There will be an inflection point when it makes total sense for a large pharma to allocate $10 to $20 million per annum on computational drug discovery. There are only 10 companies with usage levels exceeding $1 million per year. If pharmaceutical companies that are still primarily organised around iterative laboratory programs transition towards computational drug discovery, usage levels will grow by 10-20x. Again, all of the top 20 pharmaceutical companies use Schrödinger’s computational platform.
#Schrodinger combine postview software#
Schrödinger’s software revenue is currently running at an annualised run rate of $100 million. Schrödinger’s platform is a no-brainer and must-have, especially because the failure rate in drug discovery is high. Obviously, at a fraction of the time and a fraction of the cost. reduced biology risk), the industry is noticing that Schrödinger’s platform is increasing the probability of success for a drug to reach ‘development candidate’ stage from 1/8 to above 8/10.
When a pre-selected molecule is run through Schrödinger’s platform (i.e. Today, Schrödinger’s computational platform is used for 2 main reasons: 1) to explore vast amounts of chemical space on the computer (as opposed to synthesizing in a lab) to seek ‘hit’ or superior molecules and 2) to optimize or design-in drug properties required. It is a huge mistake however to ignore that learnings, discoveries, knowledge, and intellectual property are cumulatively being added to this first-principles physics-based computational platform and the speed at which Schrödinger is covering ground is accelerating. Market participants already know that all of the top 20 pharmaceutical companies use Schrödinger’s computational drug discovery platform. Schrödinger is also collaborating with other companies like ThermoFischer, Bayer, AstraZeneca and Sanofi. In exchange for exclusive output of any discoveries, Takeda will pay an undisclosed 3-digit $xxx million per program, as well as royalties on future sales. For example, Schrödinger has a partnership with Takeda Pharmaceutical where Takeda will combine its knowledge of structural biology with Schrödinger’s computational platform. Secondly, people are not accurately taking into account that large pharmaceutical companies are contributing knowledge and intellectual property onto Schrödinger’s computational platform. That gap between the output from traditional laboratories and the speed at which Schrödinger is able to cover ground is widening at an accelerating pace. To test 1,000 molecules costs c.$5 million and consumes considerable time. Not only is this snail pace, it is also based on trial-and-error. Now, compare this to a typical drug discovery program which synthesizes and tests only 1,000 molecules per year.
Obviously, compute power is increasing and accelerating each year, which means that at some point in the future, Schrödinger will be able to explore 237 billion different compounds computationally per day. Schrödinger’s platform is able to explore 237 billion different compounds computationally every six months. What does this mean and why is this the right way to think about it?Īs you may know, all the chemicals in the universe are thought to be around 1060. The investment case with Schrödinger is all about the speed at which the company is covering ground in the drug discovery space. To look around the corner in Schrödinger, investors need to find the most important metric. "Schrödinger is a fascinating company but market participants are truly struggling to find a way to think about the business model.
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