
Ask HN: What would you like to learn about bio startups? - aaavl2821
I made a comment a few weeks ago on the dearth of resources for learning about bio startups that a few people seemed to agree with.  I wanted to follow up on that topic and see what information would be most helpful for those who want to learn about the sector.<p>As a side project, I&#x27;ve been offering free career development programs &#x2F; resources to help PhD students and postdocs learn about bio startups and meet people from the startup &#x2F; VC community.<p>I would like to create resources for non-scientists as well (I myself am a non-scientist but have worked in the space for eight years and love it), and some of the students have expressed interest in helping with this effort on a part-time basis while they look for jobs or start their companies.<p>So what would you be most interested in learning about bio startups?<p>For context, all of the students &#x2F; postdocs have PhDs (or are getting PhDs) in biology, chemistry, bioinformatics or related fields from top-tier universities and have demonstrated interest &#x2F; experience in startups.  There are ~100 students in the network, all in the Bay Area.  I don&#x27;t have a science background but have worked with bio startups my whole career as a venture investor, venture-backed founder, and employee &#x2F; consultant at startups and big tech companies.<p>The format of the services will obviously depend on what resources would be most helpful.  I&#x27;ve heard requests for consulting engagements, recruiting of technical talent, part-time support for founders who don&#x27;t have life sci backgrounds, deep technical dives &#x2F; diligence, help with deal flow, exploring startup opportunities around a theme, educational content on biology and chemistry, industry overviews, etc.
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matt4077
I studied bioinformatics and enjoyed it tremendously. I unfortunately (for my
science career) got sidetracked with a startup and never finished my degree,
but I think I have a good enough understanding of the field, and I'd love to
try my hand at contributing something useful.

What I have almost no idea of is what sort of product might actually have a
chance in a commercial context. I'm not even talking about VC-startup
territory, but something that's both interesting, and could potentially
achieve "Ramen profitability".

From what I remember, startups were mostly in Pharmaceutics, requiring vast
amounts of money and expertise. And bioinformatics within those companies
seemed to mostly involve groundwork in the data cave. PyMOL comes to mind as
an exception.

Any pointers would be appreciated!

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aaavl2821
Your question is an interesting and common one, although most people ask about
VC-fundable ideas vs boostrapped ones. We might do a project soon exploring
opportunities in this area, but my feeling now is that there is a lot of
interest in using ML / informatics to find new targets, find new ways to drug
validated targets, and speed up lead optimization. the biggest bottleneck is
creating scalable pipelines for getting high quality, annotated data. a good
way to create these targeted pipelines is by starting with a targeted
therapeutic / biological hypothesis and building the informatics platform from
there

It is true that most startups are in biopharma / drug development, and that
field has performed incredibly well the last five years. In the last 18 months
or so, "bio-IT" has emerged as an area of significant interest. This is a
pretty amorphous term (one VC described it as "a biotech startup whose CEO
wears a hoodie, not a suit") that ranges in meaning from turning drug
development into an engineering discipline [1] to using AI to identify novel
targets or drug candidates (see recursion pharma as a good example of this
type of company), to mining genomic and transcriptomic data in search of novel
drug targets

a few things that might help frame the opportunity:

an MIT-affiliated venture creation firm released a good paper last year on the
applications of AI to drug dev [2]

there was a post here a few days back on using AI to predict protein folding.
tools like that would be quite useful

tools to predict tox / PK/PD of compounds is an area of interest

platforms to analyze various -omics data to identify disease associated
pathways and targets are interesting

"virtual screening" tools have been an early area of research, either based on
biochemistry / biophysics and structural data, or AI / ML approaches

however the holy grail is using informatics to find new validated targets. the
failure of big late stage studies due to targets that worked in mice but not
in humans is by far the biggest driver of the cost of drug r&d

my favorite example of using bioinformatics to find validated targets is the
Regeneron Genetics Center. regeneron is a "big biotech" company with one of
the best reputations for early stage r&d in the field. their approach is
"lower tech" but quite clever and effective

RGC started with a clever therapeutic hypothesis that 1) gets the most "juice"
out of each piece of data and 2) enables the targeted optimization of the data
collection and analytics platform. the thesis is basically: drugs can really
only target one molecule at a time, so you want to find single genetic
mutations that are correlated with very dramatic clinical phenotypes (rather
than a "signature" of a bunch of small effect sizes); rare phenotypes can
inform generalizable clinical strategies (if one rare mutation results in,
say, super low LDL in a tiny subset of patients, then you can probably target
that mutation to reduce LDL in any patient); you need scale to find these rare
and actionable mutations (so focus on lots of cheap WES vs higher resolution
but more expensive WGS)

they dont use advanced AI or ML, but get cheap, high quality data through
partnerships with health systems, have a high throughput, highly automated
genomics core, and an informatics infrastructure that makes it easy to do the
kinds of queries they need. many firms start with data and then search for
therapeutic applications, but this has proved inefficient and ineffective, and
RGC's approach stands out as very clever and scalable.

[1] [https://a16z.com/2017/12/14/second-bio-
fund/](https://a16z.com/2017/12/14/second-bio-fund/)

[2]
[https://static1.squarespace.com/static/57b630ae9de4bb6c5c0b6...](https://static1.squarespace.com/static/57b630ae9de4bb6c5c0b6907/t/592cdd922e69cfe0047c0ab2/1496112532453/NestBio-
ArtificialIntelligenceAndComputationalBiochemistry.pdf)

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matt4077
Thank you for taking the time to write such an extensive answer. Do you have a
link to your other writing, or the project you mentioned in submission?

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aaavl2821
The website for the program is newbio.tech. I've written a few articles on the
site and might write up something on bio-it in the next month or so

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indescions_2018
Where the money goes. Seriously. We routinely see Angel and Series A rounds
raised by Professors with little business experience that are 5-10X the amount
an early stage AI company would get.

I'd really be interested in seeing the blueprint for how to bootstrap a
successful synthbio startup starting with a single, garage-sized DNA foundry.

~~~
aaavl2821
Haven't seen data for 2017 AI funding, but biopharma startups raised $12B in
2017 (AI startups raised $5B in 2016).

The large rounds are due to 1) amazing exit environment attracting tons of
capital and 2) no growth in number of funded startups in the last decade. More
money chasing fewer deals = larger $ per deal

From what I see professors don't typically don't join companies, but rather
stay in academia and advise re the science. The management team is generally
comprised of a team of serial entrepreneurs or 20-30 year big pharma veterans.
Most startups are seeded in house by a cadre of biotech VCs

Will write a post about the blueprint for these startups, but in broad strokes
it's 1) $1-5M seed round to make sure the academic work is reproducible,
generate IP and derisk core hypotheses with a few "killer experiments". If
that looks good, there will be a $20-50M (or even $100M+) series a that is
typically designed to get through human proof of concept in phase 1. This is
when the exit window opens, and the exit window these days is big. Should be
noted that most of these rounds are tranched

There are several Bruce booth blog posts that walk through this in more
detail. Bruce is a partner at atlas venture, a leading early stage biotech
fund, and he's one of the few biotech VCs who blogs. Don't have specific links
offhand but can find a few if interested

It's tough to bootstrap in biopharma because you don't generate revenue
basically ever. You could theoretically sell assays or reagents or something
to get revenue to support the real r&d but it's often preferable to get VC
money. The best exits require $10-20M minimum

