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Another way to think about diamandoids is to consider what kind of organic chemistry you need to put them together the "traditional" way.  That'll give you some insight into the processes you're going to be competing with as you try to assemble these structures, no matter which technique you use.  The syntheses tend to go by rearrangements of other scaffolds that are easier to assemble but somewhat less thermodynamically stable ( for example).  However, this technique gets arduous beyond 4 or 5 adamantane units:

Agreed that the Nanoputians aren't impressive.  Lots of drugs are comparably complex, and they're actually designed to elicit a biological effect.  

The B12 synthesis is sweet, but I'll put in a vote for the Woodward synthesis of strychnine (done using 1954 technology, no less!):

I geek out about unusual plants.  I find Welwitschia interesting because it's kind of an outlier.  It's a gymnosperm, meaning it doesn't produce flowers, is wind-pollinated, and forms seeds differently than an angiosperm, but it doesn't look like other gymnosperms.  Central examples of gymnosperms are conifers, with less-central examples being things like cycads and ginkgo trees, but Welwitschia looks nothing like those, or really any other plants I can think of.  It's got a central meristem (growth zone) and two leaves that grow from that meristem at their base.  The plant basically grows by elongating the two leaves, and they can get 4+ meters long.  These things grow in the Namib desert and the wind blows the leaves all over the place and splits them at the veins (which run parallel down the leaves), so the mature plant looks like a pile of dirty green ribbon in the middle of the desert.  Growing 4-meter leaves is also something of an unusual survival strategy for a desert plant.  Like a lot of desert life, they grow slowly and can live a long time (possibly millennia!).  The fact that it's unrelated to other desert plants like cacti or Euphorbias mean we can use it to get another data point about desert adaptation at a genome level as well.


Thanks for this!  Austin Vernon took a look at why the nuclear industry isn't growing and came to some broadly similar conclusions here.  I think a lot of environmental groups have gotten themselves into a situation where "nuclear power is evil" is kind of taken as a given, while the techno-optimist crowd is overly sanguine about cost problems being driven by regulations and the chances of those regulations changing.  in other words, there's a lot of people with different assumptions and values talking past one another in this debate.  Continuing to pump money into next-gen nuclear research seems like a reasonable use of money to me because the potential payoff is big enough to outweigh the low-ish probability of success.  You could say the same about lots of branches of renewables research (e.g. perovskite solar cells) though.  

The lack of context for comparable search spaces is a fair criticism.  The implicit assumption (which I now realize was inappropriate not to spell out for this audience) was that your search would, at some point, involve actually making the molecules in question in order to subject them to some form of experimental characterization.  The comparison of the number of possible small molecules to the amount of available terrestrial carbon was intended to make the point that achieving sizable coverage of the search space experimentally is close to a non-starter.  In practice, of course, there are all kinds of ways to bias your search in productive directions.  

Some search-space context:

Number of possible chess games: Shannon conservatively estimated 10^120 possible games, 10^43 possible board positions.

Number of possible Go games: Wikipedia gives 10^172

Number of ways to order a standard 52-card deck: 8 x 10^67

As for why we don't see complex silicon-containing compounds in biology, here's an attempt at an answer:  We do see silicates in structural roles, for example in phytoliths.  However, low Si-Si bond strength relative to C-C, combined with very strong Si-O bonds mean that you tend to get Si-O-Si linkages (like in silicone polymers) rather than Si-Si bonds, and in the absence of Si-C bonds to prevent further oxidation, you form silicates pretty quickly.

I should have written 'common proteinogenic' in place of 'naturally-occurring'.  Thank you for the correction.

Solving protein folding doesn't only give you the ability to know how existing proteins fold. It also gives you the ability to design new proteins. 

I don't agree with this claim.  AlphaFold gives you the ability to calculate how a given amino acid sequence is likely to fold.  That is very different from being able to predict an amino acid sequence that performs a specific function or even has a given shape.  Small modifications of known shapes or functionalities would be tractable using AlphaFold's technology, but there are other ways to get that, for example directed evolution.  Search in the space of amino acid sequences is possible in principle but even with several orders of magnitude increase in compute the size of the search space still seems intractable to me.

If the big pharma companies would be functional, it would be appropriate for each of them to spend a billion per year on AI.

Isn't this significantly more than DeepMind spends?  I realize increased competition for ML talent would drive up salaries but I just can't see that kind of budget allocation happening for something that pharma companies don't consider to be core to their business.

Thanks again for engaging.  It's been fun to see how someone in the Silicon Valley mindset looks at the biopharma landscape.  

Thanks for engaging!  I think there's a real debate to be had about how public research money is spent.  I put a higher expected value on continuing to fund basic cancer research than I think you do.  I also am more bullish on doing working at the object level (going after specific targets) relative to the meta level (technology platforms).  Maybe this is myopia on my part, working as I do in the pharmaceutical industry, but I have also spent a fair amount of time thinking about the problem.

DeepMind beating Big Pharma at protein folding prediction suggests relatively little investment in the basic technology. 

I actually think DeepMind is plausibly the only entity in the world who could have made AlphaFold when they did.  The sheer amount of compute involved puts it out of the reach of nearly everyone else, plus pharma companies would have found it hard to hire away the caliber of ML talent DeepMind attracts.  There's a case to be made that this is a nearly-ideal outcome for the pharma industry: the problem was cracked, publicly, by a company with little to no interest in making medicines.  My prediction is that DeepMind either licenses the technology to pharma companies or contracts with them on specific targets (if the compute requirement is prohibitive for licensing).  That seems to satisfy the incentives of DeepMind (this should be a significant money-maker, plus good publicity if and when their structures help lead to new drugs) and pharma (get structures for important targets that we can't get other ways).  

I have a lot to say about this but I will keep it short.  First, I think you're underselling the insight that cancer isn't a single disease (the Atlantic headline was shitty; of course cancer is a disease).  This wasn't obvious a priori.  The fact that every case of cancer is a unique and horrible snowflake means that we can't expect "a cure for cancer" any more than we can expect "a cure for car trouble".  You're right, however, that some things are more likely to go wrong than others, and routine sequencing of tumors from each individual patient can help identify which treatments are most likely to help.

Second, I think there's a link between the decrease in death rate from heart disease and the minimal death rate decline from cancer even with all the increased testing and new treatments.  As they say, "something's gonna kill ya," and in my opinion dying from cancer at 65 instead of from a heart attack at 55 is still a win.  As a comparator in a disease area where no treatments have really worked to date, see the death rates from Alzheimer's disease.

Third, I'm as bullish on cancer immunotherapy as the next guy, but it turns out that many tumors produce an immunosuppressive environment, where T cells and NK cells just don't do their thing very well.  You can immunize against mutated protein fragments presented by the MHC all you want, but in an immunosuppressive microenvironment I still don't think you'll see those sweet sweet CRs.  

Finally, even with all the regulatory barriers and misaligned incentives, pharma companies are still working on the best cancer therapy targets we know about.  We (I work in pharma, so it's "we") certainly haven't hung up the "Mission Accomplished" banner and moved on.  While I expect continued insights about basic cancer biology to come from academic labs that receive public funding, future therapies will continue to arrive primarily from the private sector.  The potential pecuniary reward for even incremental increases in cancer survival rate is high enough to keep key players interested.

This is what I meant by "it's a trivial exercise in orbital mechanics, so maybe all of you do this instinctively".  I got there empirically. :)

What an "aspiring chemist" should do depends a lot on age and where they are in the educational process.  For children below high-school age, I think there are lots of great experiments you can do to illustrate principles of chemistry.  Lack of originality isn't a bug there, it's a feature.  In high school, if you think you like science, take chemistry!  There should be a lab component in most schools, so you can at least get a flavor for what working with chemicals is like.  Access to equipment like this is an underrated component of the educational system.  For college students, all the entry-level courses (general, organic, inorganic) are likely to have lab components.  There are a few programs that separate the lab courses from the lectures, but they're the exception.  The experiments won't be cutting-edge, but rather are designed to give students an understanding of chemical principles and what it's like to work with chemicals in a research setting.

Where things get really interesting from a lab perspective is if you can convince a professor to let you into their research lab as an undergraduate assistant.  It's helpful if you're at a research university rather than a small liberal arts college because the lab facilities will be more conducive to cutting-edge research, and there will be grad students and postdocs who relish the opportunity to teach a curious undergrad how to do chemistry.  You likely won't be designing your own project, but will have the opportunity to use modern equipment to do novel research under supervision.  My undergraduate research experience was formative and a huge reason why I do what I do today.  I was also lucky enough to get to pay it forward and mentor undergraduates when I was a grad student and a postdoc.  

Graduate school in chemistry has a (somewhat deserved) reputation as a potentially miserable time.  You get lots of great experience and training, but the hours are brutal and the rewards can be sparse.  Synthesis in particular is a field where no matter how brilliant you are, lots of time in the lab is still essential for success.  At this point you will work with your advisor to design and execute research projects, and success depends on your insight and work ethic.  You can mitigate a lot of the problems with grad school by being careful about which lab you choose and being willing to set boundaries on your work time.  It's also worth noting that I know a lot of really good chemists who started working in industry immediately after their bachelor's, or after a master's degree.

For the adult who has completed their education and wants to start doing chemistry at a level other than "fun home experiments", I don't have great suggestions.  Maybe this is just a lack of imagination on my part as someone inside "the system" but the hurdles I talked about above are pretty daunting.  

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