7 ms·

Word of advice to all those who are chomping at the bit to disrupt pharma with AI. A pharmaceutical company that heavily leverages computation is called a phar

Word of advice to all those who are chomping at the bit to disrupt pharma with AI. A pharmaceutical company that heavily leverages computation is called a pharmaceutical company. All modern pharmaceutical companies heavily leverage computational tools - including some powered by deep learning. Any company building a computational platform to accelerate drug discovery / development is not a pharmaceutical company. They are a software company who wants to sell software to pharmaceutical companies, which is a terrible business to be in. The product the author is selling already exists for free. All of the big knowledge bases use automated ML tooling for curation and extraction. And the people who run them and QC them are world class experts in their domains. I mean, just take a look at uniprot.[0] And the only types of pharma companies who would want a big knowledge graph would be the large ones with active programs across multiple therapeutic areas. Most of the small companies / start ups tend to be focused on getting one or two assets to market in a therapeutic domain. And the big companies possibly have their own ML teams doing literature extraction - because a team of 5-10 FTE ML / software engineers is like a rounding error in their R&D budget. The other thing is that the most valuable knowledge is the stuff that is not in the literature. That’s why we do experiments. [0] https://academic.oup.com/nar/article/51/D1/D523/6835362 https://academic.oup.com/nar/article/51/D1/D523/6835362

riku_iki 3y ago
> I mean, just take a look at uniprot.[0] this looks like one narrow niche knowledge base. I am not expert in this domain, but there are probably some other use cases not covered by existing offerings. > literature extraction - because a team of 5-10 FTE ML / software engineers I think this problem is so hard and open ended, that your 5-10 non-star avg salary ml ftes likely produce very mediocre and likely not usable results.
- radus 3y ago
  > this looks like one narrow niche knowledge base. I am not expert in this domain, but there are probably some other use cases not covered by existing offerings. UniProt is not niche. It contains curated information about all proteins across numerous domains.
  - riku_iki 3y ago
    yes, proteins is a niche in grand scheme of things
    - pama 3y ago
      The GP was talking about pharma. Proteins are not niche in pharma. Everything else may be niche in that domain, but proteins make up more than 95% of the targets of the pharmaceutical industry.
      - dekhn 3y ago
        and an increasing number of drugs- in the "old days" it was almost entirely small molecules, but they are starting to peter out (both because the low-hanging fruit has already been plucked, and also, small molecules are a usually a terrible way to modulate biological activity in specific ways.
      - riku_iki 3y ago
        > The GP was talking about pharma. post is not about pharma specifically, but about general bio-medical literature, including genes, diseases, symptoms, etc. > proteins make up more than 95% of the targets of the pharmaceutical industry. do you have references to support this? Some google search says it is 280B market out of 1.5T total pharma market: https://www.alliedmarketresearch.com/protein-therapeutics-market https://www.alliedmarketresearch.com/protein-therapeutics-ma... https://www.statista.com/topics/1764/global-pharmaceutical-industry/#topicOverview https://www.statista.com/topics/1764/global-pharmaceutical-i... Also, it is likely important steps how to research and produce protein drugs, but the end target is to cure diseases, so you need lots of additional data about diseases of all types, symptoms, pathways, trials, etc.
        dekhn 3y ago
        This isn't a "do you have references" situation. You're wasting folks time. Nearly all drugs target proteins, with a few that target DNA or RNA.
- pfisherman 3y ago
  Uniprot fills a pretty big niche - proteins. Your body is literally made out of proteins. They carry out most of the work and chemical reactions that amount to what we call life, and almost all drugs work by modulating some protein target in some way. UniprotIDs are the canonical identifiers that computational biologists use for proteins. But they are just one knowledge base. There are others. Each with their own focus area. And they are all associated with prominent bioNLP / biomedical AI research labs and employ human SME curators. > 5-10 avg salary non-star ML FTEs likely produce very mediocre and likely non usable results LOL!
  - riku_iki 3y ago
    > But they are just one knowledge base. There are others. So, is your claim that all topics/niches/verticals/steps in pharma development and productions are covered by all those databases with absolute quality and perfect UI/workflow? I follow some studies about trials meta-analysis, and my impression is that end result is mainly produced by manual work of some low paid postdocs, which makes them not trustworthy. > LOL! I actually claim non-trivial expertise in this area (facts extraction from untrivial niche documents), and my observations is that even SOTA (aka results from top researchers) are hardly useable in real life, because this area is very hard. Could you support your "LOL!" with any references I could check?..
    - MichaelZuo 3y ago
      The parent is right, saying a database about proteins 'looks like one narrow niche knowledge base.' in this context is just nonsense.
      - riku_iki 3y ago
        in which context, and why your bold statements should be trusted blindly?
        deleted 3y ago
        [deleted]
w10-1 3y ago
Ingenuity's Pathway Analysis product has been the standard tool big pharma has used for 15+ years. Founded by folks from Stanford, they created their own knowledge-graph database a decade before they were popular, and have been paying PhD's to curate that database with genetic findings from papers over the last decades. Obviously their analysis also includes the publicly-available databases, and they have their own private genomic databases and expertise from helping many of the first-available genomic services go live. Still, it's a tough business, and they were bought by Qiagen (to make an end-to-end solution, with moderate success, as the market is strangled by Illumina's control over NGS sequencing). And with exponential growth in information, their early relative advantage likely has waned. Also note Veeva software, which provides infrastructure for pharma, is a public benefit company, legally devoted to its clients. That's how much power the customer has. That said, there's something of a revolution in drug discovery as more is being done by small companies with ultra-focused expertise. If they get a viable candidate, a bigger company buys it to marshal through approvals, and then it can be sold again to a marketing company. Focusing on these pop-up drug-discovery companies that evolve out of someone's PhD could be a good niche; I would expect Pharma VC's to prefer funding companies using their favorite computing vendor, for reliability if not insight. Here computational discovery assistance would help a bit, but validating findings and science could be golden.
- pfisherman 3y ago
  I think the big case study everybody gives is Schrödinger who took 20 years to IPO. The problem is that the market is just not that big. Assume that every single pharma company buys your product - like Schrödinger - where does your revenue top out? You can beat that as a small pharma company with one or two assets that make it to market. So the question is: if the computational platform is so good then why not just be pharma company?
  - riwsky 3y ago
    “If you’re so smart, then why aren’t you rich?”
  - petra 3y ago
kromem 3y ago
I wouldn't be so quick to dismiss a niche for third party data analysis. A very common feature across verticals is that in house data analysis is siloed. While it's a difficult sell, getting multiple siloed data sources to agree to 3rd party analysis for shared gains can be extremely successful for everyone involved when pulled off. So looking specifically at pharma, while analysis of published papers is meh, an often discussed component of research is the bias against publishing negative results. So a hypothetical product like using ML across multiple firms' data of failed products and research in order to establish a model that could more quickly identify dud research avenues by leveraging industry data could only exist with the broadest dataset as a 3rd party product and would deliver gains that could be quite profitable for some of the largest companies out there. Also, I think anyone who has worked with larger corporations on in house tech knows that even if the individual efforts are quite large and sophisticated, the sheer amount of bureaucracy that goes into every single thing at a sizable firm can mean significant advantages for startups vs in-house efforts, particularly when related to fast moving fields. I'd agree that "moving into a niche without knowing it extremely well" can be fraught with issues and that attempting to get buy in from B2B firms for a startup is a nightmare, but I'd disagree with "large company does X in house so creating a startup to do X is a bad idea."
- pfisherman 3y ago
  I mean what you suggested already kind of exists in several forms.[0,1] Third party data analyses is a thing, but this is often bundled with conducting experiments by CROs. See my other comment about market size. The problem is that you can make A LOT more money selling drugs than you can selling software to pharma companies. So if your software is any good, then you should use it to make drugs and just be a pharma company. 0. https://www.opentargets.org/ https://www.opentargets.org/ 1. https://www.citeline.com/en/products-services/clinical/pharmaprojects https://www.citeline.com/en/products-services/clinical/pharm...
  - kromem 3y ago
    What I suggested was simply an illustrative example off the top of my head. The fact there's multiple similar companies that already independently exist furthers my point. As for your other point, I'd imagine there's a pretty big difference between the capabilities and infrastructure between bringing a drug to market and analyzing the data of people who bring drugs to market.
wodenokoto 3y ago
I did a stint at large pharma, and it was not free software that was used to extract structured data from publication.
dilawar 3y ago
Thanks for commenting. I've been thinking a bit about taking some computational insights we have developed over years to bioinformatics. During my Ph.D., my PI wanted to do some work on signalling pathway analysis. He was interested in knowing when and why sometimes body starts assuming that the sick state is the right state and start acting against drugs given to patients. This making treatment very hard. I don't know what is status of that but one insight I got from him that most (all?) Pharma companies do not trust acdemic data. I didn't ask reasons but I'd assume because of replication issue (crisis). Knowing how data is gathered and published in signalling domain, I'd not blame them for having low trust in academic data.
- pfisherman 3y ago
  Depends on what you want to use the data for. In general, you should take a trust but verify approach to new results, methods, data, etc.

riku_iki 3y ago

> I mean, just take a look at uniprot.[0] this looks like one narrow niche knowledge base. I am not expert in this domain, but there are probably some other use cases not covered by existing offerings. > literature extraction - because a team of 5-10 FTE ML / software engineers I think this problem is so hard and open ended, that your 5-10 non-star avg salary ml ftes likely produce very mediocre and likely not usable results.

radus 3y ago
> this looks like one narrow niche knowledge base. I am not expert in this domain, but there are probably some other use cases not covered by existing offerings. UniProt is not niche. It contains curated information about all proteins across numerous domains.
- riku_iki 3y ago
  yes, proteins is a niche in grand scheme of things
  - pama 3y ago
    The GP was talking about pharma. Proteins are not niche in pharma. Everything else may be niche in that domain, but proteins make up more than 95% of the targets of the pharmaceutical industry.
    - dekhn 3y ago
      and an increasing number of drugs- in the "old days" it was almost entirely small molecules, but they are starting to peter out (both because the low-hanging fruit has already been plucked, and also, small molecules are a usually a terrible way to modulate biological activity in specific ways.
    - riku_iki 3y ago
      > The GP was talking about pharma. post is not about pharma specifically, but about general bio-medical literature, including genes, diseases, symptoms, etc. > proteins make up more than 95% of the targets of the pharmaceutical industry. do you have references to support this? Some google search says it is 280B market out of 1.5T total pharma market: https://www.alliedmarketresearch.com/protein-therapeutics-market https://www.alliedmarketresearch.com/protein-therapeutics-ma... https://www.statista.com/topics/1764/global-pharmaceutical-industry/#topicOverview https://www.statista.com/topics/1764/global-pharmaceutical-i... Also, it is likely important steps how to research and produce protein drugs, but the end target is to cure diseases, so you need lots of additional data about diseases of all types, symptoms, pathways, trials, etc.
      - dekhn 3y ago
        This isn't a "do you have references" situation. You're wasting folks time. Nearly all drugs target proteins, with a few that target DNA or RNA.
pfisherman 3y ago
Uniprot fills a pretty big niche - proteins. Your body is literally made out of proteins. They carry out most of the work and chemical reactions that amount to what we call life, and almost all drugs work by modulating some protein target in some way. UniprotIDs are the canonical identifiers that computational biologists use for proteins. But they are just one knowledge base. There are others. Each with their own focus area. And they are all associated with prominent bioNLP / biomedical AI research labs and employ human SME curators. > 5-10 avg salary non-star ML FTEs likely produce very mediocre and likely non usable results LOL!
- riku_iki 3y ago
  > But they are just one knowledge base. There are others. So, is your claim that all topics/niches/verticals/steps in pharma development and productions are covered by all those databases with absolute quality and perfect UI/workflow? I follow some studies about trials meta-analysis, and my impression is that end result is mainly produced by manual work of some low paid postdocs, which makes them not trustworthy. > LOL! I actually claim non-trivial expertise in this area (facts extraction from untrivial niche documents), and my observations is that even SOTA (aka results from top researchers) are hardly useable in real life, because this area is very hard. Could you support your "LOL!" with any references I could check?..
  - MichaelZuo 3y ago
    The parent is right, saying a database about proteins 'looks like one narrow niche knowledge base.' in this context is just nonsense.
    - riku_iki 3y ago
      in which context, and why your bold statements should be trusted blindly?
      - deleted 3y ago
        [deleted]

w10-1 3y ago

Ingenuity's Pathway Analysis product has been the standard tool big pharma has used for 15+ years. Founded by folks from Stanford, they created their own knowledge-graph database a decade before they were popular, and have been paying PhD's to curate that database with genetic findings from papers over the last decades. Obviously their analysis also includes the publicly-available databases, and they have their own private genomic databases and expertise from helping many of the first-available genomic services go live. Still, it's a tough business, and they were bought by Qiagen (to make an end-to-end solution, with moderate success, as the market is strangled by Illumina's control over NGS sequencing). And with exponential growth in information, their early relative advantage likely has waned. Also note Veeva software, which provides infrastructure for pharma, is a public benefit company, legally devoted to its clients. That's how much power the customer has. That said, there's something of a revolution in drug discovery as more is being done by small companies with ultra-focused expertise. If they get a viable candidate, a bigger company buys it to marshal through approvals, and then it can be sold again to a marketing company. Focusing on these pop-up drug-discovery companies that evolve out of someone's PhD could be a good niche; I would expect Pharma VC's to prefer funding companies using their favorite computing vendor, for reliability if not insight. Here computational discovery assistance would help a bit, but validating findings and science could be golden.

pfisherman 3y ago
I think the big case study everybody gives is Schrödinger who took 20 years to IPO. The problem is that the market is just not that big. Assume that every single pharma company buys your product - like Schrödinger - where does your revenue top out? You can beat that as a small pharma company with one or two assets that make it to market. So the question is: if the computational platform is so good then why not just be pharma company?
- riwsky 3y ago
  “If you’re so smart, then why aren’t you rich?”
- petra 3y ago

kromem 3y ago

I wouldn't be so quick to dismiss a niche for third party data analysis. A very common feature across verticals is that in house data analysis is siloed. While it's a difficult sell, getting multiple siloed data sources to agree to 3rd party analysis for shared gains can be extremely successful for everyone involved when pulled off. So looking specifically at pharma, while analysis of published papers is meh, an often discussed component of research is the bias against publishing negative results. So a hypothetical product like using ML across multiple firms' data of failed products and research in order to establish a model that could more quickly identify dud research avenues by leveraging industry data could only exist with the broadest dataset as a 3rd party product and would deliver gains that could be quite profitable for some of the largest companies out there. Also, I think anyone who has worked with larger corporations on in house tech knows that even if the individual efforts are quite large and sophisticated, the sheer amount of bureaucracy that goes into every single thing at a sizable firm can mean significant advantages for startups vs in-house efforts, particularly when related to fast moving fields. I'd agree that "moving into a niche without knowing it extremely well" can be fraught with issues and that attempting to get buy in from B2B firms for a startup is a nightmare, but I'd disagree with "large company does X in house so creating a startup to do X is a bad idea."

pfisherman 3y ago
I mean what you suggested already kind of exists in several forms.[0,1] Third party data analyses is a thing, but this is often bundled with conducting experiments by CROs. See my other comment about market size. The problem is that you can make A LOT more money selling drugs than you can selling software to pharma companies. So if your software is any good, then you should use it to make drugs and just be a pharma company. 0. https://www.opentargets.org/ https://www.opentargets.org/ 1. https://www.citeline.com/en/products-services/clinical/pharmaprojects https://www.citeline.com/en/products-services/clinical/pharm...
- kromem 3y ago
  What I suggested was simply an illustrative example off the top of my head. The fact there's multiple similar companies that already independently exist furthers my point. As for your other point, I'd imagine there's a pretty big difference between the capabilities and infrastructure between bringing a drug to market and analyzing the data of people who bring drugs to market.

wodenokoto 3y ago

I did a stint at large pharma, and it was not free software that was used to extract structured data from publication.

dilawar 3y ago

Thanks for commenting. I've been thinking a bit about taking some computational insights we have developed over years to bioinformatics. During my Ph.D., my PI wanted to do some work on signalling pathway analysis. He was interested in knowing when and why sometimes body starts assuming that the sick state is the right state and start acting against drugs given to patients. This making treatment very hard. I don't know what is status of that but one insight I got from him that most (all?) Pharma companies do not trust acdemic data. I didn't ask reasons but I'd assume because of replication issue (crisis). Knowing how data is gathered and published in signalling domain, I'd not blame them for having low trust in academic data.

pfisherman 3y ago
Depends on what you want to use the data for. In general, you should take a trust but verify approach to new results, methods, data, etc.