Full Press Release Details
Transforming Discovery of
Therapeutics and Materials
Cautionary Note and Disclaimer
contains certain forward-looking statements within the meaning of the U.S. Private Securities Litigation Reform Act of 1995 that involve substantial risks and uncertainties. All statements, other than statements of historical fact,
contained in this presentation, including, without limitation, statements regarding the potential advantages of our physics-based computational platform, our strategic plans to accelerate the growth of our software business, our research and
development efforts for our internal drug discovery programs and our computational platform, the initiation, timing, progress, and results of our internal drug discovery programs or the drug discovery programs of our collaborators, our plans to
discover and develop product candidates and to maximize their commercial potential by advancing such product candidates ourselves or in collaboration with others, our plans to leverage the synergies between our businesses, our expectations regarding
our ability to fund our operating expenses and capital expenditure requirements with our cash, cash equivalents, and marketable securities, our marketing capabilities and strategy, and our expectations related to the key drivers of our performance,
are forward-looking statements. The words anticipate, believe, contemplate, continue, could, estimate, expect, intend, may,
might, plan, potential, predict, project, should, target, will, would or the negative of these words or other similar expressions are
intended to identify forward-looking statements, although not all forward-looking statements contain these identifying words.
These forward-looking statements
reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Actual results may differ materially from those described in
the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control, including those risks detailed under the caption Risk Factors and elsewhere in our Securities and
Exchange Commission filings and reports, including the Quarterly Report on Form 10-Q for the quarter ended March 31, 2020, filed with the Securities and Exchange Commission on May 13, 2020, as well
as future filings and reports by us. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this presentation as a result of new information, future events, changes in expectations or
This presentation includes statistical and other industry and market data that we obtained from industry publications and research, surveys, and studies
conducted by third parties as well as our own estimates of potential market opportunities. All of the market data used in this presentation involves a number of assumptions and limitations, and you are cautioned not to give undue weight to such
data. Neither Schr dinger nor its affiliates, advisors or representatives makes any representation as to the accuracy or completeness of that data or undertakes to update such data after the date of this presentation.
We are transforming the way
therapeutics and materials are discovered
We have developed an industry-leading physics-based computational platform that enables discovery of high-quality
molecules for drug development and materials applications faster than traditional methods, at a lower cost, and with a higher likelihood of success
Software Developers (~200 employees)
Software (~130 employees)
Industry-leading Software Solutions
~1,300 customers worldwide(1)
Drug Discovery (~70 employees)
Collaborative Programs(2) (>25)
Wholly-owned Pipeline (5)
(1) Active customers as of Dec 31, 2019. We define active customers
as the number of customers who had an ACV of at least $1,000 in a given fiscal year. (2) Based on publicly available information or information disclosed to us; excludes programs from any undisclosed collaborations
Demonstrated track record of revenue growth with large market opportunity
Continued momentum in 1Q 2020 with record total revenue of $26.2M and 26% growth
Large market opportunity to improve traditional drug discovery and materials design
Industry-leading computational platform with significant opportunity for future growth in software and drug discovery businesses
Strong leadership team global presence
Chief Financial Officer
Karen Akinsanya, PhD
EVP, Chief Biomedical Scientist, Head of Discovery R&D
Chief Information Officer
Chief Business Officer
Chief Technology Officer
~400 Employees (~200 with PhDs)*
* As of Dec 31, 2019 5
Financing History: $425M capital raised since formation in 1990
Diversified investor base: Healthcare, HC Technology, Biotech, Tech
Designing drugs is extremely hard!
lengthy, capital-intensive, and prone to high failure rates
Need to identify a molecule that balances a large number of anti-correlated properties:
Potency Selectivity Solubility
Clearance / half-life
Drug-drug interactions Synthesizability
Potency Selectivity Solubility Bioavailability Clearance Permeability
Drug-drug Interactions
Mol 1 Mol 998 Mol 999 Mol 3045 Mol 3046
1st molecule in a program far from a drug candidate
Can get tantalizingly close in the middle of a program But often optimizing one property de-optimizes others And on and on for years and thousands of molecules
~66% of programs never succeed in delivering an IND(1)
(1) Based on average, industry-wide
The drug discovery problem
If we could calculate
all the properties with perfect accuracy, designing drugs would be significantly faster and cheaper, and would produce higher-quality molecules
synthesizable molecules
Select THE best molecule
Property 1 Property 2 Property 3 Property 4 Property 5 Property 6
Property 7 Property 8 Property 9
Potential solutions to the drug discovery problem
The field has been trying for decades to calculate properties of molecules using computers two major approaches:
Knowledge-based machine learning (often referred to as AI)
If AI can beat humans
at chess and Go, recognize faces in photos, autonomously drive cars, can it be used to design drugs?
Rigorous, first principles physics-based modeling
Requires major advances in underlying science of molecular interactions and major advances in compute power
Why is drug discovery extremely hard?
complexity of the properties, very difficult to predict properties prospectively
Combining accuracy of physics with speed of machine learning
Train machine learning model
molecules using ML model
Demonstrated benefits of Schr dinger platform
Reduces time and cost and increases quality as compared to traditional drug design
Traditional Drug Design
To identify a drug development candidate:
Manual molecule design
~5,000 molecules synthesized and tested over ~4 6
Drug development candidate with
Hit Discovery Hit-to-Lead Lead Optimization
Hit-to- Lead Hit Discovery Lead Optimization
Drug development candidate with
Optimal property profile
To identify a drug development candidate:
Billions of molecules tested in computational
~1,000 molecules synthesized and tested over ~2 3 years
Schr dinger software business
with over $1,000 ACV in 2019, including biopharmaceutical companies, materials science companies, academic institutions, and government laboratories
16% CAGR since 2013, 18% year-over-year growth for both 2018 and 2019
96% or higher retention of customers with ACV over $100,000 for 2019 and previous 6 fiscal
Top 20 pharmaceutical companies* used our software in 2019 and have been customers for an average of 15 years
Total Annual Contract Value (ACV)
Customers with ACV >$100K