Idea: SDGR

Hello Saul and all board members,

I am a 20+ year investor but a new reader of the board and this is my first post, appologies for the long post. I will try to keep it to your high standards.

First, I am greatly appreciative of the work and sharing involved here and enjoy and benefit from the Knowledgebase and numerous of the other posts. I have not yet gone back at all beyond the past 2 years of posts. about 2/3rds of your positions made it into my growth portfolio, in advance of coming across the board, but sadly in much smaller allocations than you have utilized. I tend to run a much more diversified portfolio with 3% max investment per position and 10%-15% max position size at any time (due to growth). I am considering modifying my portfolio construction rules, and have cut about 20% of my names in the past 2 months to get toward a more concentrated “best-ideas” portfolio (my words for one element of your methodology which seems to help you significantly with success. I don’t know that I will get there in totality as I like some of the rules-based algorithmic processes that I use, and those get harder to utilize effectively as portofolio size shrinks.

I also currently run my growth stock portfolio almost entirely in taxable accounts and do take risks for tax reasons that you may not such as 1) 20% losers in under 1 year are almost always sold as short term gains, even if they remain high conviction and 2) significant winners with 10+ months aging are generally held to 1 year.

Second, after studying the guidance for new companies, there is one SAAS company that appears a shoe in for the strategy and I would greatly benefit from any community engagement in analyzing it and/or pointing out flaws in my thinking.

The company is Schrodinger Inc, Ticker: SDGR
It is a recent IPO (potentially a knock against it)
To use their description: “Schrodinger, Inc. provides computational software solutions for drug discovery to the biopharmaceutical industry. The Company operates through two segments: Software and Drug Discovery. The Software segment is focused on licensing the Company’s software for molecular discovery. The Drug Discovery segment is focused on building a portfolio of preclinical and clinical drug programs, internally and through collaborations.”

Founders who collectively own 50% of the company are Bill Gates and D.E. Shaw (the individual, not the hedge fund).

Market cap is $5.9 Billion.
Adressable market is some subset of the $180 billion spent on drug research annually. It appears that this adressable market size is expanding, but I have not yet done work on that.

The company benefits from both work-at-home and Covid-19, and could be a significant beneficiary of goal-oriented increases in healthcare R&D (likely).

They have contracts with 100% of the top 20 Pharma companies (mostly still small with significant potential expansion per customer).

In 2018 96% customer retention, I can’t find more recent data on this or what the increase in spend per customer is. Any help here could be extremely helpful.

TTM Sales growth is 37.5% up from revenue growth 2019 over 2018 of 29%(I know these are on the low side for Group positions), but appears set to accelerate furter with the deployment of IPO proceeds into R&D and Sales as new top-tier employees continue to be added.

The company currently has losses, managable, but still expanding with the scaling of the business. I take this as a slight negative, but appropriate to the early stage expansion of the company.

If anyone picks this up and deems it worth a thoughtful response, I will write up another recent buy that is non-SAAS but otherwise seems to fit the Group’s rules/guidelines.

Thanks for your consideration of looking into this to help me understand the risk and opportunity. I currently have a position of under 3% on this one, have been consistently adding and at odds with the Group’s thinking, did enter and exit a call position on this earlier in the year with significant benefit but perhaps with undue risk.



With a name like Schrodinger are they planning to use quantum computers for Drug discovery or just conventional quantum chemistry?

Quantum computers are still a few years away at being useful for this application, but they will have a big impact. There is value today is working out what algorithms to use on a future quantum computer.

Interesting company, thanks Archemedes97. It seems like what they do is cutting edge and only going to get better. A lot of smart people involved as well. I am curious about their billing, prices, contracts, and how they make money. But this blog from their EVP does explain a LOT about what they are capable of and trying to do.

Robert Abel, Ph.D., Executive Vice President, Science at Schrödinger
June 22, 2020
Imagine that the entire universe of “chemical space”—all the molecules that could ever possibly be created—is the size of an ocean. How much have we explored so far? Much less than a single drop of water. Stop for a minute and consider that: All the medicines and materials we have ever developed—from blood-pressure pills to cell-phone displays—come from that teeny fraction of a single drop. Imagine what we could accomplish if we could look beyond. The possibilities are truly boundless.

As the biopharma industry races to develop treatments and vaccines for the global pandemic, it’s imperative that we both broaden our gaze and quicken our pace. We need to explore more of the ocean, and we need to do it more quickly.

A new collaboration between Schrödinger and Google Cloud is designed to advance both goals—and thereby accelerate the hunt for effective medicines to treat COVID-19.

This collaboration is one piece of an unprecedented philanthropic initiative that has brought together leading biopharma companies from around the world—including Novartis, Takeda, WuXi, and Gilead Sciences—to develop antiviral therapeutics for COVID-19.

The first step in any drug discovery process is assessing molecules that could, potentially, prove effective against a given target. To make that evaluation, you have to answer a number of key questions: How tightly does the molecule bind to the target? Does it bind only to the target, or does it interfere with other important pathways in the cell? Is it soluble? How quickly is it metabolized? And so forth. In the past, the only way to get these answers was to synthesize the molecule in the lab and physically test it. As you might imagine, this was an extremely time-consuming and expensive process.

Schrödinger’s physics-based computational platform has revolutionized this crucial step in drug discovery. Over nearly 30 painstaking years of research, we have translated the laws of quantum mechanics and molecular dynamics into algorithms that enable swift evaluation of several crucial molecular properties “in silico” (on the computer) rather than “in vitro” (in a test tube or culture dish).

An ongoing strategic collaboration with Google Cloud gives our drug hunting team at Schrödinger access to immensely powerful computing capacity—equivalent to the world’s fastest supercomputers—to run these physics-based calculations in our commercial work undertaking drug discovery for biopharma partners and for our own internal pipeline.

Now, Google Cloud is making those same resources available to the global COVID-19 R&D alliance.

Under the terms of the grant, Google Cloud is providing 16 million hours of GPU time to enable Schrödinger to more comprehensively support the drug discovery efforts of this multi-company, philanthropic initiative to identify novel small-molecule therapeutics to address COVID-19 and possible future coronavirus outbreaks. If used consecutively, that would equate to 1,826 years of around-the-clock computing. In reality, of course, we leverage high-speed parallel computing, so we can move much more quickly than that statistic would imply.

In fact, with these computing resources at our disposal, we at Schrödinger are able to triage and evaluate literally billions of molecules each week for COVID-19 therapeutics, just as we do for other drug discovery projects. That means we’re exploring more of that ocean, more quickly.

We don’t want to leave you with the impression that we will have new drugs for COVID-19 patients overnight. Physics-based free-energy calculations performed on high-speed computers can accelerate drug discovery, but they can’t work miracles. Once we’ve identified the molecules that we believe would have the best in vitro properties, our partners in the alliance will still have to synthesize and test them in the lab. They’ll likely have to go through several rounds of optimization and testing before they can move into animal and clinical studies.

Still, the type of computational design we do can potentially shave years and millions of dollars from drug discovery projects, and create a greater likelihood of success. That’s what we hope to accomplish with COVID-19. We know we must work now to build our armamentarium for future waves of infection.

We hope physics-based computational design and high-speed parallel computing will lead the way.

Worth keeping an eye on, for sure.

  • Epictetus

“It is impossible for a man to learn what he thinks he already knows.”


There is no Quantum component to the Schrodinger Inc. technology. Perhaps it is a bit of a misnomer, though their software focuses on molecular modeling.

They have functioning software currently being used by numerous clients for drug discovery and to a significantly lesser degree for materials science. It is a molecular modeling software that assists with prioritizing what to test in real-world lab tests by running computer models that indicate which possibilities seem promising. If it cotinues to work, in an up-case scenario, it could actually meaningfully accelerate drug discovery timelines and reduce cost by reducing how many scenarios need to be lab-tested and prioritizing that lab work.