than what he let on. In distinguishing what was real from what was hype, Seife gives Hawking his rightful place in science, not as another Newton or Einstein but as Stephen Hawking.

Hawking Hawking: The Selling of a Scientific Celebrity https://www.amazon.com/dp/1541618378/ref=cm_sw_r_cp_api_glt_i_8H7P48KA10T7XX43N1VN

Chandra and Johnny come close to discovering black holes

This is from Jagdish Mehra and Helmut Rechenberg's monumental "The Historical Development of Quantum Mechanics, Vol. 6, Part 2". With Chandrasekhar's facility with astrophysics and von Neumann's with mathematics, there is little doubt in my mind that they would have succeeded.

As it happened, it was Oppenheimer and his student Hartland Snyder who wrote the decisive paper describing black holes in 1939. 

The timing was bad, though; on the same day that the paper came out in the Physical Review, Germany attacked Poland and started World War 2. Far more consequential was another paper published on the same day in the same issue - John Wheeler and Niels Bohr's liquid drop model of nuclear fission.

"Hawking Hawking" and Michio Kaku

Two items of amusement and interest. One is a new biography of Hawking by Charles Seife, coming out tomorrow, that attempts to close the gap between Hawking’s actual scientific accomplishments and his celebrity status. Here's a good review by top science writer and online friend Philip Ball:

https://www.prospectmagazine.co.uk/magazine/stephen-hawking-celebrity-physicist-flaws

Seife's Hawking is a human being, given to petty disputes of priority and oneupmanship and often pontificating with platitudes on fields beyond his expertise. I used to have similar thoughts about Hawking myself but thought that his pronouncements were largely harmless fun. My copy of Seife's book arrives tomorrow and I am looking forward to his views, especially his take on how much it was the media rather than Hawking himself who fueled the exaggerations and the celebrity status.

The second item is an interview with Michio Kaku which seems to have ruffled a lot of feathers in the physics and science writing communities. 

https://www.theguardian.com/science/2021/apr/03/string-theory-michio-kaku-aliens-god-equation-large-hadron-collider?fbclid=IwAR2u1XzDLgOngBsWSpi6y8xGRRh_e3n4TWEtSMpk4KXST-oqPgldQPMjhas

The critics complain that he distorts the facts and says highly misleading things like string theory directly leading to the standard model. I hear the complaints as legitimate, but my take on Kaku is different. I don’t think of him as a science writer but as a futurist, fantasist and storyteller. I think of him rather like E. T. Bell whose “Men of Mathematics”, while highly romanticized and inaccurate regarding the details, nevertheless served to get future scientists Freeman Dyson and John Nash interested in math as kids. I doubt whether either Kaku himself or his readers take the details in his books very seriously.

I think we should always distinguish between writers who write about the facts and writers who tell stories. While you should be as rigorous as possible while writing about facts, you are allowed considerable leeway and speculation while telling stories. If not for this leeway, there wouldn't be any science writers and certainly on science fiction writers. A personal memory: my father was a big fan of Alvin Toffler's "Future Shock" and other futuristic musings. But he never took Toffler seriously as a writer on technology; rather he thought of him as an "ideas man" whose ideas were raw material for more serious considerations. If Kaku's writings get a few kids excited about science and technology the way "Star Trek' did, his purpose would be served.

Six lessons from the biotech startup world

Having worked for a few biotech startups over the years, while I am not exactly a grizzled warrior, I have been around the block a bit and have drawn some conclusions about what seems to work and not work in the world of small biopharma. I don't have any kind of grand lessons related to financial strategy, funding or IPOs or special insights, just some simple observations about science and people based on a limited slice of the universe. My suspicion is that much of what I am saying will be familiar.

1. It's about the problem, not about the technology: 

Many startups are founded with a particular kind of therapeutic area in mind, perhaps a particular kind of cancer or metabolic disease to address. But some are also founded on the basis of an exciting new platform or technology. This is completely legitimate as long as there is also a concomitant list of problems that can be addressed by that platform. If there aren't, then you are in the proverbial hammer-trying-to-find-a-nail territory, trying to be tool-oriented rather than problem-oriented. The best startups I have seen do what it takes to address a problem, sometimes even pivoting from their original toolkit. The not so great ones fall in love with the platform and technology so much that they keep on generating results from it in a frenzy that may or may not be applicable to a real problem. No matter how amazing your platform may be, it's key to find the right problem space as soon as you can. Not surprisingly, this is especially an issue in Silicon Valley where breathless new technology is often the basis for the founding platform for companies. Now I am as optimistic and excited about new technology as anyone else, but with new technological vision must come rigorous scrutiny that allows constant validation of the path that you are on and course-correction if that path looks crooked.

A corollary of this obsession with tools comes from my own field of molecular modeling and structure-based drug design. I have said before that the most important reason computational chemistry stays at the periphery rather than core of drug discovery is because it's not matched to the right problem. And while technical challenges still play a big role in the failure of the field - the complexity of biology usually far overshadows the utility of the tools - the real problem in my view is cultural. In a nutshell, modelers are not paid for saying "no". A modeler constantly has to justify his or her utility by applying the latest and greatest tools to every kind of problem. It doesn't matter if the protein structure is poorly resolved; it doesn't matter if the SAR is sparse; it doesn't matter if you have one static structure for a dynamic protein with many partners - the constant clink of your hammer in that corner office must be heard if your salary is to be justified. It's even more impressive, and correspondingly more futile, if you are using The Cloud or a whole bank of GPUs for your calculations (there are certainly some cases where sheer computing power can make a difference, but these are rare). There are no incentives for you to say, "You know what, computational tools are really not the best approach to this problem given the paucity and quality of data." (as Werner Heisenberg once said, the definition of an expert is someone who knows what doesn't work).

But it goes both ways. Just like management needs to not just allow but reward this kind of judicious selection and rejection of tools, it really helps if modelers know something about assays, synthesis and pharmacology so that they can provide an alternative suggestion to using modeling, otherwise you are just cursing the dark instead of lighting a candle. They don't need to be experts, but having enough knowledge to make general suggestions helps. In my view, having a modeler say, "You know what, I don't think current computational tools are the best way to find inhibitors for this protein, but have you tried biophysical assay X" can be music to the ears.

2. Assays are everything

In all the startups I have worked at, no scientist has been more important to success in the early stages of a drug discovery project than the assay expert. Having a well designed assay that mirrors the behavior of a protein under realistic conditions is worth a thousand computer models or hundreds of hours spent around the whiteboard. Good assays can both test and validate the target. Conversely, a badly designed assay, one that does not recapitulate the real state of the protein, can not only doom the project but lead you down a rabbit hole of false positives. No matter what therapeutic area or target you are dealing with, there are going to be few more important early hires than people who know the assays. And assays are all about the details - things like salt and protein concentration, length of construct, mutations, things only known by someone who has learnt them the hard way. The devil is always in the details, but he really hides in the assays.

3. Outsourcing works great, except when it doesn't

Most biotechs now outsource key aspects of their processes like compound synthesis, HTS and biophysical assays to CROs. And this works fine in many cases, except when that devil in the details rears his head. The problem with many CROs is that while they may be doing a good job of executing on the task, they then throw the results over the wall. The details are lost, and sometimes you don't even know you are going down a rabbit hole when that happens. I remember one example where the contamination of a chip in a SPR binding assay was throwing off our results for a long time, and it took a lot of forensic work and back and forth to figure this out. Timelines were set back substantially and confusion reigned. CROs need to be as collaborative and closely involved as internal scientists, and when this doesn't happen you can spend more time fixing that relationship than actually solving your problem - needless to say, the best CROs are very good at doing this kind of collaborative work. And it's important not just to have collaborative CROs but to have access to as many details as possible in case a problem arises, which it inevitably does.

4. Automation works great, except when it doesn't

The same problems that riddle CRO collaborations riddle automation. These days some form of automation is fairly common for tools like HTS, what with banks of liquid handling robots hopping rapidly and merrily over hundreds of wells in plates. And it again works great for pre-programmed protocols. But simple problems of contamination, efficiency and breakdowns like spills and robotic arms getting stuck can afflict these systems, especially in the more cutting-edge areas like synthesis - one thing you constantly discover that the main problem with automation is not the software but the hardware. I have found that the same caveats apply to automation that Hans Moravec applied to AI - the hard things are easy and the simple things are hard. Getting that multipipetting robot to transfer nanoliters around blazingly fast is beyond the ability of human beings, but that robot won't be able to look at a powder and determine if it's fluffy or crystalline. Theranos is a good example of the catastrophe that can result when the world of well-defined hard robotic grippers and vials meets the messy world of squishy cells, fluffy chemicals and messy fluids like blood (for one thing, stuff behaves very differently at small scale). You know your automation has a problem when you are spending more time babysitting the automation than doing things manually. It's great to be able to use automation to free up your time, but you need to make sure that it's actually doing so as well as generating accurate results without needing babysitting.

5. The best managers delegate

Now a human lesson. I have had the extraordinary good fortune of working for some truly outstanding scientists and human beings, some of whom have become good friends. And I have found that the primary function of a good manager is not to get things done from their reports but to help them grow. The best way to encapsulate sound manager thinking is Steve Jobs's famous quote - "It doesn't make sense to hire good people and tell them what they should do. We hire good people so that they can tell us what to do." The best managers I have worked with delegate important responsibilities to you, trust that you can get the job done, and then check in occasionally on how things are going, leaving the details and execution to you. Not only does this provide a great learning experience but more importantly it helps you feel empowered. If your manager communicates to you how important the task entrusted to you is for the entire company and how they trust you to do it well, the sense of empowerment this brings is enormous and you will usually do the job well (if you don't, it's a good sign for both you and your manager that things are not going well and a conversation is to be had). 

Bad managers are of course well known - they micromanage, constantly tell you what you should do and are often not on top of things. And while this is an uncomfortable truth to hear, often the best scientists are also the poorest managers (there's exceptions of course - Roy Vagelos who led Merck during its glory days excelled at both). One of the best scientists I have ever encountered wisely and deliberately stay away from senior managerial positions that repeatedly came his way. There are few managers worse than distracted scientists.

6. Expect trouble and enjoy the journey

I will leave the most obvious observation for last. Biology and drug discovery are devilishly complicated, hard and messy. After a hundred years of examining life at the molecular level, we still haven't figured it out. Almost every strategy you will adopt, every inspired idea you will have, every new million-dollar tranche of funding you will sink into your organization, will fail. No model will be accurate enough to capture the real life workings of a drug in a cell or a gene that's part of a network of genes, and you will have to approximate, simplify, build model systems and hope for the best. And on the human side, you will have disagreements and friction that should always be handle with considerateness and respect. Be forgiving of both the science and the people since both are hard. But in that sense, getting to the right answer in biotechnology is like building that "more perfect union" that Lincoln talked about. It's a goal that always seems to be one step beyond where you are, but that's precisely why you should enjoy the journey, because you will find that the gems you uncover on the way make the whole effort worth it.

Some thoughts on "broader impact" statements for scientific papers

I know that conferences like NeurIPS (formerly called NIPS) have asked for statements about ethical and "broader impact" to accompany papers submitted to them. In principle I am all for this since it's always good for scientists to think about the social implications of the work. I also read the details of the requirements and they aren't draconian, especially for highly theoretical papers whose broader impact is far from clear.

But from a fundamental philosophical viewpoint I still don't think this is useful. My problem is not with the morality of predicting impact but with the lack of utility. The history of science and technology show that it is impossible to predict broader impact. When Maxwell published his electromagnetic equations he could have scarcely imagined the social and political repercussions electrical power generation would have. When Einstein published his general theory of relativity, he could have scarcely imagined the broader impact it would have on space exploration and GPS, both in war and peace. Perhaps most notably, nobody could have predicted the broader impacts of the discovery of the proton or the discovery of DNA as the genetic material. I do not see how James Chadwick or Oswald Avery could have submitted broader impact statements with their papers; anything interesting they might have had to say would probably have been untrue a few years later, and anything they would have admitted would probably have turned out to be important.

My biggest problem is not that broader impact statements will put more of a burden on already-overworked researchers, or that they might inflame all kinds of radical social agendas, or that they might bias conferences against very good technical papers which struggle to find broad impact, or that they might stifle lines of research which are considered to be dangerous or biased. All these problems are real and should be acknowledged. But the real problem simply is that whatever points these statements would make would almost certainly turn out to be wrong because of the fundamental unpredictability and rapid progress of technology. And they would then only cause confusion by sending people down a rabbit hole, one in which the rabbit not only does not exist but is likely to be a whole other creature. And this will be the case with all new technologies like AI and CRISPR.

The other problem with broader statements is what to do with them even if they are accurate, because accurate and actionable are two different things. Facial recognition software is an obvious example. It can be used to identify terrorists or bad guys but it can also be used to identify dissidents and put them into jail. So if I submit a broader statement with my facial recognition paper and point out these facts, now what? Would this kind of research be banned? That would be throwing the baby out with the bathwater. The fact is that science and technology are always dual use, and it is impossible to separate their good from their bad uses except as a matter of social choice after the fact. I am not saying that pointing out this dual use is a bad thing, but I am concerned that doing so might lead to stifling good research for fear that it may be put to bad ends.

So what is the remedy? Except for obvious cases, I would say that science and technology should be allowed to play out the way they have played out since the time of Francis Bacon and the Royal Society, as open areas of inquiry with no moral judgements being made beforehand. In that sense science has always put a severe burden on society and has asked for a tough bargain in return. It says, "If you want me to be useful, don't put me in a straitjacket and try to predict how I will work out. Instead give me the unfettered freedom of discovery, and then accept both the benefits and the risks that come with this freedom." This is the way science has always worked. Personally I believe that we have done an excellent jobs maximizing its benefits and minimizing its risks, and I do not see why it will be different with any new technology including machine learning. Let machine learning run unfettered, and while we should be mindful of its broader impact, predicting it will be as futile as damming the ocean.

Book Review: "His Very Best: Jimmy Carter, A Life" by Jonathan Alter

I first saw Jimmy Carter with a few other students during my first year as a graduate student at Emory University where he remains a visiting professor - I still remember the then 81-year-old briskly striding in with his signature broad, toothy smile and the energy of a man half his age. The amusing thing was that when he opened up the floor to any question that we wanted to ask him, the first question someone asked right away was whether LBJ was responsible for JFK's assassination. Without batting an eyelid Carter said no and moved on.

Now I am finishing Jonathan Alter's comprehensive biography of Carter and it's a revelation. The book's main goal is to show that Jimmy Carter is a much more complex human being and president than what people believe, and it succeeds exceedingly well in this goal. Carter was a highly intelligent, immensely hard working and, most importantly, a good man in the wrong job. But even then, the things he accomplished were very substantial. Most important were his two signature foreign policy achievements - giving the Panama Canal back to Panama, and brokering a peace between Israel and Egypt which has lasted up to to the present day. The Camp David Accords in particular showed a commitment over thirteen days that is without precedent before or since; Carter simply refused to give up, even when Begin and Sadat were on the verge of going home multiple times. The other huge achievement - albeit one that now seems like a mixed blessing - was to normalize relations with China. He also campaigned for human rights at a time when it wasn't fashionable for American presidents to do so, abandoning American presidents' traditional cozy relationship with anti-communist dictatorships.

There are also other, more minor achievements that are now forgotten - appointing more liberal federal judges (even more than Trump), deregulating the airline industry, appointing more African-Americans to important government positions than many of his predecessors, restoring a sense of decency and common sense to the White House after the tumultuous years of Vietnam and lackluster years of Ford, being the first Democratic president to woo evangelicals (the last before they turned Republican) and popularizing alternative energy and climate change at a time when few people cared about it. There's no doubt that the binary classification of Carter as "failed president, great ex-president" is flawed and reality is more complex.

The book is also fabulous at exploring Carter's childhood and background in rural Georgia as a farmer and his education in nuclear engineering at Annapolis. Carter grew up as the son of a farmer and general store owner, Earl Carter, who competed with his son in daily tasks and sports and was a fair if harsh father. Carter's mother Lillian who lived to see her son became president was quite liberal for her time, and astonishingly joined the Peace Corps and went to India for a few months for public service in her sixties. Alter does not shirk from criticizing Carter's poor record on civil rights before he became president (at one point he was friendly with George Wallace). Carter was a product of his time and grew up in the segregated Deep South after all, but this does not excuse his reluctance to take a stand even in matters like school desegregation. Of course, the defining relationship that Carter has had is with his wife Rosalynn who he married when he was twenty-one and she was nineteen; they have been married for more than seventy-five years now. Rosalynn has been a commanding presence in his life, and he often sought advice from her along with his other advisors during the most crucial moments of his presidency.

Carter's main problem was that he was dour, practical and business-like and almost completely lacked the warmth, optimism and PR skills that are necessary for political leaders to win over not just minds but hearts. He was the strict father who wants to lecture his children about what's best for them. His grim fireside chats about consumption and self-indulgence, while sensible, did not go down well with the American people.

In addition, while he did a good job during his first two years, Carter was completely overtaken by global events during his second two, most notably the Iranian Revolution, Soviet aggression and the oil crisis that hiked up oil prices. The book does a good job showing that while Carter was not responsible for these events, he was as clueless in understanding the situation in Iran as anyone else.

There is an excellent account of the Iranian Hostage crisis in Alter's biography which includes many details that I did not know. It seems like a real tragedy since it was a comedy of errors in some sense, albeit one which the US had yoked itself with since installing the Shah of Iran in a coup in 1953 (there is an excellent account of how Mohammed Mosaddegh's democratic government was toppled in Stephen Kinzer's book "All the Shah's Men"). The hostage crisis was essentially triggered by the Shah being allowed into the US for medical treatment. He had fled from Iran after the Ayatollah had been reinstalled and flown in from exile in France.

The Shah's case was engineered by a lobby prominently led among others by Kissinger - the man's villainy continued unabated even after leaving the Nixon administration. He and his cabal greatly exaggerated the Shah's medical condition and forced Carter to admit him into the US on humanitarian grounds; he was in Mexico, and the Kissinger faction wrongly made the case that Mexican hospitals weren't equipped to diagnose and treat him. This was the last straw since it told the Iranians that the US was about to embark on another 1953-like adventure. This wasn't true, but at this point cooler heads weren't prevailing.

Also complicit in the disaster was Carter's hawkish national security advisor Zbigniew Brzezinski, an early neoconservative. A dissenter was secretary of state Cyrus Vance who strenuously advised against letting the Shah in, having a good idea of how perilous the situation in Iran was. As it turned out, Carter was blindsided and ignorant of the internal situation in Iran and ended up letting the Shah in (before hurriedly getting him out again).

The irony was that the die had been cast by Carter's predecessors, especially Eisenhower and Nixon, and Carter himself had very little interest in adventurism abroad, but the Shah was America's burden to bear, and Carter's actions were conflated with previous ones by the Iranians. Once the hostages were taken Carter's hands were tied for months, his approval ratings plummeted and the way was paved for Reagan (and Ben Affleck and his team in 'Argo').

I always feel that the fractured relations between the US and Iran constitute one of the great international tragedies of the 20th and early 21st centuries. Both countries have a rich heritage and have so much to offer each other. If the US had not completely thrown in their lot with Saudi Arabia and Israel and instead been friends with Iran, we would have had a powerful ally against Islamic fundamentalism in the Middle East. As it happened, the current Iranian regime is certainly nothing to praise and funds terrorist groups like Hezbollah. But it's important to note that it was largely US actions in the 1950s that led to the present state of affairs.

In retrospect, it appears obvious how someone like Reagan who was just fundamentally better at being a people pleaser and projected sunny optimism could defeat Carter. Fortunately Carter's career was just beginning at the end of his presidency, and in the next three decades he did very significant human rights work, including eradicating guinea worm from Africa and working on Habitat for Humanity, winning the Nobel Peace Prize in the process and becoming a far more deserving recipient than most others who got the prize. Now 97, he still teaches Sunday School in Plains, GA. What we need today is the pragmatism and intelligence of Jimmy Carter and the optimism of Ronald Reagan.

A fantastic book, well worth its almost 800 pages, and likely the definitive biography of a remarkable man for many years to come.