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Chemistry is a bit late to the game, compared (for instance) to the advertising and surveillance industries. This has given them the chance to realize that it's

Chemistry is a bit late to the game, compared (for instance) to the advertising and surveillance industries. This has given them the chance to realize that it's the data, and not the algorithms, that are of long term value. As a consequence, they're not giving it away for free. It's also possible that chemists have had a head start in terms of guarding their data security. And processes are extremely costly to scale up, so they want a moat in order to recoup their investment. Industries like pharma have been security conscious for a long time.

throwawaymaths 3y ago
> Chemistry is a bit late to the game This is not true. In the 80s? 90s? E.J. Corey spearheaded an attempt to create a database of all chemical reactions and tried to get programmers to design expert systems to create intelligent chemistry planners. If anything, they were too early to the game.
- jleyank 3y ago
  I suspect chemistry was playing about with automatic (retro)synthesis way earlier than you think: https://pubs.acs.org/doi/pdf/10.1021/bk-1977-0061.ch001 https://pubs.acs.org/doi/pdf/10.1021/bk-1977-0061.ch001. I think it was work started in the 60's, and big names worked on it.
  - throwawaymaths 3y ago
    Heh that was the exact paper I was talking about, guess I got the decade wrong
- EGreg 3y ago
  Wasn’t the human genome mapped recently? That’s some of the most relevant (to us) chemistry In fact biochemistry is a data science AND also programming. It’s not von neumann architecture, but it’s far more complex!
  - throwawaymaths 3y ago
    Eh, the dimensionality and coupled-ness of the genome makes it a very different big data problem than basically every other big data problem.
a_bonobo 3y ago
I concur - the most interesting research in this space comes from inside large companies like Bayer who likely have decades of datasets. Random example: https://chemrxiv.org/engage/chemrxiv/article-details/621e3c33011b58c289b953ed https://chemrxiv.org/engage/chemrxiv/article-details/621e3c3... All the authors work at Bayer. I think some of the authors recently got poached by Pfizer. Imagine how much of their research doesn't make it outside the company!
Projectiboga 3y ago
Scale up isn't an issue per se but the fact the Pharma Corp has to set the exact process somewhat early and have it in the DMF, drug master file. With the ML angle proposed here are a few not comprehensive challenges. One measurement of temperature in actual reactions wasn't too accurate until last few decades. Many reactions were not attempted under pressure. There is a whole cat, mouse and booby trap element with scientific papers and even more so w patents a patent only has to be obvious 'to some one skilled in the art of, in this case, chemistry". Then huge amounts of data sit in silos or in dusty lab notebooks. This is before the notions of what has been patented and what is now under patent until when. Trade secrets can still be an issue, but our switch to first to file from our previous first to invent patent system should reduce 'submarine patent' risk. So these IP issues and siloed data present challenges to using ML in this space. It can help target research by better selecting lead cannidates, and find novel uses to existing molecules.
dalke 3y ago
A bit late? There's big money in chemistry, and that's made chemists secretive about the data since its start in the 1700s. Longer if you include its alchemical roots. https://www.cairn.info/revue-entreprises-et-histoire-2016-1-page-32.htm https://www.cairn.info/revue-entreprises-et-histoire-2016-1-... """This article analyzes why late nineteenth- and early twentieth-century German and German-American high-technology firms were presented as being overly secretive in chemical circles in the United States. It suggests that German chemical companies did not just develop innovative uses of the patent system, but also pioneered intellectual property strategies of which both patenting and secrecy were important components. Focusing on two German-American firms, Mallinckrodt Chemical Works and Roessler & Hasslacher, the study relates statements on restrictive knowledge management and intellectual property practices of German companies to transatlantic institutional differences. It points to a dissonance between the persistent association of German high-technology enterprises with secrecy and the actual directions in which the German and American systems of corporate intellectual property were moving in the early twentieth century.""" https://www.sciencedirect.com/science/article/abs/pii/S0048733397000140 https://www.sciencedirect.com/science/article/abs/pii/S00487... """In the 19th century, market leaders in the chemical industry combined patents and secrecy to deter entry. Within cartels, patents were used to stabilize cartels and organize technology licensing.""" "The History of Artificial Ultramarine (1787–1844): Science, Industry and Secrecy", https://www.tandfonline.com/doi/abs/10.1179/amb.2004.51.3.219 https://www.tandfonline.com/doi/abs/10.1179/amb.2004.51.3.21...
cookieperson 3y ago
A lot of data science techniques came from chemists. I don't think this comment really holds water.

throwawaymaths 3y ago

> Chemistry is a bit late to the game This is not true. In the 80s? 90s? E.J. Corey spearheaded an attempt to create a database of all chemical reactions and tried to get programmers to design expert systems to create intelligent chemistry planners. If anything, they were too early to the game.

jleyank 3y ago
I suspect chemistry was playing about with automatic (retro)synthesis way earlier than you think: https://pubs.acs.org/doi/pdf/10.1021/bk-1977-0061.ch001 https://pubs.acs.org/doi/pdf/10.1021/bk-1977-0061.ch001. I think it was work started in the 60's, and big names worked on it.
- throwawaymaths 3y ago
  Heh that was the exact paper I was talking about, guess I got the decade wrong
EGreg 3y ago
Wasn’t the human genome mapped recently? That’s some of the most relevant (to us) chemistry In fact biochemistry is a data science AND also programming. It’s not von neumann architecture, but it’s far more complex!
- throwawaymaths 3y ago
  Eh, the dimensionality and coupled-ness of the genome makes it a very different big data problem than basically every other big data problem.

a_bonobo 3y ago

I concur - the most interesting research in this space comes from inside large companies like Bayer who likely have decades of datasets. Random example: https://chemrxiv.org/engage/chemrxiv/article-details/621e3c33011b58c289b953ed https://chemrxiv.org/engage/chemrxiv/article-details/621e3c3... All the authors work at Bayer. I think some of the authors recently got poached by Pfizer. Imagine how much of their research doesn't make it outside the company!

Projectiboga 3y ago

Scale up isn't an issue per se but the fact the Pharma Corp has to set the exact process somewhat early and have it in the DMF, drug master file. With the ML angle proposed here are a few not comprehensive challenges. One measurement of temperature in actual reactions wasn't too accurate until last few decades. Many reactions were not attempted under pressure. There is a whole cat, mouse and booby trap element with scientific papers and even more so w patents a patent only has to be obvious 'to some one skilled in the art of, in this case, chemistry". Then huge amounts of data sit in silos or in dusty lab notebooks. This is before the notions of what has been patented and what is now under patent until when. Trade secrets can still be an issue, but our switch to first to file from our previous first to invent patent system should reduce 'submarine patent' risk. So these IP issues and siloed data present challenges to using ML in this space. It can help target research by better selecting lead cannidates, and find novel uses to existing molecules.

dalke 3y ago

A bit late? There's big money in chemistry, and that's made chemists secretive about the data since its start in the 1700s. Longer if you include its alchemical roots. https://www.cairn.info/revue-entreprises-et-histoire-2016-1-page-32.htm https://www.cairn.info/revue-entreprises-et-histoire-2016-1-... """This article analyzes why late nineteenth- and early twentieth-century German and German-American high-technology firms were presented as being overly secretive in chemical circles in the United States. It suggests that German chemical companies did not just develop innovative uses of the patent system, but also pioneered intellectual property strategies of which both patenting and secrecy were important components. Focusing on two German-American firms, Mallinckrodt Chemical Works and Roessler & Hasslacher, the study relates statements on restrictive knowledge management and intellectual property practices of German companies to transatlantic institutional differences. It points to a dissonance between the persistent association of German high-technology enterprises with secrecy and the actual directions in which the German and American systems of corporate intellectual property were moving in the early twentieth century.""" https://www.sciencedirect.com/science/article/abs/pii/S0048733397000140 https://www.sciencedirect.com/science/article/abs/pii/S00487... """In the 19th century, market leaders in the chemical industry combined patents and secrecy to deter entry. Within cartels, patents were used to stabilize cartels and organize technology licensing.""" "The History of Artificial Ultramarine (1787–1844): Science, Industry and Secrecy", https://www.tandfonline.com/doi/abs/10.1179/amb.2004.51.3.219 https://www.tandfonline.com/doi/abs/10.1179/amb.2004.51.3.21...

cookieperson 3y ago

A lot of data science techniques came from chemists. I don't think this comment really holds water.